diff --git a/module-1/Resolving-Git-Conflicts/pablo.md b/module-1/Resolving-Git-Conflicts/pablo.md
new file mode 100644
index 00000000..e69de29b
diff --git a/module-3/Data-Cleaning-Challenge/.ipynb_checkpoints/Data-cleaning-Challenge-checkpoint.ipynb b/module-3/Data-Cleaning-Challenge/.ipynb_checkpoints/Data-cleaning-Challenge-checkpoint.ipynb
new file mode 100644
index 00000000..d43e27d6
--- /dev/null
+++ b/module-3/Data-Cleaning-Challenge/.ipynb_checkpoints/Data-cleaning-Challenge-checkpoint.ipynb
@@ -0,0 +1,1700 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = pd.read_csv(\"iris-data.csv\", index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_width_cm</th>\n",
+       "      <th>petal_length_cm</th>\n",
+       "      <th>petal_width_cm</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sepal_length_cm</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6.7</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.3</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.2</th>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.9</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
+       "sepal_length_cm                                                    \n",
+       "6.7                         3.0              5.2             2.3   \n",
+       "6.3                         2.5              5.0             2.3   \n",
+       "6.5                         3.0              5.2             2.0   \n",
+       "6.2                         3.4              5.4             2.3   \n",
+       "5.9                         3.0              5.1             1.8   \n",
+       "\n",
+       "                          class  \n",
+       "sepal_length_cm                  \n",
+       "6.7              Iris-virginica  \n",
+       "6.3              Iris-virginica  \n",
+       "6.5              Iris-virginica  \n",
+       "6.2              Iris-virginica  \n",
+       "5.9              Iris-virginica  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = iris.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length_cm</th>\n",
+       "      <th>sepal_width_cm</th>\n",
+       "      <th>petal_length_cm</th>\n",
+       "      <th>petal_width_cm</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
+       "145              6.7             3.0              5.2             2.3   \n",
+       "146              6.3             2.5              5.0             2.3   \n",
+       "147              6.5             3.0              5.2             2.0   \n",
+       "148              6.2             3.4              5.4             2.3   \n",
+       "149              5.9             3.0              5.1             1.8   \n",
+       "\n",
+       "              class  \n",
+       "145  Iris-virginica  \n",
+       "146  Iris-virginica  \n",
+       "147  Iris-virginica  \n",
+       "148  Iris-virginica  \n",
+       "149  Iris-virginica  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(iris)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal_length_cm    0\n",
+       "sepal_width_cm     0\n",
+       "petal_length_cm    0\n",
+       "petal_width_cm     5\n",
+       "class              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal_length_cm    float64\n",
+       "sepal_width_cm     float64\n",
+       "petal_length_cm    float64\n",
+       "petal_width_cm     float64\n",
+       "class               object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:title={'center':'sepal_length_cm'}>,\n",
+       "        <AxesSubplot:title={'center':'sepal_width_cm'}>],\n",
+       "       [<AxesSubplot:title={'center':'petal_length_cm'}>,\n",
+       "        <AxesSubplot:title={'center':'petal_width_cm'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris.hist(bins=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris.plot.box()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length_cm</th>\n",
+       "      <th>sepal_width_cm</th>\n",
+       "      <th>petal_length_cm</th>\n",
+       "      <th>petal_width_cm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>145.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.644627</td>\n",
+       "      <td>3.054667</td>\n",
+       "      <td>3.758667</td>\n",
+       "      <td>1.236552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.312781</td>\n",
+       "      <td>0.433123</td>\n",
+       "      <td>1.764420</td>\n",
+       "      <td>0.755058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.055000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.700000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.350000</td>\n",
+       "      <td>1.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>3.300000</td>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>6.900000</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm\n",
+       "count       150.000000      150.000000       150.000000      145.000000\n",
+       "mean          5.644627        3.054667         3.758667        1.236552\n",
+       "std           1.312781        0.433123         1.764420        0.755058\n",
+       "min           0.055000        2.000000         1.000000        0.100000\n",
+       "25%           5.100000        2.800000         1.600000        0.400000\n",
+       "50%           5.700000        3.000000         4.350000        1.300000\n",
+       "75%           6.400000        3.300000         5.100000        1.800000\n",
+       "max           7.900000        4.400000         6.900000        2.500000"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "class\n",
+       "Iris-setosa        0.250000\n",
+       "Iris-setossa       0.300000\n",
+       "Iris-versicolor    1.335556\n",
+       "Iris-virginica     2.034000\n",
+       "versicolor         1.240000\n",
+       "Name: petal_width_cm, dtype: float64"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris['petal_width_cm'].groupby(iris['class']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      0.2\n",
+       "1      0.2\n",
+       "2      0.2\n",
+       "3      0.2\n",
+       "4      0.2\n",
+       "      ... \n",
+       "145    2.3\n",
+       "146    2.3\n",
+       "147    2.0\n",
+       "148    2.3\n",
+       "149    1.8\n",
+       "Name: petal_width_cm, Length: 150, dtype: float64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris['petal_width_cm'].fillna(iris['petal_width_cm'].groupby(iris['class']).mean())\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iris-setosa\n",
+      "Iris-setossa\n",
+      "Iris-versicolor\n",
+      "versicolor\n",
+      "Iris-virginica\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = []\n",
+    "for i in iris['class'].unique():\n",
+    "    print(i)\n",
+    "    temporary_df = iris[iris['class'] == i]\n",
+    "    my_list.append(temporary_df.fillna(temporary_df['petal_width_cm'].mean()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris2 = pd.concat(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length_cm</th>\n",
+       "      <th>sepal_width_cm</th>\n",
+       "      <th>petal_length_cm</th>\n",
+       "      <th>petal_width_cm</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
+       "0                5.1             3.5              1.4             0.2   \n",
+       "1                4.9             3.0              1.4             0.2   \n",
+       "2                4.7             3.2              1.3             0.2   \n",
+       "3                4.6             3.1              1.5             0.2   \n",
+       "4                5.0             3.6              1.4             0.2   \n",
+       "..               ...             ...              ...             ...   \n",
+       "145              6.7             3.0              5.2             2.3   \n",
+       "146              6.3             2.5              5.0             2.3   \n",
+       "147              6.5             3.0              5.2             2.0   \n",
+       "148              6.2             3.4              5.4             2.3   \n",
+       "149              5.9             3.0              5.1             1.8   \n",
+       "\n",
+       "              class  \n",
+       "0       Iris-setosa  \n",
+       "1       Iris-setosa  \n",
+       "2       Iris-setosa  \n",
+       "3       Iris-setosa  \n",
+       "4       Iris-setosa  \n",
+       "..              ...  \n",
+       "145  Iris-virginica  \n",
+       "146  Iris-virginica  \n",
+       "147  Iris-virginica  \n",
+       "148  Iris-virginica  \n",
+       "149  Iris-virginica  \n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = iris.interpolate(inplace=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal_length_cm    0\n",
+       "sepal_width_cm     0\n",
+       "petal_length_cm    0\n",
+       "petal_width_cm     0\n",
+       "class              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal_length_cm    0\n",
+       "sepal_width_cm     0\n",
+       "petal_length_cm    0\n",
+       "petal_width_cm     0\n",
+       "class              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris2.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:title={'center':'sepal_length_cm'}>,\n",
+       "        <AxesSubplot:title={'center':'sepal_width_cm'}>],\n",
+       "       [<AxesSubplot:title={'center':'petal_length_cm'}>,\n",
+       "        <AxesSubplot:title={'center':'petal_width_cm'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris2.hist(bins=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/pablogtorres/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/Users/pablogtorres/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/Users/pablogtorres/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/Users/pablogtorres/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x7ff349a4a190>"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 823.25x720 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib as plt\n",
+    "\n",
+    "sns.pairplot(iris, hue = 'class')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's get to the machine learning part!!\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(iris.drop(['class'], axis=1), iris['class'],random_state = 0, test_size = 0.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length_cm</th>\n",
+       "      <th>sepal_width_cm</th>\n",
+       "      <th>petal_length_cm</th>\n",
+       "      <th>petal_width_cm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>5.80</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>2.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>5.50</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>7.30</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5.00</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.266667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>6.30</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>5.00</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>6.70</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>6.80</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>1.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>6.10</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>6.10</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>1.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>6.40</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>6.10</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>1.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>6.50</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>6.10</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>1.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>4.90</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>5.50</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>1.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>4.80</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>5.40</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>5.60</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>5.60</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>4.80</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4.40</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.233333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>6.20</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4.60</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>5.10</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>6.20</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>1.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>5.00</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>5.00</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>6.40</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>5.40</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>5.20</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>6.10</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>6.40</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>2.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>5.20</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>5.70</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.600000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm\n",
+       "114             5.80             2.8              5.1        2.400000\n",
+       "62              6.00             2.2              4.0        1.000000\n",
+       "33              5.50             4.2              1.4        0.200000\n",
+       "107             7.30             2.9              6.3        1.800000\n",
+       "7               5.00             3.4              1.5        0.266667\n",
+       "100             6.30             3.3              6.0        2.500000\n",
+       "40              5.00             3.5              1.3        0.300000\n",
+       "86              6.70             3.1              4.7        1.500000\n",
+       "76              6.80             2.8              4.8        1.400000\n",
+       "71              6.10             2.8              4.0        1.300000\n",
+       "134             6.10             2.6              5.6        1.400000\n",
+       "51              6.40             3.2              4.5        1.500000\n",
+       "73              6.10             2.8              4.7        1.200000\n",
+       "54              6.50             2.8              4.6        1.500000\n",
+       "63              6.10             2.9              4.7        1.400000\n",
+       "37              4.90             3.1              1.5        0.100000\n",
+       "78              0.06             2.9              4.5        1.500000\n",
+       "90              5.50             2.6              4.4        1.200000\n",
+       "45              4.80             3.0              1.4        0.300000\n",
+       "16              5.40             3.9              1.3        0.400000\n",
+       "121             5.60             2.8              4.9        2.000000\n",
+       "66              5.60             3.0              4.5        1.500000\n",
+       "24              4.80             3.4              1.9        0.200000\n",
+       "8               4.40             2.9              1.4        0.233333\n",
+       "126             6.20             2.8              4.8        1.800000\n",
+       "22              4.60             3.6              1.0        0.200000\n",
+       "44              5.10             3.8              1.9        0.400000\n",
+       "97              6.20             2.9              4.3        1.300000\n",
+       "93              5.00             2.3              3.3        1.000000\n",
+       "26              5.00             3.4              1.6        0.400000\n",
+       "137             6.40             3.1              5.5        1.800000\n",
+       "84              5.40             3.0              4.5        1.500000\n",
+       "27              5.20             3.5              1.5        0.200000\n",
+       "127             6.10             3.0              4.9        1.800000\n",
+       "132             6.40             2.8              5.6        2.200000\n",
+       "59              5.20             2.7              3.9        1.400000\n",
+       "18              5.70             3.8              1.7        0.300000\n",
+       "83              6.00             2.8              5.1        1.600000"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test.shape\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knn = KNeighborsClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predict = list(knn.predict(X_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9464285714285714"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.score(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       0.92      1.00      0.96        12\n",
+      "   Iris-setossa       0.00      0.00      0.00         1\n",
+      "Iris-versicolor       0.93      0.93      0.93        15\n",
+      " Iris-virginica       0.90      1.00      0.95         9\n",
+      "     versicolor       0.00      0.00      0.00         1\n",
+      "\n",
+      "       accuracy                           0.92        38\n",
+      "      macro avg       0.55      0.59      0.57        38\n",
+      "   weighted avg       0.87      0.92      0.90        38\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/pablogtorres/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report,confusion_matrix\n",
+    "print(classification_report(y_test,y_predict))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test = list(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error = []\n",
+    "for i in range(0,len(y_test)):\n",
+    "    error.append(y_test[i] == y_predict[i])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error = "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "38"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy = errorlen(error)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica']"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setossa',\n",
+       " 'Iris-versicolor']"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remodel = iris.drop(['class'], axis =1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predict2 = knn.predict(remodel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'versicolor', 'versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'versicolor', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error2 = []\n",
+    "for i in range(0,len(y_predict2)):\n",
+    "    error2.append(iris['class'][i] == y_predict2[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mistakes = error2.count(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy_knn = (1 - (mistakes / len(error2)))*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94.0"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_knn"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module-3/Data-Cleaning-Challenge/.ipynb_checkpoints/README-checkpoint.md b/module-3/Data-Cleaning-Challenge/.ipynb_checkpoints/README-checkpoint.md
new file mode 100644
index 00000000..d7e90149
--- /dev/null
+++ b/module-3/Data-Cleaning-Challenge/.ipynb_checkpoints/README-checkpoint.md
@@ -0,0 +1,27 @@
+![Ironhack logo](https://i.imgur.com/1QgrNNw.png)
+
+# Lab | Data Cleaning Bonus Challenge
+The objectives of this lab are:
+* Know the Iris Dataset (you will use it tomorrow)
+* Review cleaning concepts
+
+## Introduction
+As you know, data comes with a lot of errors. In this lab we propose you a challenge. We have purposely added some errors to the famous [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set). Let's take a look on the data and fix some possible errors as:
+* Data is encoded properly.
+
+* Data falls within the expected range, and use domain knowledge whenever possible to define that expected range.
+
+* Deal with missing data in one way or another: replace it if you can or drop it.
+
+
+## Some advices:
+
+* Never tidy your data manually because that is not easily reproducible
+
+* Use code as a record of how you tidied your data
+
+* Plot everything you can about the data at this stage of the analysis so you can visually confirm everything looks correct
+
+## Deliverables
+* A notebook with the code you used to clean the data.
+* The final data in a csv.
diff --git a/module-3/Data-Cleaning-Challenge/Data-cleaning-Challenge.ipynb b/module-3/Data-Cleaning-Challenge/Data-cleaning-Challenge.ipynb
new file mode 100644
index 00000000..d43e27d6
--- /dev/null
+++ b/module-3/Data-Cleaning-Challenge/Data-cleaning-Challenge.ipynb
@@ -0,0 +1,1700 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = pd.read_csv(\"iris-data.csv\", index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_width_cm</th>\n",
+       "      <th>petal_length_cm</th>\n",
+       "      <th>petal_width_cm</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sepal_length_cm</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6.7</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.3</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.2</th>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.9</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
+       "sepal_length_cm                                                    \n",
+       "6.7                         3.0              5.2             2.3   \n",
+       "6.3                         2.5              5.0             2.3   \n",
+       "6.5                         3.0              5.2             2.0   \n",
+       "6.2                         3.4              5.4             2.3   \n",
+       "5.9                         3.0              5.1             1.8   \n",
+       "\n",
+       "                          class  \n",
+       "sepal_length_cm                  \n",
+       "6.7              Iris-virginica  \n",
+       "6.3              Iris-virginica  \n",
+       "6.5              Iris-virginica  \n",
+       "6.2              Iris-virginica  \n",
+       "5.9              Iris-virginica  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = iris.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length_cm</th>\n",
+       "      <th>sepal_width_cm</th>\n",
+       "      <th>petal_length_cm</th>\n",
+       "      <th>petal_width_cm</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
+       "145              6.7             3.0              5.2             2.3   \n",
+       "146              6.3             2.5              5.0             2.3   \n",
+       "147              6.5             3.0              5.2             2.0   \n",
+       "148              6.2             3.4              5.4             2.3   \n",
+       "149              5.9             3.0              5.1             1.8   \n",
+       "\n",
+       "              class  \n",
+       "145  Iris-virginica  \n",
+       "146  Iris-virginica  \n",
+       "147  Iris-virginica  \n",
+       "148  Iris-virginica  \n",
+       "149  Iris-virginica  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(iris)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal_length_cm    0\n",
+       "sepal_width_cm     0\n",
+       "petal_length_cm    0\n",
+       "petal_width_cm     5\n",
+       "class              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal_length_cm    float64\n",
+       "sepal_width_cm     float64\n",
+       "petal_length_cm    float64\n",
+       "petal_width_cm     float64\n",
+       "class               object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:title={'center':'sepal_length_cm'}>,\n",
+       "        <AxesSubplot:title={'center':'sepal_width_cm'}>],\n",
+       "       [<AxesSubplot:title={'center':'petal_length_cm'}>,\n",
+       "        <AxesSubplot:title={'center':'petal_width_cm'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris.hist(bins=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris.plot.box()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length_cm</th>\n",
+       "      <th>sepal_width_cm</th>\n",
+       "      <th>petal_length_cm</th>\n",
+       "      <th>petal_width_cm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>145.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.644627</td>\n",
+       "      <td>3.054667</td>\n",
+       "      <td>3.758667</td>\n",
+       "      <td>1.236552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.312781</td>\n",
+       "      <td>0.433123</td>\n",
+       "      <td>1.764420</td>\n",
+       "      <td>0.755058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.055000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.700000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.350000</td>\n",
+       "      <td>1.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>3.300000</td>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>6.900000</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm\n",
+       "count       150.000000      150.000000       150.000000      145.000000\n",
+       "mean          5.644627        3.054667         3.758667        1.236552\n",
+       "std           1.312781        0.433123         1.764420        0.755058\n",
+       "min           0.055000        2.000000         1.000000        0.100000\n",
+       "25%           5.100000        2.800000         1.600000        0.400000\n",
+       "50%           5.700000        3.000000         4.350000        1.300000\n",
+       "75%           6.400000        3.300000         5.100000        1.800000\n",
+       "max           7.900000        4.400000         6.900000        2.500000"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "class\n",
+       "Iris-setosa        0.250000\n",
+       "Iris-setossa       0.300000\n",
+       "Iris-versicolor    1.335556\n",
+       "Iris-virginica     2.034000\n",
+       "versicolor         1.240000\n",
+       "Name: petal_width_cm, dtype: float64"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris['petal_width_cm'].groupby(iris['class']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      0.2\n",
+       "1      0.2\n",
+       "2      0.2\n",
+       "3      0.2\n",
+       "4      0.2\n",
+       "      ... \n",
+       "145    2.3\n",
+       "146    2.3\n",
+       "147    2.0\n",
+       "148    2.3\n",
+       "149    1.8\n",
+       "Name: petal_width_cm, Length: 150, dtype: float64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris['petal_width_cm'].fillna(iris['petal_width_cm'].groupby(iris['class']).mean())\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iris-setosa\n",
+      "Iris-setossa\n",
+      "Iris-versicolor\n",
+      "versicolor\n",
+      "Iris-virginica\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = []\n",
+    "for i in iris['class'].unique():\n",
+    "    print(i)\n",
+    "    temporary_df = iris[iris['class'] == i]\n",
+    "    my_list.append(temporary_df.fillna(temporary_df['petal_width_cm'].mean()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris2 = pd.concat(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length_cm</th>\n",
+       "      <th>sepal_width_cm</th>\n",
+       "      <th>petal_length_cm</th>\n",
+       "      <th>petal_width_cm</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
+       "0                5.1             3.5              1.4             0.2   \n",
+       "1                4.9             3.0              1.4             0.2   \n",
+       "2                4.7             3.2              1.3             0.2   \n",
+       "3                4.6             3.1              1.5             0.2   \n",
+       "4                5.0             3.6              1.4             0.2   \n",
+       "..               ...             ...              ...             ...   \n",
+       "145              6.7             3.0              5.2             2.3   \n",
+       "146              6.3             2.5              5.0             2.3   \n",
+       "147              6.5             3.0              5.2             2.0   \n",
+       "148              6.2             3.4              5.4             2.3   \n",
+       "149              5.9             3.0              5.1             1.8   \n",
+       "\n",
+       "              class  \n",
+       "0       Iris-setosa  \n",
+       "1       Iris-setosa  \n",
+       "2       Iris-setosa  \n",
+       "3       Iris-setosa  \n",
+       "4       Iris-setosa  \n",
+       "..              ...  \n",
+       "145  Iris-virginica  \n",
+       "146  Iris-virginica  \n",
+       "147  Iris-virginica  \n",
+       "148  Iris-virginica  \n",
+       "149  Iris-virginica  \n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = iris.interpolate(inplace=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal_length_cm    0\n",
+       "sepal_width_cm     0\n",
+       "petal_length_cm    0\n",
+       "petal_width_cm     0\n",
+       "class              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal_length_cm    0\n",
+       "sepal_width_cm     0\n",
+       "petal_length_cm    0\n",
+       "petal_width_cm     0\n",
+       "class              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris2.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:title={'center':'sepal_length_cm'}>,\n",
+       "        <AxesSubplot:title={'center':'sepal_width_cm'}>],\n",
+       "       [<AxesSubplot:title={'center':'petal_length_cm'}>,\n",
+       "        <AxesSubplot:title={'center':'petal_width_cm'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris2.hist(bins=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/pablogtorres/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/Users/pablogtorres/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/Users/pablogtorres/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/Users/pablogtorres/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x7ff349a4a190>"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 823.25x720 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib as plt\n",
+    "\n",
+    "sns.pairplot(iris, hue = 'class')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's get to the machine learning part!!\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(iris.drop(['class'], axis=1), iris['class'],random_state = 0, test_size = 0.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length_cm</th>\n",
+       "      <th>sepal_width_cm</th>\n",
+       "      <th>petal_length_cm</th>\n",
+       "      <th>petal_width_cm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>5.80</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>2.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>5.50</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>7.30</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5.00</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.266667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>6.30</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>5.00</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>6.70</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>6.80</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>1.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>6.10</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>6.10</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>1.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>6.40</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>6.10</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>1.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>6.50</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>6.10</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>1.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>4.90</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>5.50</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>1.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>4.80</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>5.40</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>5.60</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>5.60</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>4.80</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4.40</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.233333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>6.20</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4.60</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>5.10</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>6.20</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>1.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>5.00</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>5.00</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>6.40</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>5.40</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>5.20</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>6.10</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>6.40</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>2.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>5.20</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>5.70</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.600000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm\n",
+       "114             5.80             2.8              5.1        2.400000\n",
+       "62              6.00             2.2              4.0        1.000000\n",
+       "33              5.50             4.2              1.4        0.200000\n",
+       "107             7.30             2.9              6.3        1.800000\n",
+       "7               5.00             3.4              1.5        0.266667\n",
+       "100             6.30             3.3              6.0        2.500000\n",
+       "40              5.00             3.5              1.3        0.300000\n",
+       "86              6.70             3.1              4.7        1.500000\n",
+       "76              6.80             2.8              4.8        1.400000\n",
+       "71              6.10             2.8              4.0        1.300000\n",
+       "134             6.10             2.6              5.6        1.400000\n",
+       "51              6.40             3.2              4.5        1.500000\n",
+       "73              6.10             2.8              4.7        1.200000\n",
+       "54              6.50             2.8              4.6        1.500000\n",
+       "63              6.10             2.9              4.7        1.400000\n",
+       "37              4.90             3.1              1.5        0.100000\n",
+       "78              0.06             2.9              4.5        1.500000\n",
+       "90              5.50             2.6              4.4        1.200000\n",
+       "45              4.80             3.0              1.4        0.300000\n",
+       "16              5.40             3.9              1.3        0.400000\n",
+       "121             5.60             2.8              4.9        2.000000\n",
+       "66              5.60             3.0              4.5        1.500000\n",
+       "24              4.80             3.4              1.9        0.200000\n",
+       "8               4.40             2.9              1.4        0.233333\n",
+       "126             6.20             2.8              4.8        1.800000\n",
+       "22              4.60             3.6              1.0        0.200000\n",
+       "44              5.10             3.8              1.9        0.400000\n",
+       "97              6.20             2.9              4.3        1.300000\n",
+       "93              5.00             2.3              3.3        1.000000\n",
+       "26              5.00             3.4              1.6        0.400000\n",
+       "137             6.40             3.1              5.5        1.800000\n",
+       "84              5.40             3.0              4.5        1.500000\n",
+       "27              5.20             3.5              1.5        0.200000\n",
+       "127             6.10             3.0              4.9        1.800000\n",
+       "132             6.40             2.8              5.6        2.200000\n",
+       "59              5.20             2.7              3.9        1.400000\n",
+       "18              5.70             3.8              1.7        0.300000\n",
+       "83              6.00             2.8              5.1        1.600000"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test.shape\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knn = KNeighborsClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predict = list(knn.predict(X_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9464285714285714"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.score(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       0.92      1.00      0.96        12\n",
+      "   Iris-setossa       0.00      0.00      0.00         1\n",
+      "Iris-versicolor       0.93      0.93      0.93        15\n",
+      " Iris-virginica       0.90      1.00      0.95         9\n",
+      "     versicolor       0.00      0.00      0.00         1\n",
+      "\n",
+      "       accuracy                           0.92        38\n",
+      "      macro avg       0.55      0.59      0.57        38\n",
+      "   weighted avg       0.87      0.92      0.90        38\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/pablogtorres/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report,confusion_matrix\n",
+    "print(classification_report(y_test,y_predict))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test = list(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error = []\n",
+    "for i in range(0,len(y_test)):\n",
+    "    error.append(y_test[i] == y_predict[i])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error = "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "38"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy = errorlen(error)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica']"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-setosa',\n",
+       " 'versicolor',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setosa',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-virginica',\n",
+       " 'Iris-versicolor',\n",
+       " 'Iris-setossa',\n",
+       " 'Iris-versicolor']"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remodel = iris.drop(['class'], axis =1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predict2 = knn.predict(remodel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'versicolor', 'versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'versicolor', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error2 = []\n",
+    "for i in range(0,len(y_predict2)):\n",
+    "    error2.append(iris['class'][i] == y_predict2[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mistakes = error2.count(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy_knn = (1 - (mistakes / len(error2)))*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94.0"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_knn"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}