From ca859ddc14a3bb03aa1d848a9f85e968869ba00d Mon Sep 17 00:00:00 2001
From: Burak Yildiz <145865827+burakkyildiz@users.noreply.github.com>
Date: Sun, 19 Oct 2025 16:49:20 +0300
Subject: [PATCH] fix variable name mismatch in remove_outliers_from_column

Replaced undefined variable 'col' with 'target_col' to prevent NameError.
---
 .../21-XGBoostRegressor-checkpoint.ipynb      | 3023 +++++++++++++++++
 21-XGBoostRegressor.ipynb                     |   10 +-
 2 files changed, 3028 insertions(+), 5 deletions(-)
 create mode 100644 .ipynb_checkpoints/21-XGBoostRegressor-checkpoint.ipynb

diff --git a/.ipynb_checkpoints/21-XGBoostRegressor-checkpoint.ipynb b/.ipynb_checkpoints/21-XGBoostRegressor-checkpoint.ipynb
new file mode 100644
index 0000000..52163c9
--- /dev/null
+++ b/.ipynb_checkpoints/21-XGBoostRegressor-checkpoint.ipynb
@@ -0,0 +1,3023 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0f15ab0a-d8c0-4ab7-a788-05ae26f8a949",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2ccf395c-7bb0-420a-8ada-d89fbcb81c03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#https://www.kaggle.com/datasets/camnugent/california-housing-prices/data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9f487aff-ee9a-4d8a-b842-6ab8aa27d0bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"21-housing.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6e520191-b1ac-468b-8b5f-7694a34a729b",
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>452600.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>7099.0</td>\n",
+       "      <td>1106.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>1138.0</td>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>358500.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1467.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>352100.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1274.0</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>341300.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1627.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>342200.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "0    -122.23     37.88                41.0        880.0           129.0   \n",
+       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
+       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
+       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
+       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
+       "\n",
+       "   population  households  median_income  median_house_value ocean_proximity  \n",
+       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
+       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
+       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
+       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
+       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "39b0bbed-d164-4169-9ac9-decec66402ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 20640 entries, 0 to 20639\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           20640 non-null  float64\n",
+      " 1   latitude            20640 non-null  float64\n",
+      " 2   housing_median_age  20640 non-null  float64\n",
+      " 3   total_rooms         20640 non-null  float64\n",
+      " 4   total_bedrooms      20433 non-null  float64\n",
+      " 5   population          20640 non-null  float64\n",
+      " 6   households          20640 non-null  float64\n",
+      " 7   median_income       20640 non-null  float64\n",
+      " 8   median_house_value  20640 non-null  float64\n",
+      " 9   ocean_proximity     20640 non-null  object \n",
+      "dtypes: float64(9), object(1)\n",
+      "memory usage: 1.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3989f716-cd42-42dd-aeac-3957009f7390",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "longitude               0\n",
+       "latitude                0\n",
+       "housing_median_age      0\n",
+       "total_rooms             0\n",
+       "total_bedrooms        207\n",
+       "population              0\n",
+       "households              0\n",
+       "median_income           0\n",
+       "median_house_value      0\n",
+       "ocean_proximity         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b8365fc9-2094-4c4a-9bce-7dd7a888a579",
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20433.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-119.569704</td>\n",
+       "      <td>35.631861</td>\n",
+       "      <td>28.639486</td>\n",
+       "      <td>2635.763081</td>\n",
+       "      <td>537.870553</td>\n",
+       "      <td>1425.476744</td>\n",
+       "      <td>499.539680</td>\n",
+       "      <td>3.870671</td>\n",
+       "      <td>206855.816909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.003532</td>\n",
+       "      <td>2.135952</td>\n",
+       "      <td>12.585558</td>\n",
+       "      <td>2181.615252</td>\n",
+       "      <td>421.385070</td>\n",
+       "      <td>1132.462122</td>\n",
+       "      <td>382.329753</td>\n",
+       "      <td>1.899822</td>\n",
+       "      <td>115395.615874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-124.350000</td>\n",
+       "      <td>32.540000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.499900</td>\n",
+       "      <td>14999.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-121.800000</td>\n",
+       "      <td>33.930000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>1447.750000</td>\n",
+       "      <td>296.000000</td>\n",
+       "      <td>787.000000</td>\n",
+       "      <td>280.000000</td>\n",
+       "      <td>2.563400</td>\n",
+       "      <td>119600.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>-118.490000</td>\n",
+       "      <td>34.260000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>2127.000000</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>1166.000000</td>\n",
+       "      <td>409.000000</td>\n",
+       "      <td>3.534800</td>\n",
+       "      <td>179700.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>-118.010000</td>\n",
+       "      <td>37.710000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>3148.000000</td>\n",
+       "      <td>647.000000</td>\n",
+       "      <td>1725.000000</td>\n",
+       "      <td>605.000000</td>\n",
+       "      <td>4.743250</td>\n",
+       "      <td>264725.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>-114.310000</td>\n",
+       "      <td>41.950000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>39320.000000</td>\n",
+       "      <td>6445.000000</td>\n",
+       "      <td>35682.000000</td>\n",
+       "      <td>6082.000000</td>\n",
+       "      <td>15.000100</td>\n",
+       "      <td>500001.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
+       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
+       "std        2.003532      2.135952           12.585558   2181.615252   \n",
+       "min     -124.350000     32.540000            1.000000      2.000000   \n",
+       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
+       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
+       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
+       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
+       "\n",
+       "       total_bedrooms    population    households  median_income  \\\n",
+       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
+       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
+       "std        421.385070   1132.462122    382.329753       1.899822   \n",
+       "min          1.000000      3.000000      1.000000       0.499900   \n",
+       "25%        296.000000    787.000000    280.000000       2.563400   \n",
+       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
+       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
+       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+       "\n",
+       "       median_house_value  \n",
+       "count        20640.000000  \n",
+       "mean        206855.816909  \n",
+       "std         115395.615874  \n",
+       "min          14999.000000  \n",
+       "25%         119600.000000  \n",
+       "50%         179700.000000  \n",
+       "75%         264725.000000  \n",
+       "max         500001.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a3c17b55-d682-4c62-92b5-764fd07ae255",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ocean_proximity\n",
+       "<1H OCEAN     9136\n",
+       "INLAND        6551\n",
+       "NEAR OCEAN    2658\n",
+       "NEAR BAY      2290\n",
+       "ISLAND           5\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['ocean_proximity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "da617a6e-f426-4a58-9c8c-de25ed33b7e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
+       "       'total_bedrooms', 'population', 'households', 'median_income',\n",
+       "       'median_house_value', 'ocean_proximity'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e372e145-a887-4f62-ac53-28717736e40c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x1200 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns = ['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
+    "       'total_bedrooms', 'population', 'households', 'median_income',\n",
+    "       'median_house_value']\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows = 3, ncols = 3, figsize=(15,12))\n",
+    "fig.suptitle(\"Distributions\", fontsize = 18, fontweight = \"bold\")\n",
+    "\n",
+    "for i, col in enumerate(columns):\n",
+    "    row = i // 3\n",
+    "    col_idx = i % 3\n",
+    "    ax = axes[row, col_idx]\n",
+    "    sns.histplot(data = df, x = col, kde=True, ax=ax, bins=30)\n",
+    "    ax.set_title(col, fontsize=10, fontstyle = \"italic\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9ca68c6d-6815-4d49-93ee-a2342900b4af",
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>longitude</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.924664</td>\n",
+       "      <td>-0.108197</td>\n",
+       "      <td>0.044568</td>\n",
+       "      <td>0.069608</td>\n",
+       "      <td>0.099773</td>\n",
+       "      <td>0.055310</td>\n",
+       "      <td>-0.015176</td>\n",
+       "      <td>-0.045967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>latitude</th>\n",
+       "      <td>-0.924664</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.011173</td>\n",
+       "      <td>-0.036100</td>\n",
+       "      <td>-0.066983</td>\n",
+       "      <td>-0.108785</td>\n",
+       "      <td>-0.071035</td>\n",
+       "      <td>-0.079809</td>\n",
+       "      <td>-0.144160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <td>-0.108197</td>\n",
+       "      <td>0.011173</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.361262</td>\n",
+       "      <td>-0.320451</td>\n",
+       "      <td>-0.296244</td>\n",
+       "      <td>-0.302916</td>\n",
+       "      <td>-0.119034</td>\n",
+       "      <td>0.105623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_rooms</th>\n",
+       "      <td>0.044568</td>\n",
+       "      <td>-0.036100</td>\n",
+       "      <td>-0.361262</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.930380</td>\n",
+       "      <td>0.857126</td>\n",
+       "      <td>0.918484</td>\n",
+       "      <td>0.198050</td>\n",
+       "      <td>0.134153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <td>0.069608</td>\n",
+       "      <td>-0.066983</td>\n",
+       "      <td>-0.320451</td>\n",
+       "      <td>0.930380</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.877747</td>\n",
+       "      <td>0.979728</td>\n",
+       "      <td>-0.007723</td>\n",
+       "      <td>0.049686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>population</th>\n",
+       "      <td>0.099773</td>\n",
+       "      <td>-0.108785</td>\n",
+       "      <td>-0.296244</td>\n",
+       "      <td>0.857126</td>\n",
+       "      <td>0.877747</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.907222</td>\n",
+       "      <td>0.004834</td>\n",
+       "      <td>-0.024650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>households</th>\n",
+       "      <td>0.055310</td>\n",
+       "      <td>-0.071035</td>\n",
+       "      <td>-0.302916</td>\n",
+       "      <td>0.918484</td>\n",
+       "      <td>0.979728</td>\n",
+       "      <td>0.907222</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.013033</td>\n",
+       "      <td>0.065843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>median_income</th>\n",
+       "      <td>-0.015176</td>\n",
+       "      <td>-0.079809</td>\n",
+       "      <td>-0.119034</td>\n",
+       "      <td>0.198050</td>\n",
+       "      <td>-0.007723</td>\n",
+       "      <td>0.004834</td>\n",
+       "      <td>0.013033</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.688075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>median_house_value</th>\n",
+       "      <td>-0.045967</td>\n",
+       "      <td>-0.144160</td>\n",
+       "      <td>0.105623</td>\n",
+       "      <td>0.134153</td>\n",
+       "      <td>0.049686</td>\n",
+       "      <td>-0.024650</td>\n",
+       "      <td>0.065843</td>\n",
+       "      <td>0.688075</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
+       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
+       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
+       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
+       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
+       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
+       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
+       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
+       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
+       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
+       "\n",
+       "                    total_bedrooms  population  households  median_income  \\\n",
+       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
+       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
+       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
+       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
+       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
+       "population                0.877747    1.000000    0.907222       0.004834   \n",
+       "households                0.979728    0.907222    1.000000       0.013033   \n",
+       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
+       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
+       "\n",
+       "                    median_house_value  \n",
+       "longitude                    -0.045967  \n",
+       "latitude                     -0.144160  \n",
+       "housing_median_age            0.105623  \n",
+       "total_rooms                   0.134153  \n",
+       "total_bedrooms                0.049686  \n",
+       "population                   -0.024650  \n",
+       "households                    0.065843  \n",
+       "median_income                 0.688075  \n",
+       "median_house_value            1.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(numeric_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4283036a-4046-4964-b590-d0d1accc0f92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_outliers_iqr(df, threshold = 1.5):\n",
+    "    outlier_summary = {}\n",
+    "\n",
+    "    numeric_cols = df.select_dtypes(include=[\"float64\", \"int64\"]).columns\n",
+    "    \n",
+    "    for col in numeric_cols:\n",
+    "        Q1 = df[col].quantile(0.25)\n",
+    "        Q3 = df[col].quantile(0.75)\n",
+    "        IQR = Q3 - Q1\n",
+    "\n",
+    "        lower_bound = Q1 - threshold * IQR\n",
+    "        upper_bound = Q3 + threshold * IQR\n",
+    "\n",
+    "        outliers = df[ (df[col] < lower_bound) | (df[col] > upper_bound)]\n",
+    "        \n",
+    "        outlier_summary[col] = {\n",
+    "            \"outlier_count\" : outliers.shape[0],\n",
+    "            \"outlier_percentage\" : 100 * outliers.shape[0] / df.shape[0],\n",
+    "            \"lower_bound\" : lower_bound,\n",
+    "            \"upper_bound\" : upper_bound\n",
+    "        }\n",
+    "    return pd.DataFrame(outlier_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7bed5ce1-8cf3-417b-91da-26caab02d347",
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>outlier_count</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1287.000000</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1196.000000</td>\n",
+       "      <td>1220.000000</td>\n",
+       "      <td>681.000000</td>\n",
+       "      <td>1071.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>outlier_percentage</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.235465</td>\n",
+       "      <td>6.157946</td>\n",
+       "      <td>5.794574</td>\n",
+       "      <td>5.910853</td>\n",
+       "      <td>3.299419</td>\n",
+       "      <td>5.188953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lower_bound</th>\n",
+       "      <td>-127.485</td>\n",
+       "      <td>28.26</td>\n",
+       "      <td>-10.5</td>\n",
+       "      <td>-1102.625000</td>\n",
+       "      <td>-230.500000</td>\n",
+       "      <td>-620.000000</td>\n",
+       "      <td>-207.500000</td>\n",
+       "      <td>-0.706375</td>\n",
+       "      <td>-98087.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>upper_bound</th>\n",
+       "      <td>-112.325</td>\n",
+       "      <td>43.38</td>\n",
+       "      <td>65.5</td>\n",
+       "      <td>5698.375000</td>\n",
+       "      <td>1173.500000</td>\n",
+       "      <td>3132.000000</td>\n",
+       "      <td>1092.500000</td>\n",
+       "      <td>8.013025</td>\n",
+       "      <td>482412.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
+       "outlier_count           0.000      0.00                 0.0  1287.000000   \n",
+       "outlier_percentage      0.000      0.00                 0.0     6.235465   \n",
+       "lower_bound          -127.485     28.26               -10.5 -1102.625000   \n",
+       "upper_bound          -112.325     43.38                65.5  5698.375000   \n",
+       "\n",
+       "                    total_bedrooms   population   households  median_income  \\\n",
+       "outlier_count          1271.000000  1196.000000  1220.000000     681.000000   \n",
+       "outlier_percentage        6.157946     5.794574     5.910853       3.299419   \n",
+       "lower_bound            -230.500000  -620.000000  -207.500000      -0.706375   \n",
+       "upper_bound            1173.500000  3132.000000  1092.500000       8.013025   \n",
+       "\n",
+       "                    median_house_value  \n",
+       "outlier_count              1071.000000  \n",
+       "outlier_percentage            5.188953  \n",
+       "lower_bound              -98087.500000  \n",
+       "upper_bound              482412.500000  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "find_outliers_iqr(df, threshold = 1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "c9220e7e-2efa-4425-8eb9-6bccfb9cbd8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# i will only remove outliers in our target column which is median_house_value\n",
+    "# model tries to predict this value and outliers in target column may corrupt loss function and result in deviations\n",
+    "# of course outliers in input columns may corrupt the model as well but if we are using a decision tree based model\n",
+    "# such as gradients, forests etc it wouldn't hurt us much\n",
+    "# and we will preserve the most of the data \n",
+    "# let's create two functions to compare how would it look like if we clean all data and only output column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b9aecbd7-fe0c-4f83-9adc-531a5773cb6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def remove_outliers_from_column(df,target_col, threshold = 1.5):\n",
+    "    Q1 = df[target_col].quantile(0.25)\n",
+    "    Q3 = df[target_col].quantile(0.75)\n",
+    "    IQR = Q3 - Q1\n",
+    "\n",
+    "    lower_bound = Q1 - threshold * IQR\n",
+    "    upper_bound = Q3 + threshold * IQR\n",
+    "    return df[ (df[target_col] >= lower_bound) & (df[target_col] <= upper_bound)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "869e2b64-b5f6-41b6-8efa-0ab87d81e460",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def remove_outliers_from_all_columns(df, threshold = 1.5):\n",
+    "    df_clean = df.copy()\n",
+    "    numeric_cols = df.select_dtypes(include=[\"float64\", \"int64\"]).columns\n",
+    "    \n",
+    "    for col in numeric_cols:\n",
+    "        Q1 = df[col].quantile(0.25)\n",
+    "        Q3 = df[col].quantile(0.75)\n",
+    "        IQR = Q3 - Q1\n",
+    "\n",
+    "        lower_bound = Q1 - threshold * IQR\n",
+    "        upper_bound = Q3 + threshold * IQR\n",
+    "\n",
+    "        df_clean = df_clean[(df_clean[col] >= lower_bound) & (df_clean[col] <= upper_bound)]\n",
+    "    return df_clean.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1086bf9f-7285-4d57-b555-d538188a8ba4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "original data shape:  (20640, 10)\n",
+      "only target column cleaning shape:  (19569, 10)\n",
+      "all columns cleaning shape:  (17446, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"original data shape: \", df.shape)\n",
+    "df_target_clean = remove_outliers_from_column(df, \"median_house_value\")\n",
+    "print(\"only target column cleaning shape: \", df_target_clean.shape)\n",
+    "df_all_clean = remove_outliers_from_all_columns(df)\n",
+    "print(\"all columns cleaning shape: \", df_all_clean.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "1e2adbbd-3a59-4051-8599-c8e8b796ac63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# i am going to use only target column cleaning in this case for the reasons i mentioned\n",
+    "# if you want, you can train the model with these different dfs to compare the performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3ff7bdac-1a45-47f3-a5e4-cf7cacc20989",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "longitude               0\n",
+       "latitude                0\n",
+       "housing_median_age      0\n",
+       "total_rooms             0\n",
+       "total_bedrooms        200\n",
+       "population              0\n",
+       "households              0\n",
+       "median_income           0\n",
+       "median_house_value      0\n",
+       "ocean_proximity         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_target_clean.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "af715d98-6502-453d-9ab5-54b18120dc1c",
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19369.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-119.562786</td>\n",
+       "      <td>35.654159</td>\n",
+       "      <td>28.352752</td>\n",
+       "      <td>2619.977260</td>\n",
+       "      <td>539.893335</td>\n",
+       "      <td>1442.788952</td>\n",
+       "      <td>501.394859</td>\n",
+       "      <td>3.665568</td>\n",
+       "      <td>190852.301906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.005764</td>\n",
+       "      <td>2.151007</td>\n",
+       "      <td>12.497772</td>\n",
+       "      <td>2183.419302</td>\n",
+       "      <td>422.650225</td>\n",
+       "      <td>1145.011369</td>\n",
+       "      <td>383.396308</td>\n",
+       "      <td>1.557927</td>\n",
+       "      <td>95438.555669</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-124.350000</td>\n",
+       "      <td>32.540000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.499900</td>\n",
+       "      <td>14999.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-121.760000</td>\n",
+       "      <td>33.930000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>1438.000000</td>\n",
+       "      <td>297.000000</td>\n",
+       "      <td>797.000000</td>\n",
+       "      <td>282.000000</td>\n",
+       "      <td>2.522700</td>\n",
+       "      <td>116200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>-118.510000</td>\n",
+       "      <td>34.270000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>2110.000000</td>\n",
+       "      <td>437.000000</td>\n",
+       "      <td>1181.000000</td>\n",
+       "      <td>411.000000</td>\n",
+       "      <td>3.441200</td>\n",
+       "      <td>173200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>-117.990000</td>\n",
+       "      <td>37.730000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>3123.000000</td>\n",
+       "      <td>648.000000</td>\n",
+       "      <td>1749.000000</td>\n",
+       "      <td>606.000000</td>\n",
+       "      <td>4.572100</td>\n",
+       "      <td>246700.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>-114.310000</td>\n",
+       "      <td>41.950000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>39320.000000</td>\n",
+       "      <td>6445.000000</td>\n",
+       "      <td>35682.000000</td>\n",
+       "      <td>6082.000000</td>\n",
+       "      <td>15.000100</td>\n",
+       "      <td>482200.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+       "count  19569.000000  19569.000000        19569.000000  19569.000000   \n",
+       "mean    -119.562786     35.654159           28.352752   2619.977260   \n",
+       "std        2.005764      2.151007           12.497772   2183.419302   \n",
+       "min     -124.350000     32.540000            1.000000      2.000000   \n",
+       "25%     -121.760000     33.930000           18.000000   1438.000000   \n",
+       "50%     -118.510000     34.270000           28.000000   2110.000000   \n",
+       "75%     -117.990000     37.730000           37.000000   3123.000000   \n",
+       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
+       "\n",
+       "       total_bedrooms    population    households  median_income  \\\n",
+       "count    19369.000000  19569.000000  19569.000000   19569.000000   \n",
+       "mean       539.893335   1442.788952    501.394859       3.665568   \n",
+       "std        422.650225   1145.011369    383.396308       1.557927   \n",
+       "min          2.000000      3.000000      2.000000       0.499900   \n",
+       "25%        297.000000    797.000000    282.000000       2.522700   \n",
+       "50%        437.000000   1181.000000    411.000000       3.441200   \n",
+       "75%        648.000000   1749.000000    606.000000       4.572100   \n",
+       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+       "\n",
+       "       median_house_value  \n",
+       "count        19569.000000  \n",
+       "mean        190852.301906  \n",
+       "std          95438.555669  \n",
+       "min          14999.000000  \n",
+       "25%         116200.000000  \n",
+       "50%         173200.000000  \n",
+       "75%         246700.000000  \n",
+       "max         482200.000000  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_target_clean.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a7b5479b-3092-4633-b16b-c5fcdab171a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/n9/38m8fw8j1flb3nhw8ld2ds5m0000gn/T/ipykernel_79936/3688706348.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_target_clean[\"total_bedrooms\"] = df_target_clean[\"total_bedrooms\"].fillna(df_target_clean[\"total_bedrooms\"].median())\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_target_clean[\"total_bedrooms\"] = df_target_clean[\"total_bedrooms\"].fillna(df_target_clean[\"total_bedrooms\"].median())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f23991cb-e2f1-41ba-8abe-ced9ab5fb2eb",
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "      <td>19569.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-119.562786</td>\n",
+       "      <td>35.654159</td>\n",
+       "      <td>28.352752</td>\n",
+       "      <td>2619.977260</td>\n",
+       "      <td>538.841739</td>\n",
+       "      <td>1442.788952</td>\n",
+       "      <td>501.394859</td>\n",
+       "      <td>3.665568</td>\n",
+       "      <td>190852.301906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.005764</td>\n",
+       "      <td>2.151007</td>\n",
+       "      <td>12.497772</td>\n",
+       "      <td>2183.419302</td>\n",
+       "      <td>420.612109</td>\n",
+       "      <td>1145.011369</td>\n",
+       "      <td>383.396308</td>\n",
+       "      <td>1.557927</td>\n",
+       "      <td>95438.555669</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-124.350000</td>\n",
+       "      <td>32.540000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.499900</td>\n",
+       "      <td>14999.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-121.760000</td>\n",
+       "      <td>33.930000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>1438.000000</td>\n",
+       "      <td>299.000000</td>\n",
+       "      <td>797.000000</td>\n",
+       "      <td>282.000000</td>\n",
+       "      <td>2.522700</td>\n",
+       "      <td>116200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>-118.510000</td>\n",
+       "      <td>34.270000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>2110.000000</td>\n",
+       "      <td>437.000000</td>\n",
+       "      <td>1181.000000</td>\n",
+       "      <td>411.000000</td>\n",
+       "      <td>3.441200</td>\n",
+       "      <td>173200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>-117.990000</td>\n",
+       "      <td>37.730000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>3123.000000</td>\n",
+       "      <td>645.000000</td>\n",
+       "      <td>1749.000000</td>\n",
+       "      <td>606.000000</td>\n",
+       "      <td>4.572100</td>\n",
+       "      <td>246700.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>-114.310000</td>\n",
+       "      <td>41.950000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>39320.000000</td>\n",
+       "      <td>6445.000000</td>\n",
+       "      <td>35682.000000</td>\n",
+       "      <td>6082.000000</td>\n",
+       "      <td>15.000100</td>\n",
+       "      <td>482200.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+       "count  19569.000000  19569.000000        19569.000000  19569.000000   \n",
+       "mean    -119.562786     35.654159           28.352752   2619.977260   \n",
+       "std        2.005764      2.151007           12.497772   2183.419302   \n",
+       "min     -124.350000     32.540000            1.000000      2.000000   \n",
+       "25%     -121.760000     33.930000           18.000000   1438.000000   \n",
+       "50%     -118.510000     34.270000           28.000000   2110.000000   \n",
+       "75%     -117.990000     37.730000           37.000000   3123.000000   \n",
+       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
+       "\n",
+       "       total_bedrooms    population    households  median_income  \\\n",
+       "count    19569.000000  19569.000000  19569.000000   19569.000000   \n",
+       "mean       538.841739   1442.788952    501.394859       3.665568   \n",
+       "std        420.612109   1145.011369    383.396308       1.557927   \n",
+       "min          2.000000      3.000000      2.000000       0.499900   \n",
+       "25%        299.000000    797.000000    282.000000       2.522700   \n",
+       "50%        437.000000   1181.000000    411.000000       3.441200   \n",
+       "75%        645.000000   1749.000000    606.000000       4.572100   \n",
+       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+       "\n",
+       "       median_house_value  \n",
+       "count        19569.000000  \n",
+       "mean        190852.301906  \n",
+       "std          95438.555669  \n",
+       "min          14999.000000  \n",
+       "25%         116200.000000  \n",
+       "50%         173200.000000  \n",
+       "75%         246700.000000  \n",
+       "max         482200.000000  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_target_clean.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "20e3222a-84ce-418c-8608-239d1b7f3bd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "longitude             0\n",
+       "latitude              0\n",
+       "housing_median_age    0\n",
+       "total_rooms           0\n",
+       "total_bedrooms        0\n",
+       "population            0\n",
+       "households            0\n",
+       "median_income         0\n",
+       "median_house_value    0\n",
+       "ocean_proximity       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_target_clean.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ad3bb71c-acbc-422f-a0c3-23d95a6b0b87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ocean_proximity\n",
+       "<1H OCEAN     8552\n",
+       "INLAND        6519\n",
+       "NEAR OCEAN    2419\n",
+       "NEAR BAY      2074\n",
+       "ISLAND           5\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_target_clean[\"ocean_proximity\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5066ca46-1e70-4ee6-b4b7-aaf4c618d028",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_target_clean = pd.get_dummies(df_target_clean, columns=[\"ocean_proximity\"], drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "52677998-11b9-4562-9034-6f80083632e2",
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity_INLAND</th>\n",
+       "      <th>ocean_proximity_ISLAND</th>\n",
+       "      <th>ocean_proximity_NEAR BAY</th>\n",
+       "      <th>ocean_proximity_NEAR OCEAN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>452600.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>7099.0</td>\n",
+       "      <td>1106.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>1138.0</td>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>358500.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1467.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>352100.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1274.0</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>341300.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1627.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>342200.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "0    -122.23     37.88                41.0        880.0           129.0   \n",
+       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
+       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
+       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
+       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
+       "\n",
+       "   population  households  median_income  median_house_value  \\\n",
+       "0       322.0       126.0         8.3252            452600.0   \n",
+       "1      2401.0      1138.0         8.3014            358500.0   \n",
+       "2       496.0       177.0         7.2574            352100.0   \n",
+       "3       558.0       219.0         5.6431            341300.0   \n",
+       "4       565.0       259.0         3.8462            342200.0   \n",
+       "\n",
+       "   ocean_proximity_INLAND  ocean_proximity_ISLAND  ocean_proximity_NEAR BAY  \\\n",
+       "0                   False                   False                      True   \n",
+       "1                   False                   False                      True   \n",
+       "2                   False                   False                      True   \n",
+       "3                   False                   False                      True   \n",
+       "4                   False                   False                      True   \n",
+       "\n",
+       "   ocean_proximity_NEAR OCEAN  \n",
+       "0                       False  \n",
+       "1                       False  \n",
+       "2                       False  \n",
+       "3                       False  \n",
+       "4                       False  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_target_clean.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d0b5ab2c-5510-4c4a-84ad-3ab566883b54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df_target_clean.drop(\"median_house_value\", axis = 1)\n",
+    "y = df_target_clean[\"median_house_value\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "5bfe5d00-3a5e-40dc-84a9-53a06fffa490",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d9aa7166-262e-4c2c-af3f-3589d7ab01c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "825c2e0e-68e6-4d81-96d7-37696b2f8554",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
+       "       'total_bedrooms', 'population', 'households', 'median_income',\n",
+       "       'ocean_proximity_INLAND', 'ocean_proximity_ISLAND',\n",
+       "       'ocean_proximity_NEAR BAY', 'ocean_proximity_NEAR OCEAN'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "0757c0a7-dfab-4ab0-a8bf-ae43ef0e984b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from xgboost import XGBRegressor\n",
+    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "f15d4beb-04c9-4830-9e90-46742c50e72d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_model(true, predicted):\n",
+    "    mae = mean_absolute_error(true, predicted)\n",
+    "    mse = mean_squared_error(true, predicted)\n",
+    "    rmse = np.sqrt(mean_squared_error(true, predicted))\n",
+    "    r2_square = r2_score(true, predicted)\n",
+    "    return mae, rmse, r2_square"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "56b77b12-d658-4f2d-9b70-2d56b560fe7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = {\n",
+    "    \"Linear Regression\" : LinearRegression(),\n",
+    "    \"Lasso\" : Lasso(),\n",
+    "    \"Ridge\" : Ridge(),\n",
+    "    \"K Neighbors Regressor\" : KNeighborsRegressor(),\n",
+    "    \"Decision Tree\" : DecisionTreeRegressor(),\n",
+    "    \"Random Forest Regressor\" : RandomForestRegressor(),\n",
+    "    \"Adaboost Regressor\" : AdaBoostRegressor(),\n",
+    "    \"Gradient Boost Regressor\" : GradientBoostingRegressor(),\n",
+    "    \"XGBoost Regressor\" : XGBRegressor()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "d287374e-f63c-43b6-a363-082ef5e91b4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear Regression\n",
+      "Model performance for Training Set\n",
+      "Root Mean Squared Error:  59377.10592926239\n",
+      "Mean Absolute Error:  43858.387482410806\n",
+      "R2 Score:  0.610423647092475\n",
+      "-----------------------------------\n",
+      "Model performance for Test Set\n",
+      "Root Mean Squared Error:  58769.547257392114\n",
+      "Mean Absolute Error:  43594.3638630079\n",
+      "R2 Score:  0.6263296157229526\n",
+      "-----------------------------------\n",
+      "\n",
+      "\n",
+      "Lasso\n",
+      "Model performance for Training Set\n",
+      "Root Mean Squared Error:  59377.14466856272\n",
+      "Mean Absolute Error:  43859.008585346324\n",
+      "R2 Score:  0.6104231387510857\n",
+      "-----------------------------------\n",
+      "Model performance for Test Set\n",
+      "Root Mean Squared Error:  58768.46230442246\n",
+      "Mean Absolute Error:  43594.66878006595\n",
+      "R2 Score:  0.6263434123598097\n",
+      "-----------------------------------\n",
+      "\n",
+      "\n",
+      "Ridge\n",
+      "Model performance for Training Set\n",
+      "Root Mean Squared Error:  59381.16868007145\n",
+      "Mean Absolute Error:  43864.67731493723\n",
+      "R2 Score:  0.6103703334199277\n",
+      "-----------------------------------\n",
+      "Model performance for Test Set\n",
+      "Root Mean Squared Error:  58763.9612580603\n",
+      "Mean Absolute Error:  43597.14291244854\n",
+      "R2 Score:  0.6264006465004595\n",
+      "-----------------------------------\n",
+      "\n",
+      "\n",
+      "K Neighbors Regressor\n",
+      "Model performance for Training Set\n",
+      "Root Mean Squared Error:  72182.63745183809\n",
+      "Mean Absolute Error:  56469.60021901007\n",
+      "R2 Score:  0.42426845114904344\n",
+      "-----------------------------------\n",
+      "Model performance for Test Set\n",
+      "Root Mean Squared Error:  88529.85796376482\n",
+      "Mean Absolute Error:  69873.33350366207\n",
+      "R2 Score:  0.15206312267157307\n",
+      "-----------------------------------\n",
+      "\n",
+      "\n",
+      "Decision Tree\n",
+      "Model performance for Training Set\n",
+      "Root Mean Squared Error:  0.0\n",
+      "Mean Absolute Error:  0.0\n",
+      "R2 Score:  1.0\n",
+      "-----------------------------------\n",
+      "Model performance for Test Set\n",
+      "Root Mean Squared Error:  61596.55951234169\n",
+      "Mean Absolute Error:  41377.31221257026\n",
+      "R2 Score:  0.5895153711481962\n",
+      "-----------------------------------\n",
+      "\n",
+      "\n",
+      "Random Forest Regressor\n",
+      "Model performance for Training Set\n",
+      "Root Mean Squared Error:  16389.480126263974\n",
+      "Mean Absolute Error:  11027.73526719229\n",
+      "R2 Score:  0.9703185651636019\n",
+      "-----------------------------------\n",
+      "Model performance for Test Set\n",
+      "Root Mean Squared Error:  43679.809693639094\n",
+      "Mean Absolute Error:  29665.794331459714\n",
+      "R2 Score:  0.7935829991593981\n",
+      "-----------------------------------\n",
+      "\n",
+      "\n",
+      "Adaboost Regressor\n",
+      "Model performance for Training Set\n",
+      "Root Mean Squared Error:  76106.02225280044\n",
+      "Mean Absolute Error:  65855.81224924499\n",
+      "R2 Score:  0.3599814184240435\n",
+      "-----------------------------------\n",
+      "Model performance for Test Set\n",
+      "Root Mean Squared Error:  76738.6325665839\n",
+      "Mean Absolute Error:  65942.33427336476\n",
+      "R2 Score:  0.3628934049958057\n",
+      "-----------------------------------\n",
+      "\n",
+      "\n",
+      "Gradient Boost Regressor\n",
+      "Model performance for Training Set\n",
+      "Root Mean Squared Error:  47359.96361397147\n",
+      "Mean Absolute Error:  33890.09891379556\n",
+      "R2 Score:  0.7521566585991728\n",
+      "-----------------------------------\n",
+      "Model performance for Test Set\n",
+      "Root Mean Squared Error:  49302.564127118465\n",
+      "Mean Absolute Error:  35159.11233861628\n",
+      "R2 Score:  0.7370198299692805\n",
+      "-----------------------------------\n",
+      "\n",
+      "\n",
+      "XGBoost Regressor\n",
+      "Model performance for Training Set\n",
+      "Root Mean Squared Error:  24400.36680478298\n",
+      "Mean Absolute Error:  17501.127480545747\n",
+      "R2 Score:  0.9342119149001019\n",
+      "-----------------------------------\n",
+      "Model performance for Test Set\n",
+      "Root Mean Squared Error:  42221.55332562842\n",
+      "Mean Absolute Error:  28663.591162466997\n",
+      "R2 Score:  0.8071354526362838\n",
+      "-----------------------------------\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(len(list(models))):\n",
+    "    model = list(models.values())[i]\n",
+    "    model.fit(X_train, y_train)\n",
+    "\n",
+    "    y_train_pred = model.predict(X_train)\n",
+    "    y_test_pred = model.predict(X_test)\n",
+    "\n",
+    "    model_train_mae, model_train_rmse, model_train_r2 = evaluate_model(y_train, y_train_pred)\n",
+    "    model_test_mae, model_test_rmse, model_test_r2 = evaluate_model(y_test, y_test_pred)\n",
+    "\n",
+    "    print(list(models.keys())[i])\n",
+    "    print(\"Model performance for Training Set\")\n",
+    "    print(\"Root Mean Squared Error: \", model_train_rmse)\n",
+    "    print(\"Mean Absolute Error: \", model_train_mae)\n",
+    "    print(\"R2 Score: \", model_train_r2)\n",
+    "\n",
+    "    print(\"-----------------------------------\")\n",
+    "    \n",
+    "    print(\"Model performance for Test Set\")\n",
+    "    print(\"Root Mean Squared Error: \", model_test_rmse)\n",
+    "    print(\"Mean Absolute Error: \", model_test_mae)\n",
+    "    print(\"R2 Score: \", model_test_r2)\n",
+    "\n",
+    "    print(\"-----------------------------------\")\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "3eff2707-d20c-4f6b-9ba6-8c603091f854",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xgboost_params = {\n",
+    "        \"learning_rate\" : [0.1, 0.01],\n",
+    "        \"max_depth\" : [5,8,12,20,30],\n",
+    "        \"n_estimators\" : [100,200,300,500],\n",
+    "        \"colsample_bytree\" : [0.3, 0.4, 0.5, 0.7, 1]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "5b0a0251-0102-47d8-99de-b2c7751f5837",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import RandomizedSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a279c82e-0f0e-4cf0-8da6-6a4acf1f1df3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "randomized_cv = RandomizedSearchCV(estimator=XGBRegressor(), param_distributions=xgboost_params, cv = 5, n_jobs = -1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "fd1c798e-5c39-4177-9d40-337083c789b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/lib/python3.12/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
+       "                   estimator=XGBRegressor(base_score=None, booster=None,\n",
+       "                                          callbacks=None,\n",
+       "                                          colsample_bylevel=None,\n",
+       "                                          colsample_bynode=None,\n",
+       "                                          colsample_bytree=None, device=None,\n",
+       "                                          early_stopping_rounds=None,\n",
+       "                                          enable_categorical=False,\n",
+       "                                          eval_metric=None, feature_types=None,\n",
+       "                                          feature_weights=None, gamma=None,\n",
+       "                                          grow_policy=None,\n",
+       "                                          importance_type=None,\n",
+       "                                          interaction_constraint...\n",
+       "                                          max_cat_to_onehot=None,\n",
+       "                                          max_delta_step=None, max_depth=None,\n",
+       "                                          max_leaves=None,\n",
+       "                                          min_child_weight=None, missing=nan,\n",
+       "                                          monotone_constraints=None,\n",
+       "                                          multi_strategy=None,\n",
+       "                                          n_estimators=None, n_jobs=None,\n",
+       "                                          num_parallel_tree=None, ...),\n",
+       "                   n_jobs=-1,\n",
+       "                   param_distributions={&#x27;colsample_bytree&#x27;: [0.3, 0.4, 0.5, 0.7,\n",
+       "                                                             1],\n",
+       "                                        &#x27;learning_rate&#x27;: [0.1, 0.01],\n",
+       "                                        &#x27;max_depth&#x27;: [5, 8, 12, 20, 30],\n",
+       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 500]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5,\n",
+       "                   estimator=XGBRegressor(base_score=None, booster=None,\n",
+       "                                          callbacks=None,\n",
+       "                                          colsample_bylevel=None,\n",
+       "                                          colsample_bynode=None,\n",
+       "                                          colsample_bytree=None, device=None,\n",
+       "                                          early_stopping_rounds=None,\n",
+       "                                          enable_categorical=False,\n",
+       "                                          eval_metric=None, feature_types=None,\n",
+       "                                          feature_weights=None, gamma=None,\n",
+       "                                          grow_policy=None,\n",
+       "                                          importance_type=None,\n",
+       "                                          interaction_constraint...\n",
+       "                                          max_cat_to_onehot=None,\n",
+       "                                          max_delta_step=None, max_depth=None,\n",
+       "                                          max_leaves=None,\n",
+       "                                          min_child_weight=None, missing=nan,\n",
+       "                                          monotone_constraints=None,\n",
+       "                                          multi_strategy=None,\n",
+       "                                          n_estimators=None, n_jobs=None,\n",
+       "                                          num_parallel_tree=None, ...),\n",
+       "                   n_jobs=-1,\n",
+       "                   param_distributions={&#x27;colsample_bytree&#x27;: [0.3, 0.4, 0.5, 0.7,\n",
+       "                                                             1],\n",
+       "                                        &#x27;learning_rate&#x27;: [0.1, 0.01],\n",
+       "                                        &#x27;max_depth&#x27;: [5, 8, 12, 20, 30],\n",
+       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 500]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: XGBRegressor</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=0.7, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             feature_weights=None, gamma=None, grow_policy=None,\n",
+       "             importance_type=None, interaction_constraints=None,\n",
+       "             learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=20,\n",
+       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
+       "             monotone_constraints=None, multi_strategy=None, n_estimators=300,\n",
+       "             n_jobs=None, num_parallel_tree=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.0.0/python/python_api.html#xgboost.XGBRegressor\">?<span>Documentation for XGBRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=0.7, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             feature_weights=None, gamma=None, grow_policy=None,\n",
+       "             importance_type=None, interaction_constraints=None,\n",
+       "             learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=20,\n",
+       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
+       "             monotone_constraints=None, multi_strategy=None, n_estimators=300,\n",
+       "             n_jobs=None, num_parallel_tree=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomizedSearchCV(cv=5,\n",
+       "                   estimator=XGBRegressor(base_score=None, booster=None,\n",
+       "                                          callbacks=None,\n",
+       "                                          colsample_bylevel=None,\n",
+       "                                          colsample_bynode=None,\n",
+       "                                          colsample_bytree=None, device=None,\n",
+       "                                          early_stopping_rounds=None,\n",
+       "                                          enable_categorical=False,\n",
+       "                                          eval_metric=None, feature_types=None,\n",
+       "                                          feature_weights=None, gamma=None,\n",
+       "                                          grow_policy=None,\n",
+       "                                          importance_type=None,\n",
+       "                                          interaction_constraint...\n",
+       "                                          max_cat_to_onehot=None,\n",
+       "                                          max_delta_step=None, max_depth=None,\n",
+       "                                          max_leaves=None,\n",
+       "                                          min_child_weight=None, missing=nan,\n",
+       "                                          monotone_constraints=None,\n",
+       "                                          multi_strategy=None,\n",
+       "                                          n_estimators=None, n_jobs=None,\n",
+       "                                          num_parallel_tree=None, ...),\n",
+       "                   n_jobs=-1,\n",
+       "                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.5, 0.7,\n",
+       "                                                             1],\n",
+       "                                        'learning_rate': [0.1, 0.01],\n",
+       "                                        'max_depth': [5, 8, 12, 20, 30],\n",
+       "                                        'n_estimators': [100, 200, 300, 500]})"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "randomized_cv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "6241cbbc-8cc3-4f8d-95bb-26380917c7d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_estimators': 300,\n",
+       " 'max_depth': 20,\n",
+       " 'learning_rate': 0.1,\n",
+       " 'colsample_bytree': 0.7}"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "randomized_cv.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "1a24b9ff-10ed-47ce-a6a3-41b7730b3467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# max_depth 20 -> will lead to overfitting, we confirm this by seeing training r2 = 99 while test r2 = 79 in\n",
+    "# next evaluation, that's why i chose max_depth as the default 6 in here\n",
+    "model = XGBRegressor(n_estimators = 300, max_depth = 6, learning_rate = 0.1, colsample_bytree = 0.7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "2e5bda6b-e5e6-49c2-a862-ad3b21935f85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=0.7, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             feature_weights=None, gamma=None, grow_policy=None,\n",
+       "             importance_type=None, interaction_constraints=None,\n",
+       "             learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=6,\n",
+       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
+       "             monotone_constraints=None, multi_strategy=None, n_estimators=300,\n",
+       "             n_jobs=None, num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.0.0/python/python_api.html#xgboost.XGBRegressor\">?<span>Documentation for XGBRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=0.7, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             feature_weights=None, gamma=None, grow_policy=None,\n",
+       "             importance_type=None, interaction_constraints=None,\n",
+       "             learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=6,\n",
+       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
+       "             monotone_constraints=None, multi_strategy=None, n_estimators=300,\n",
+       "             n_jobs=None, num_parallel_tree=None, ...)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=0.7, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             feature_weights=None, gamma=None, grow_policy=None,\n",
+       "             importance_type=None, interaction_constraints=None,\n",
+       "             learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=6,\n",
+       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
+       "             monotone_constraints=None, multi_strategy=None, n_estimators=300,\n",
+       "             n_jobs=None, num_parallel_tree=None, ...)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "1fc50986-9353-47d7-8e89-8ce9976e2499",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBoost Regressor\n",
+      "Model performance for Training Set\n",
+      "Root Mean Squared Error:  24585.120216667747\n",
+      "Mean Absolute Error:  17450.709833751964\n",
+      "R2 Score:  0.933211881666206\n",
+      "-----------------------------------\n",
+      "Model performance for Test Set\n",
+      "Root Mean Squared Error:  41260.67165360902\n",
+      "Mean Absolute Error:  28105.23964155116\n",
+      "R2 Score:  0.8158140174979622\n",
+      "-----------------------------------\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "    y_train_pred = model.predict(X_train)\n",
+    "    y_test_pred = model.predict(X_test)\n",
+    "\n",
+    "    model_train_mae, model_train_rmse, model_train_r2 = evaluate_model(y_train, y_train_pred)\n",
+    "    model_test_mae, model_test_rmse, model_test_r2 = evaluate_model(y_test, y_test_pred)\n",
+    "\n",
+    "    print(list(models.keys())[i])\n",
+    "    print(\"Model performance for Training Set\")\n",
+    "    print(\"Root Mean Squared Error: \", model_train_rmse)\n",
+    "    print(\"Mean Absolute Error: \", model_train_mae)\n",
+    "    print(\"R2 Score: \", model_train_r2)\n",
+    "\n",
+    "    print(\"-----------------------------------\")\n",
+    "    \n",
+    "    print(\"Model performance for Test Set\")\n",
+    "    print(\"Root Mean Squared Error: \", model_test_rmse)\n",
+    "    print(\"Mean Absolute Error: \", model_test_mae)\n",
+    "    print(\"R2 Score: \", model_test_r2)\n",
+    "\n",
+    "    print(\"-----------------------------------\")\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75179de8-cb64-43a5-a628-357942f397be",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c16f4e1-8317-41f6-9cd4-8b03e24bb7e8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/21-XGBoostRegressor.ipynb b/21-XGBoostRegressor.ipynb
index f604f53..52163c9 100644
--- a/21-XGBoostRegressor.ipynb
+++ b/21-XGBoostRegressor.ipynb
@@ -868,13 +868,13 @@
    "outputs": [],
    "source": [
     "def remove_outliers_from_column(df,target_col, threshold = 1.5):\n",
-    "    Q1 = df[col].quantile(0.25)\n",
-    "    Q3 = df[col].quantile(0.75)\n",
+    "    Q1 = df[target_col].quantile(0.25)\n",
+    "    Q3 = df[target_col].quantile(0.75)\n",
     "    IQR = Q3 - Q1\n",
     "\n",
     "    lower_bound = Q1 - threshold * IQR\n",
     "    upper_bound = Q3 + threshold * IQR\n",
-    "    return df[ (df[col] >= lower_bound) & (df[col] <= upper_bound)]"
+    "    return df[ (df[target_col] >= lower_bound) & (df[target_col] <= upper_bound)]"
    ]
   },
   {
@@ -3001,9 +3001,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:base] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-base-py"
   },
   "language_info": {
    "codemirror_mode": {