{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c8117e62-4c93-49fe-b828-ebe5b2e9e252",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6e4dbb-589d-4a26-9805-89d88e386d13",
   "metadata": {},
   "source": [
    "<img src=\"https://www.shutterstock.com/image-photo/cartoon-artistic-image-background-gym-600nw-2649262529.jpg\" width=750>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dd56cd5-2ffc-4ac2-9476-3aabf9dbab0c",
   "metadata": {},
   "source": [
    "### Dataset Download  \n",
    "You can download the CSV file here:  \n",
    "[https://www.kaggle.com/competitions/ml-olympiad-predicting-wellness/data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a5322a6-c20c-4c32-a299-1f7f5209ea57",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "A regression model was built using physical measurements such as age, weight, height, and different body circumferences to estimate body fat density. The goal is to create a model that can be used for practical health-related predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8648dd2b-5a11-4c91-b048-79f6fb5cf53f",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "17447693-2637-40bc-8ae2-bc725eefef57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor,GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a9094e-56ca-463d-bf3f-f9b02ab78a56",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b29207b6-1d15-4016-aaf8-01515aa4ea59",
   "metadata": {},
   "outputs": [],
   "source": [
    "tr=pd.read_csv('bodyfat-comp.csv')\n",
    "te=pd.read_csv('bodyfat-validate.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5863c813-aae9-4f44-bacf-71002f4ecfef",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Density</th>\n",
       "      <th>BodyFat</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Neck</th>\n",
       "      <th>Chest</th>\n",
       "      <th>Abdomen</th>\n",
       "      <th>Hip</th>\n",
       "      <th>Thigh</th>\n",
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       "      <th>Ankle</th>\n",
       "      <th>Biceps</th>\n",
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       "      <th>0</th>\n",
       "      <td>Person2</td>\n",
       "      <td>1.0853</td>\n",
       "      <td>6.1</td>\n",
       "      <td>22</td>\n",
       "      <td>173.25</td>\n",
       "      <td>72.25</td>\n",
       "      <td>38.5</td>\n",
       "      <td>93.6</td>\n",
       "      <td>83.0</td>\n",
       "      <td>98.7</td>\n",
       "      <td>58.7</td>\n",
       "      <td>37.3</td>\n",
       "      <td>23.4</td>\n",
       "      <td>30.5</td>\n",
       "      <td>28.9</td>\n",
       "      <td>18.2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Person3</td>\n",
       "      <td>1.0414</td>\n",
       "      <td>25.3</td>\n",
       "      <td>22</td>\n",
       "      <td>154.00</td>\n",
       "      <td>66.25</td>\n",
       "      <td>34.0</td>\n",
       "      <td>95.8</td>\n",
       "      <td>87.9</td>\n",
       "      <td>99.2</td>\n",
       "      <td>59.6</td>\n",
       "      <td>38.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>16.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Person4</td>\n",
       "      <td>1.0751</td>\n",
       "      <td>10.4</td>\n",
       "      <td>26</td>\n",
       "      <td>184.75</td>\n",
       "      <td>72.25</td>\n",
       "      <td>37.4</td>\n",
       "      <td>101.8</td>\n",
       "      <td>86.4</td>\n",
       "      <td>101.2</td>\n",
       "      <td>60.1</td>\n",
       "      <td>37.3</td>\n",
       "      <td>22.8</td>\n",
       "      <td>32.4</td>\n",
       "      <td>29.4</td>\n",
       "      <td>18.2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Person6</td>\n",
       "      <td>1.0502</td>\n",
       "      <td>20.9</td>\n",
       "      <td>24</td>\n",
       "      <td>210.25</td>\n",
       "      <td>74.75</td>\n",
       "      <td>39.0</td>\n",
       "      <td>104.5</td>\n",
       "      <td>94.4</td>\n",
       "      <td>107.8</td>\n",
       "      <td>66.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>35.7</td>\n",
       "      <td>30.6</td>\n",
       "      <td>18.8</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Person7</td>\n",
       "      <td>1.0549</td>\n",
       "      <td>19.2</td>\n",
       "      <td>26</td>\n",
       "      <td>181.00</td>\n",
       "      <td>69.75</td>\n",
       "      <td>36.4</td>\n",
       "      <td>105.1</td>\n",
       "      <td>90.7</td>\n",
       "      <td>100.3</td>\n",
       "      <td>58.4</td>\n",
       "      <td>38.3</td>\n",
       "      <td>22.9</td>\n",
       "      <td>31.9</td>\n",
       "      <td>27.8</td>\n",
       "      <td>17.7</td>\n",
       "    </tr>\n",
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       "        Id  Density  BodyFat  Age  Weight  Height  Neck  Chest  Abdomen  \\\n",
       "0  Person2   1.0853      6.1   22  173.25   72.25  38.5   93.6     83.0   \n",
       "1  Person3   1.0414     25.3   22  154.00   66.25  34.0   95.8     87.9   \n",
       "2  Person4   1.0751     10.4   26  184.75   72.25  37.4  101.8     86.4   \n",
       "3  Person6   1.0502     20.9   24  210.25   74.75  39.0  104.5     94.4   \n",
       "4  Person7   1.0549     19.2   26  181.00   69.75  36.4  105.1     90.7   \n",
       "\n",
       "     Hip  Thigh  Knee  Ankle  Biceps  Forearm  Wrist  \n",
       "0   98.7   58.7  37.3   23.4    30.5     28.9   18.2  \n",
       "1   99.2   59.6  38.9   24.0    28.8     25.2   16.6  \n",
       "2  101.2   60.1  37.3   22.8    32.4     29.4   18.2  \n",
       "3  107.8   66.0  42.0   25.6    35.7     30.6   18.8  \n",
       "4  100.3   58.4  38.3   22.9    31.9     27.8   17.7  "
      ]
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    "tr.head()"
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   "execution_count": 4,
   "id": "64c4ffaf-49f3-46de-a5a0-e074a0cd6ab1",
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   "outputs": [
    {
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       "      <td>Person244</td>\n",
       "      <td>1.0256</td>\n",
       "      <td>32.6</td>\n",
       "      <td>67</td>\n",
       "      <td>227.75</td>\n",
       "      <td>72.75</td>\n",
       "      <td>41.3</td>\n",
       "      <td>115.8</td>\n",
       "      <td>113.4</td>\n",
       "      <td>109.8</td>\n",
       "      <td>65.6</td>\n",
       "      <td>46.0</td>\n",
       "      <td>25.4</td>\n",
       "      <td>35.3</td>\n",
       "      <td>29.8</td>\n",
       "      <td>19.5</td>\n",
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       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Person246</td>\n",
       "      <td>1.0641</td>\n",
       "      <td>15.2</td>\n",
       "      <td>68</td>\n",
       "      <td>155.50</td>\n",
       "      <td>69.25</td>\n",
       "      <td>36.3</td>\n",
       "      <td>97.4</td>\n",
       "      <td>84.3</td>\n",
       "      <td>94.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>37.5</td>\n",
       "      <td>22.6</td>\n",
       "      <td>29.2</td>\n",
       "      <td>27.3</td>\n",
       "      <td>18.5</td>\n",
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       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Person247</td>\n",
       "      <td>1.0308</td>\n",
       "      <td>30.2</td>\n",
       "      <td>69</td>\n",
       "      <td>215.50</td>\n",
       "      <td>70.50</td>\n",
       "      <td>40.8</td>\n",
       "      <td>113.7</td>\n",
       "      <td>107.6</td>\n",
       "      <td>110.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>37.5</td>\n",
       "      <td>32.6</td>\n",
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       "      <th>166</th>\n",
       "      <td>Person248</td>\n",
       "      <td>1.0736</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>134.25</td>\n",
       "      <td>67.00</td>\n",
       "      <td>34.9</td>\n",
       "      <td>89.2</td>\n",
       "      <td>83.6</td>\n",
       "      <td>88.8</td>\n",
       "      <td>49.6</td>\n",
       "      <td>34.8</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25.7</td>\n",
       "      <td>18.5</td>\n",
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       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Person250</td>\n",
       "      <td>1.0328</td>\n",
       "      <td>29.3</td>\n",
       "      <td>72</td>\n",
       "      <td>186.75</td>\n",
       "      <td>66.00</td>\n",
       "      <td>38.9</td>\n",
       "      <td>111.1</td>\n",
       "      <td>111.5</td>\n",
       "      <td>101.7</td>\n",
       "      <td>60.3</td>\n",
       "      <td>37.3</td>\n",
       "      <td>21.5</td>\n",
       "      <td>31.3</td>\n",
       "      <td>27.2</td>\n",
       "      <td>18.0</td>\n",
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       "            Id  Density  BodyFat  Age  Weight  Height  Neck  Chest  Abdomen  \\\n",
       "163  Person244   1.0256     32.6   67  227.75   72.75  41.3  115.8    113.4   \n",
       "164  Person246   1.0641     15.2   68  155.50   69.25  36.3   97.4     84.3   \n",
       "165  Person247   1.0308     30.2   69  215.50   70.50  40.8  113.7    107.6   \n",
       "166  Person248   1.0736     11.0   70  134.25   67.00  34.9   89.2     83.6   \n",
       "167  Person250   1.0328     29.3   72  186.75   66.00  38.9  111.1    111.5   \n",
       "\n",
       "       Hip  Thigh  Knee  Ankle  Biceps  Forearm  Wrist  \n",
       "163  109.8   65.6  46.0   25.4    35.3     29.8   19.5  \n",
       "164   94.4   54.3  37.5   22.6    29.2     27.3   18.5  \n",
       "165  110.0   63.3  44.0   22.6    37.5     32.6   18.8  \n",
       "166   88.8   49.6  34.8   21.5    25.6     25.7   18.5  \n",
       "167  101.7   60.3  37.3   21.5    31.3     27.2   18.0  "
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    "tr.tail()"
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       "      <td>Person68</td>\n",
       "      <td>55</td>\n",
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       "      <td>71.50</td>\n",
       "      <td>36.9</td>\n",
       "      <td>95.4</td>\n",
       "      <td>86.6</td>\n",
       "      <td>91.8</td>\n",
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       "      <td>Person252</td>\n",
       "      <td>74</td>\n",
       "      <td>207.50</td>\n",
       "      <td>70.00</td>\n",
       "      <td>40.8</td>\n",
       "      <td>112.4</td>\n",
       "      <td>108.5</td>\n",
       "      <td>107.1</td>\n",
       "      <td>59.3</td>\n",
       "      <td>42.2</td>\n",
       "      <td>24.6</td>\n",
       "      <td>33.7</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Person232</td>\n",
       "      <td>57</td>\n",
       "      <td>182.25</td>\n",
       "      <td>71.75</td>\n",
       "      <td>39.4</td>\n",
       "      <td>103.4</td>\n",
       "      <td>96.7</td>\n",
       "      <td>100.7</td>\n",
       "      <td>59.3</td>\n",
       "      <td>38.6</td>\n",
       "      <td>22.8</td>\n",
       "      <td>31.8</td>\n",
       "      <td>29.1</td>\n",
       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Person162</td>\n",
       "      <td>33</td>\n",
       "      <td>196.00</td>\n",
       "      <td>73.00</td>\n",
       "      <td>38.5</td>\n",
       "      <td>103.8</td>\n",
       "      <td>95.6</td>\n",
       "      <td>105.1</td>\n",
       "      <td>61.4</td>\n",
       "      <td>40.6</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.3</td>\n",
       "      <td>29.2</td>\n",
       "      <td>19.1</td>\n",
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       "      <th>4</th>\n",
       "      <td>Person92</td>\n",
       "      <td>44</td>\n",
       "      <td>179.75</td>\n",
       "      <td>69.50</td>\n",
       "      <td>39.2</td>\n",
       "      <td>101.9</td>\n",
       "      <td>93.2</td>\n",
       "      <td>100.6</td>\n",
       "      <td>58.9</td>\n",
       "      <td>39.7</td>\n",
       "      <td>23.1</td>\n",
       "      <td>31.4</td>\n",
       "      <td>28.4</td>\n",
       "      <td>18.8</td>\n",
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      "text/plain": [
       "          Id  Age  Weight  Height  Neck  Chest  Abdomen    Hip  Thigh  Knee  \\\n",
       "0   Person68   55  154.75   71.50  36.9   95.4     86.6   91.8   54.3  35.4   \n",
       "1  Person252   74  207.50   70.00  40.8  112.4    108.5  107.1   59.3  42.2   \n",
       "2  Person232   57  182.25   71.75  39.4  103.4     96.7  100.7   59.3  38.6   \n",
       "3  Person162   33  196.00   73.00  38.5  103.8     95.6  105.1   61.4  40.6   \n",
       "4   Person92   44  179.75   69.50  39.2  101.9     93.2  100.6   58.9  39.7   \n",
       "\n",
       "   Ankle  Biceps  Forearm  Wrist  \n",
       "0   21.5    32.8     27.4   18.7  \n",
       "1   24.6    33.7     30.0   20.9  \n",
       "2   22.8    31.8     29.1   19.0  \n",
       "3   25.0    31.3     29.2   19.1  \n",
       "4   23.1    31.4     28.4   18.8  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "125aba68-88ae-4382-b228-3a2e39e96420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Neck</th>\n",
       "      <th>Chest</th>\n",
       "      <th>Abdomen</th>\n",
       "      <th>Hip</th>\n",
       "      <th>Thigh</th>\n",
       "      <th>Knee</th>\n",
       "      <th>Ankle</th>\n",
       "      <th>Biceps</th>\n",
       "      <th>Forearm</th>\n",
       "      <th>Wrist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Person208</td>\n",
       "      <td>47</td>\n",
       "      <td>195.00</td>\n",
       "      <td>72.50</td>\n",
       "      <td>40.2</td>\n",
       "      <td>102.7</td>\n",
       "      <td>101.3</td>\n",
       "      <td>101.7</td>\n",
       "      <td>60.7</td>\n",
       "      <td>39.4</td>\n",
       "      <td>23.3</td>\n",
       "      <td>36.7</td>\n",
       "      <td>31.6</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Person91</td>\n",
       "      <td>46</td>\n",
       "      <td>177.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>37.2</td>\n",
       "      <td>99.7</td>\n",
       "      <td>95.6</td>\n",
       "      <td>102.2</td>\n",
       "      <td>58.3</td>\n",
       "      <td>38.2</td>\n",
       "      <td>22.5</td>\n",
       "      <td>29.1</td>\n",
       "      <td>27.7</td>\n",
       "      <td>17.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Person15</td>\n",
       "      <td>35</td>\n",
       "      <td>187.75</td>\n",
       "      <td>69.50</td>\n",
       "      <td>40.5</td>\n",
       "      <td>101.3</td>\n",
       "      <td>96.4</td>\n",
       "      <td>100.1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>23.1</td>\n",
       "      <td>36.1</td>\n",
       "      <td>30.5</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Person160</td>\n",
       "      <td>31</td>\n",
       "      <td>177.25</td>\n",
       "      <td>71.50</td>\n",
       "      <td>36.2</td>\n",
       "      <td>101.1</td>\n",
       "      <td>92.4</td>\n",
       "      <td>99.3</td>\n",
       "      <td>59.4</td>\n",
       "      <td>39.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>30.1</td>\n",
       "      <td>28.2</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Person22</td>\n",
       "      <td>28</td>\n",
       "      <td>200.50</td>\n",
       "      <td>69.75</td>\n",
       "      <td>41.3</td>\n",
       "      <td>111.4</td>\n",
       "      <td>98.8</td>\n",
       "      <td>104.8</td>\n",
       "      <td>63.4</td>\n",
       "      <td>40.6</td>\n",
       "      <td>24.6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id  Age  Weight  Height  Neck  Chest  Abdomen    Hip  Thigh  Knee  \\\n",
       "79  Person208   47  195.00   72.50  40.2  102.7    101.3  101.7   60.7  39.4   \n",
       "80   Person91   46  177.00   70.00  37.2   99.7     95.6  102.2   58.3  38.2   \n",
       "81   Person15   35  187.75   69.50  40.5  101.3     96.4  100.1   69.0  39.0   \n",
       "82  Person160   31  177.25   71.50  36.2  101.1     92.4   99.3   59.4  39.0   \n",
       "83   Person22   28  200.50   69.75  41.3  111.4     98.8  104.8   63.4  40.6   \n",
       "\n",
       "    Ankle  Biceps  Forearm  Wrist  \n",
       "79   23.3    36.7     31.6   18.4  \n",
       "80   22.5    29.1     27.7   17.7  \n",
       "81   23.1    36.1     30.5   18.2  \n",
       "82   24.6    30.1     28.2   18.2  \n",
       "83   24.6    33.0     32.8   19.9  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f601f096-cf9a-48b5-bfa2-3e09e4f6f432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 168 entries, 0 to 167\n",
      "Data columns (total 16 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   Id       168 non-null    object \n",
      " 1   Density  168 non-null    float64\n",
      " 2   BodyFat  168 non-null    float64\n",
      " 3   Age      168 non-null    int64  \n",
      " 4   Weight   168 non-null    float64\n",
      " 5   Height   168 non-null    float64\n",
      " 6   Neck     168 non-null    float64\n",
      " 7   Chest    168 non-null    float64\n",
      " 8   Abdomen  168 non-null    float64\n",
      " 9   Hip      168 non-null    float64\n",
      " 10  Thigh    168 non-null    float64\n",
      " 11  Knee     168 non-null    float64\n",
      " 12  Ankle    168 non-null    float64\n",
      " 13  Biceps   168 non-null    float64\n",
      " 14  Forearm  168 non-null    float64\n",
      " 15  Wrist    168 non-null    float64\n",
      "dtypes: float64(14), int64(1), object(1)\n",
      "memory usage: 21.1+ KB\n"
     ]
    }
   ],
   "source": [
    "tr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c39e541d-5e43-4a92-88da-b194316bd221",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 84 entries, 0 to 83\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   Id       84 non-null     object \n",
      " 1   Age      84 non-null     int64  \n",
      " 2   Weight   84 non-null     float64\n",
      " 3   Height   84 non-null     float64\n",
      " 4   Neck     84 non-null     float64\n",
      " 5   Chest    84 non-null     float64\n",
      " 6   Abdomen  84 non-null     float64\n",
      " 7   Hip      84 non-null     float64\n",
      " 8   Thigh    84 non-null     float64\n",
      " 9   Knee     84 non-null     float64\n",
      " 10  Ankle    84 non-null     float64\n",
      " 11  Biceps   84 non-null     float64\n",
      " 12  Forearm  84 non-null     float64\n",
      " 13  Wrist    84 non-null     float64\n",
      "dtypes: float64(12), int64(1), object(1)\n",
      "memory usage: 9.3+ KB\n"
     ]
    }
   ],
   "source": [
    "te.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d512533-df7d-46ee-94ee-1188f1be8746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id         0\n",
       "Density    0\n",
       "BodyFat    0\n",
       "Age        0\n",
       "Weight     0\n",
       "Height     0\n",
       "Neck       0\n",
       "Chest      0\n",
       "Abdomen    0\n",
       "Hip        0\n",
       "Thigh      0\n",
       "Knee       0\n",
       "Ankle      0\n",
       "Biceps     0\n",
       "Forearm    0\n",
       "Wrist      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b70415ce-8d8a-4f45-958c-b89c454458dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id         0\n",
       "Age        0\n",
       "Weight     0\n",
       "Height     0\n",
       "Neck       0\n",
       "Chest      0\n",
       "Abdomen    0\n",
       "Hip        0\n",
       "Thigh      0\n",
       "Knee       0\n",
       "Ankle      0\n",
       "Biceps     0\n",
       "Forearm    0\n",
       "Wrist      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c7fa6a3d-860f-49ff-8d88-aea423a81309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>BodyFat</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Neck</th>\n",
       "      <th>Chest</th>\n",
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       "      <th>Hip</th>\n",
       "      <th>Thigh</th>\n",
       "      <th>Knee</th>\n",
       "      <th>Ankle</th>\n",
       "      <th>Biceps</th>\n",
       "      <th>Forearm</th>\n",
       "      <th>Wrist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.055817</td>\n",
       "      <td>18.994048</td>\n",
       "      <td>44.720238</td>\n",
       "      <td>177.323214</td>\n",
       "      <td>70.053571</td>\n",
       "      <td>37.961310</td>\n",
       "      <td>100.194643</td>\n",
       "      <td>91.944048</td>\n",
       "      <td>99.618452</td>\n",
       "      <td>59.279167</td>\n",
       "      <td>38.470833</td>\n",
       "      <td>23.057143</td>\n",
       "      <td>32.196429</td>\n",
       "      <td>28.656548</td>\n",
       "      <td>18.173214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.018204</td>\n",
       "      <td>8.098242</td>\n",
       "      <td>12.691610</td>\n",
       "      <td>25.441979</td>\n",
       "      <td>4.061756</td>\n",
       "      <td>2.192801</td>\n",
       "      <td>7.666736</td>\n",
       "      <td>9.747560</td>\n",
       "      <td>6.176463</td>\n",
       "      <td>4.688064</td>\n",
       "      <td>2.275102</td>\n",
       "      <td>1.508323</td>\n",
       "      <td>2.767409</td>\n",
       "      <td>1.948365</td>\n",
       "      <td>0.810668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.995000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>127.500000</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>83.400000</td>\n",
       "      <td>70.400000</td>\n",
       "      <td>85.300000</td>\n",
       "      <td>49.300000</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>16.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.041550</td>\n",
       "      <td>12.175000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>68.250000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>93.825000</td>\n",
       "      <td>84.250000</td>\n",
       "      <td>95.575000</td>\n",
       "      <td>56.075000</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>22.075000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>17.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.054900</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>174.125000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>37.900000</td>\n",
       "      <td>99.550000</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>99.150000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>38.300000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>31.950000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>18.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.071000</td>\n",
       "      <td>25.225000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>195.250000</td>\n",
       "      <td>72.250000</td>\n",
       "      <td>39.325000</td>\n",
       "      <td>104.750000</td>\n",
       "      <td>98.875000</td>\n",
       "      <td>103.125000</td>\n",
       "      <td>62.200000</td>\n",
       "      <td>39.725000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>34.300000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>18.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.092600</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>244.250000</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>43.900000</td>\n",
       "      <td>121.600000</td>\n",
       "      <td>122.100000</td>\n",
       "      <td>115.500000</td>\n",
       "      <td>72.900000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>33.900000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>34.900000</td>\n",
       "      <td>20.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Density     BodyFat         Age      Weight      Height        Neck  \\\n",
       "count  168.000000  168.000000  168.000000  168.000000  168.000000  168.000000   \n",
       "mean     1.055817   18.994048   44.720238  177.323214   70.053571   37.961310   \n",
       "std      0.018204    8.098242   12.691610   25.441979    4.061756    2.192801   \n",
       "min      0.995000    3.000000   22.000000  127.500000   29.500000   31.100000   \n",
       "25%      1.041550   12.175000   35.000000  159.000000   68.250000   36.400000   \n",
       "50%      1.054900   19.200000   43.000000  174.125000   70.000000   37.900000   \n",
       "75%      1.071000   25.225000   54.000000  195.250000   72.250000   39.325000   \n",
       "max      1.092600   47.500000   72.000000  244.250000   77.500000   43.900000   \n",
       "\n",
       "            Chest     Abdomen         Hip       Thigh        Knee       Ankle  \\\n",
       "count  168.000000  168.000000  168.000000  168.000000  168.000000  168.000000   \n",
       "mean   100.194643   91.944048   99.618452   59.279167   38.470833   23.057143   \n",
       "std      7.666736    9.747560    6.176463    4.688064    2.275102    1.508323   \n",
       "min     83.400000   70.400000   85.300000   49.300000   33.400000   20.100000   \n",
       "25%     93.825000   84.250000   95.575000   56.075000   36.900000   22.075000   \n",
       "50%     99.550000   90.800000   99.150000   58.900000   38.300000   22.700000   \n",
       "75%    104.750000   98.875000  103.125000   62.200000   39.725000   23.800000   \n",
       "max    121.600000  122.100000  115.500000   72.900000   46.000000   33.900000   \n",
       "\n",
       "           Biceps     Forearm       Wrist  \n",
       "count  168.000000  168.000000  168.000000  \n",
       "mean    32.196429   28.656548   18.173214  \n",
       "std      2.767409    1.948365    0.810668  \n",
       "min     25.600000   21.000000   16.300000  \n",
       "25%     30.200000   27.300000   17.600000  \n",
       "50%     31.950000   28.600000   18.250000  \n",
       "75%     34.300000   30.000000   18.800000  \n",
       "max     38.500000   34.900000   20.200000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ee256988-61c4-468b-b3ac-abac4c335a61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(168, 16)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bbf8c51c-05d7-4033-bf97-2cfebc963700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84, 14)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dd731467-67eb-47ba-8c2a-0f5e31f7d7f6",
   "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>Density</th>\n",
       "      <th>BodyFat</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Neck</th>\n",
       "      <th>Chest</th>\n",
       "      <th>Abdomen</th>\n",
       "      <th>Hip</th>\n",
       "      <th>Thigh</th>\n",
       "      <th>Knee</th>\n",
       "      <th>Ankle</th>\n",
       "      <th>Biceps</th>\n",
       "      <th>Forearm</th>\n",
       "      <th>Wrist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Density</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.998886</td>\n",
       "      <td>-0.233494</td>\n",
       "      <td>-0.582952</td>\n",
       "      <td>0.149804</td>\n",
       "      <td>-0.432339</td>\n",
       "      <td>-0.683622</td>\n",
       "      <td>-0.806419</td>\n",
       "      <td>-0.612034</td>\n",
       "      <td>-0.550080</td>\n",
       "      <td>-0.439740</td>\n",
       "      <td>-0.123453</td>\n",
       "      <td>-0.458876</td>\n",
       "      <td>-0.261861</td>\n",
       "      <td>-0.227499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BodyFat</th>\n",
       "      <td>-0.998886</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238958</td>\n",
       "      <td>0.580351</td>\n",
       "      <td>-0.155602</td>\n",
       "      <td>0.431046</td>\n",
       "      <td>0.681594</td>\n",
       "      <td>0.805204</td>\n",
       "      <td>0.611006</td>\n",
       "      <td>0.546547</td>\n",
       "      <td>0.436767</td>\n",
       "      <td>0.120453</td>\n",
       "      <td>0.455961</td>\n",
       "      <td>0.259636</td>\n",
       "      <td>0.227795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.233494</td>\n",
       "      <td>0.238958</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.095632</td>\n",
       "      <td>-0.198223</td>\n",
       "      <td>0.089267</td>\n",
       "      <td>0.142609</td>\n",
       "      <td>0.211897</td>\n",
       "      <td>-0.128182</td>\n",
       "      <td>-0.248580</td>\n",
       "      <td>-0.040640</td>\n",
       "      <td>-0.240363</td>\n",
       "      <td>-0.078470</td>\n",
       "      <td>-0.149905</td>\n",
       "      <td>0.155011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>-0.582952</td>\n",
       "      <td>0.580351</td>\n",
       "      <td>-0.095632</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.255341</td>\n",
       "      <td>0.768575</td>\n",
       "      <td>0.886094</td>\n",
       "      <td>0.854161</td>\n",
       "      <td>0.932861</td>\n",
       "      <td>0.846469</td>\n",
       "      <td>0.835699</td>\n",
       "      <td>0.537630</td>\n",
       "      <td>0.786406</td>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.658868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>0.149804</td>\n",
       "      <td>-0.155602</td>\n",
       "      <td>-0.198223</td>\n",
       "      <td>0.255341</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.206130</td>\n",
       "      <td>0.096477</td>\n",
       "      <td>0.020138</td>\n",
       "      <td>0.092696</td>\n",
       "      <td>0.050735</td>\n",
       "      <td>0.201468</td>\n",
       "      <td>0.244313</td>\n",
       "      <td>0.178943</td>\n",
       "      <td>0.199221</td>\n",
       "      <td>0.293180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neck</th>\n",
       "      <td>-0.432339</td>\n",
       "      <td>0.431046</td>\n",
       "      <td>0.089267</td>\n",
       "      <td>0.768575</td>\n",
       "      <td>0.206130</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.736974</td>\n",
       "      <td>0.688699</td>\n",
       "      <td>0.653906</td>\n",
       "      <td>0.605201</td>\n",
       "      <td>0.601090</td>\n",
       "      <td>0.361136</td>\n",
       "      <td>0.663869</td>\n",
       "      <td>0.598396</td>\n",
       "      <td>0.684473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chest</th>\n",
       "      <td>-0.683622</td>\n",
       "      <td>0.681594</td>\n",
       "      <td>0.142609</td>\n",
       "      <td>0.886094</td>\n",
       "      <td>0.096477</td>\n",
       "      <td>0.736974</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.903205</td>\n",
       "      <td>0.816447</td>\n",
       "      <td>0.712954</td>\n",
       "      <td>0.685727</td>\n",
       "      <td>0.381960</td>\n",
       "      <td>0.727498</td>\n",
       "      <td>0.515783</td>\n",
       "      <td>0.574647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdomen</th>\n",
       "      <td>-0.806419</td>\n",
       "      <td>0.805204</td>\n",
       "      <td>0.211897</td>\n",
       "      <td>0.854161</td>\n",
       "      <td>0.020138</td>\n",
       "      <td>0.688699</td>\n",
       "      <td>0.903205</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.841958</td>\n",
       "      <td>0.726147</td>\n",
       "      <td>0.684706</td>\n",
       "      <td>0.322703</td>\n",
       "      <td>0.649429</td>\n",
       "      <td>0.425356</td>\n",
       "      <td>0.512487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hip</th>\n",
       "      <td>-0.612034</td>\n",
       "      <td>0.611006</td>\n",
       "      <td>-0.128182</td>\n",
       "      <td>0.932861</td>\n",
       "      <td>0.092696</td>\n",
       "      <td>0.653906</td>\n",
       "      <td>0.816447</td>\n",
       "      <td>0.841958</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.898364</td>\n",
       "      <td>0.826642</td>\n",
       "      <td>0.488204</td>\n",
       "      <td>0.730192</td>\n",
       "      <td>0.528022</td>\n",
       "      <td>0.552337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thigh</th>\n",
       "      <td>-0.550080</td>\n",
       "      <td>0.546547</td>\n",
       "      <td>-0.248580</td>\n",
       "      <td>0.846469</td>\n",
       "      <td>0.050735</td>\n",
       "      <td>0.605201</td>\n",
       "      <td>0.712954</td>\n",
       "      <td>0.726147</td>\n",
       "      <td>0.898364</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.790278</td>\n",
       "      <td>0.472632</td>\n",
       "      <td>0.715126</td>\n",
       "      <td>0.518326</td>\n",
       "      <td>0.475385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knee</th>\n",
       "      <td>-0.439740</td>\n",
       "      <td>0.436767</td>\n",
       "      <td>-0.040640</td>\n",
       "      <td>0.835699</td>\n",
       "      <td>0.201468</td>\n",
       "      <td>0.601090</td>\n",
       "      <td>0.685727</td>\n",
       "      <td>0.684706</td>\n",
       "      <td>0.826642</td>\n",
       "      <td>0.790278</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.574567</td>\n",
       "      <td>0.637310</td>\n",
       "      <td>0.494940</td>\n",
       "      <td>0.601573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ankle</th>\n",
       "      <td>-0.123453</td>\n",
       "      <td>0.120453</td>\n",
       "      <td>-0.240363</td>\n",
       "      <td>0.537630</td>\n",
       "      <td>0.244313</td>\n",
       "      <td>0.361136</td>\n",
       "      <td>0.381960</td>\n",
       "      <td>0.322703</td>\n",
       "      <td>0.488204</td>\n",
       "      <td>0.472632</td>\n",
       "      <td>0.574567</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.393001</td>\n",
       "      <td>0.354374</td>\n",
       "      <td>0.507432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biceps</th>\n",
       "      <td>-0.458876</td>\n",
       "      <td>0.455961</td>\n",
       "      <td>-0.078470</td>\n",
       "      <td>0.786406</td>\n",
       "      <td>0.178943</td>\n",
       "      <td>0.663869</td>\n",
       "      <td>0.727498</td>\n",
       "      <td>0.649429</td>\n",
       "      <td>0.730192</td>\n",
       "      <td>0.715126</td>\n",
       "      <td>0.637310</td>\n",
       "      <td>0.393001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676421</td>\n",
       "      <td>0.578034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forearm</th>\n",
       "      <td>-0.261861</td>\n",
       "      <td>0.259636</td>\n",
       "      <td>-0.149905</td>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.199221</td>\n",
       "      <td>0.598396</td>\n",
       "      <td>0.515783</td>\n",
       "      <td>0.425356</td>\n",
       "      <td>0.528022</td>\n",
       "      <td>0.518326</td>\n",
       "      <td>0.494940</td>\n",
       "      <td>0.354374</td>\n",
       "      <td>0.676421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.521716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrist</th>\n",
       "      <td>-0.227499</td>\n",
       "      <td>0.227795</td>\n",
       "      <td>0.155011</td>\n",
       "      <td>0.658868</td>\n",
       "      <td>0.293180</td>\n",
       "      <td>0.684473</td>\n",
       "      <td>0.574647</td>\n",
       "      <td>0.512487</td>\n",
       "      <td>0.552337</td>\n",
       "      <td>0.475385</td>\n",
       "      <td>0.601573</td>\n",
       "      <td>0.507432</td>\n",
       "      <td>0.578034</td>\n",
       "      <td>0.521716</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Density   BodyFat       Age    Weight    Height      Neck     Chest  \\\n",
       "Density  1.000000 -0.998886 -0.233494 -0.582952  0.149804 -0.432339 -0.683622   \n",
       "BodyFat -0.998886  1.000000  0.238958  0.580351 -0.155602  0.431046  0.681594   \n",
       "Age     -0.233494  0.238958  1.000000 -0.095632 -0.198223  0.089267  0.142609   \n",
       "Weight  -0.582952  0.580351 -0.095632  1.000000  0.255341  0.768575  0.886094   \n",
       "Height   0.149804 -0.155602 -0.198223  0.255341  1.000000  0.206130  0.096477   \n",
       "Neck    -0.432339  0.431046  0.089267  0.768575  0.206130  1.000000  0.736974   \n",
       "Chest   -0.683622  0.681594  0.142609  0.886094  0.096477  0.736974  1.000000   \n",
       "Abdomen -0.806419  0.805204  0.211897  0.854161  0.020138  0.688699  0.903205   \n",
       "Hip     -0.612034  0.611006 -0.128182  0.932861  0.092696  0.653906  0.816447   \n",
       "Thigh   -0.550080  0.546547 -0.248580  0.846469  0.050735  0.605201  0.712954   \n",
       "Knee    -0.439740  0.436767 -0.040640  0.835699  0.201468  0.601090  0.685727   \n",
       "Ankle   -0.123453  0.120453 -0.240363  0.537630  0.244313  0.361136  0.381960   \n",
       "Biceps  -0.458876  0.455961 -0.078470  0.786406  0.178943  0.663869  0.727498   \n",
       "Forearm -0.261861  0.259636 -0.149905  0.624012  0.199221  0.598396  0.515783   \n",
       "Wrist   -0.227499  0.227795  0.155011  0.658868  0.293180  0.684473  0.574647   \n",
       "\n",
       "          Abdomen       Hip     Thigh      Knee     Ankle    Biceps   Forearm  \\\n",
       "Density -0.806419 -0.612034 -0.550080 -0.439740 -0.123453 -0.458876 -0.261861   \n",
       "BodyFat  0.805204  0.611006  0.546547  0.436767  0.120453  0.455961  0.259636   \n",
       "Age      0.211897 -0.128182 -0.248580 -0.040640 -0.240363 -0.078470 -0.149905   \n",
       "Weight   0.854161  0.932861  0.846469  0.835699  0.537630  0.786406  0.624012   \n",
       "Height   0.020138  0.092696  0.050735  0.201468  0.244313  0.178943  0.199221   \n",
       "Neck     0.688699  0.653906  0.605201  0.601090  0.361136  0.663869  0.598396   \n",
       "Chest    0.903205  0.816447  0.712954  0.685727  0.381960  0.727498  0.515783   \n",
       "Abdomen  1.000000  0.841958  0.726147  0.684706  0.322703  0.649429  0.425356   \n",
       "Hip      0.841958  1.000000  0.898364  0.826642  0.488204  0.730192  0.528022   \n",
       "Thigh    0.726147  0.898364  1.000000  0.790278  0.472632  0.715126  0.518326   \n",
       "Knee     0.684706  0.826642  0.790278  1.000000  0.574567  0.637310  0.494940   \n",
       "Ankle    0.322703  0.488204  0.472632  0.574567  1.000000  0.393001  0.354374   \n",
       "Biceps   0.649429  0.730192  0.715126  0.637310  0.393001  1.000000  0.676421   \n",
       "Forearm  0.425356  0.528022  0.518326  0.494940  0.354374  0.676421  1.000000   \n",
       "Wrist    0.512487  0.552337  0.475385  0.601573  0.507432  0.578034  0.521716   \n",
       "\n",
       "            Wrist  \n",
       "Density -0.227499  \n",
       "BodyFat  0.227795  \n",
       "Age      0.155011  \n",
       "Weight   0.658868  \n",
       "Height   0.293180  \n",
       "Neck     0.684473  \n",
       "Chest    0.574647  \n",
       "Abdomen  0.512487  \n",
       "Hip      0.552337  \n",
       "Thigh    0.475385  \n",
       "Knee     0.601573  \n",
       "Ankle    0.507432  \n",
       "Biceps   0.578034  \n",
       "Forearm  0.521716  \n",
       "Wrist    1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr.corr(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aa2c3805-78e1-42f5-aa23-0636a63fecb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nlmUVKo3kjCyZLVYF+9tebhvi56OEtMx/3D8+NUPrdh/Wq3ddX2qZnUdO6+DpBD13W99/HW9llZyeIbPFopDzZpOGBPgrIbnkK2laN6mvyY/cocff+kR5+fkymS3qfnlLPXn3yMIynVs306xFK9WueQPVrBamDTv2afWG7TaX6Tqb5LTMgrYM8LPZHhLgp4SUUtqycV1NHn+LJkz9sqgt21+mJ++8obBM3erhevG+G9WwVqQys3P09ZLfNeqZdzX3zcdVOzKsxPet6pLT0s/2S9uTXCGBAUpMLvlKuivbtdTCZat1def2atagjvYcOKLvfv5NJpNZKanpCgspGKOmZ2ap503jlJ9vkouLiyY9MKpYwt5ZFPVJX5vtIQG+SkgpviSGdLZPPnCzJkyddU6fbK4n7xhcWKZu9Qi9eN9INax5tk8uXaNRz07T3Dcedd4+mZ5VelumltKWjepo8v0jNeG9r5WXbypoy3bN9OTtRVegbd1/VN+t3qi5rz5UnuFXKgYvXxlcXGXNsv1etGaly+Bd8pUN1sxU5a6cJcuZYzIY3eTa5Ap53PCwcudPkeVUwck1l8j6cm1+pXK+fqnc61AZ/D0OCvG3XSojxN9HCakZ/7h/fEq61u06qMl3D7bZ3rl5Pc1asUHtGtVWzbAgbdh7RKu3RTv38TurYEmWYF9Pm+0hPh5KyCh5WdBTKZnaeiJR7kZXTRnaUSnZeXp12TalZufphevblbjPO6t2K9zPS1c42US2cyVn5RaMz89vS19PJaSXvMzvqaQMbT0eJ3eji6bc1FUpWbl69ceNSs3O1QuDOxWUSc7QxiMZ6teyrqbd2l3HE9M1+aeNMlusuqeHcx7Dgf+ai06kT5o0SXXr1tW0adM0a9YsSVLjxo01Y8YM3XTTTZKksWPH6t577y31PXJzc5Wba5toNVvNcjWU340kuw7qprGT7y98/sqoFwv+cd5ldgaDwe4NQaO37lf01qLLavdt3Kv/LZmqfndcr0+fmy5Juur6Luo2uLvefuAtHY8+rrrN6+mu50Yr+UySVs3/tewqVUkYDLbPrVbJUHJR1Qn2VZ3gooF9q+pBOpOeoy83Hi5MpG86kahPNxzUxJ6XqUVkgE6kZOnNVXs0ff0BjelUdZfHibzhSjV/8+7C55tvfr3gH8Uu9dQ/35T2H/bZcf/7ajH1HvXY8aEsJrPSdh5RzMJ18m9RMCPWcHb2b9yyzTr28RJJUvruYwrq0Ei1bu9FIh1Fzu+KhpI2Fufetae8bxql9JefljU15Zz9XWRJTVHm+29JFovMh6LlEhwqryEjnTaR/jfDed+MVllL/a4816I/dsrPy1NXty79SpXv1+1Qg+qhanH2SihnVmI7ltKQh06c1usz5uqeEdfpyjbNFJ+cqilfLNTLH36tFx4oWPf3idHD9cL7X2nguOdlkEE1qoVpYM/O+uGXkpeTcCYGw/ltWXzb3w6djNXrn3+ne4Zeo86tmig+OU1vf7VIL8+YpxfuLTgx0bJRHbVsVKdwn9aN62rkE//Tt0vX6Mk7h5RXNSqH89vSznH8npsHKyE5Vbc8+JysVqtCggI0sHdXfT7vJ7m4Fl2d4+PlqfkfvKqsnBxt2Lpbb378tWpUC1eHVs51v5hzFeuT1uLjzL8dOhmr17/4Xvfc0FudWzUu6JNf/6SXP5mvF8aOkCS1bFRbLRvVLtyndeM6Gvnk2/p22VqbhLszKv5dWXzb3w6dPKPXZy7SPYN7qXOrRgVt+c0SvfzZQr0wZpgys3P11Aez9dzoGxTkX/K6wShgTT4j89mbilolWWIOy8UvWMa2vZV36oDk5iH3Pncq75dZUs4/n0x3JiV/vv95JLTojx3y8/bU1W1sr1iZcOM1enHmYg2a9KEMBqlGWJAGXtlKP6zbXqZxV0bnt5q970rL2ddeHdhBfp4FV0M91quFHluwQRP7tJbnebP6P18frWW7T+iTW7rKw1h++ZnKouS2LLkxLVarDDLo1WFXys+zYLb+Y33MemzOGk3s30GebkZZrFYF+3jqmYGXy9XFRc2iQhSfnq2Za/eQSAecxEUn0iXp5ptv1s0331zq615e9m9IMXnyZL3wwgs22xr7N1TTgPJb1/WvFX8pemvRWlduZ290GRgWpOS4ohvFBIQEKPW8Wer2WK1WHdxxQNXrFCUvbn/6Di38YL7W/rhGknR8/zGFRYVpyH3DnCqRHuTlLleDQYmZtidFkrLyFOxz4Uv7tIgM1JK9RcsbfLAuWtc1i9KQljUlSQ3D/JWdb9bLK3ZqdMcGcrmAAVdlFLdss1I3Fy0V4HK2D7qHByo3LqVwu3toQLEZ5+fKjUuR+9nZ56Xtk33sjP4a/KJcvT1k9PVSblyKWk1/UFnHC9a5y0tKkyXfpIxo28spM6JPK+gK511fGRfOmpYqq9kklyDb2eeGgCBZUkq/uZZUcJNS3/ETlP7ac8rfbrv2pSU5UVaTSTrn8kbzyWNyCQ6RjEaphGUNqrogX2+5uhiUeN7s86T0rGKzs85ntVr1/R87dV3H5nIr5cdMdl6+ft64V/cO6FJmMVdGQX6+cnVxUUKK7fdjUmp6sVnqf/t0/s9q3bS+7hh8jSSpUZ0a8vLw0Kin3tK4mwcqLDhAwQF+euepe5Wbl6+U9AyFBwdq6pffKSqi9CuDqrogf5+zbWk7yzIpNb3YLPW/ffrdSrVuXFejBhTc/K1R7ery8nTXHc++p3Ej+yosqPiyQy4uLmpev5aOxzrv+r9B/n5ydXFRYnKKzfak1DSFlNAmkuTp4a6XHh2jZx+8U4nJqQoLDtL8Jb/Kx9tTQf5F7e/i4qJaUQU3zGtSv44OnzitT+YscspEelGftJ0xnZSWUXqf/P5XtW5UR6MG9JB0Tp987n2NG9FXYUHFvxcK+mRNHY8t+bJ9ZxDk513QlufNPk9KzSg2S/1vny5aVdCW1xes+92oVqS8PNx1x4sfadywa5WYmqHT8cka/1bRCW/L2ZNFbW+ZqB/+95hqRoSU+N5VmTU7Q1aLudjsc4O3X7FZ6vaYYw7L2PSKgn0Dw+QSECqPAUUTvP7OgHqN/0A5M5+VNdW5+uff46DzZ58npWde2Dho7Tb179ii2Dgo2M9HU8cNV26+SSkZWQoP9NPUBb+qemhgWVeh0gjy9ij4/X3e7POkrFyF+HiWuE+or6fC/bwKk+iSVDfUT1ZJZ9KzVfucSW4z/4zWp+v26+ObrlKjiNKXE3QGQd4F92kr1paZOQrxLaUt/bwU7u9VmESXpLphAbJapTNpWaod4q8wPy8ZXVzkes6ylXXD/JWQkaN8k7nU8TyAqsMhi9JOnDhRqampNo9G/g3K9W/mZGYr9lhM4eNE9HElxSWpVZfWhWWMbkY1v+Iy7du876Leu06zekqOK1r+wMPLQ5bzLimzWCxycamaCeDSuLm6qGmEv/48ZjvY+/NYglpVD7zg99kXl6bQcw78OfnmYslyF4NBVlXtGymbM3OUdfRM4SNj/0nlnElWaLeiM9MGN1cFd2qqlI0l3+BEklI2H1BoV9uz2aHdWiplU/F9zFm5yo1LkTHAR6HdWyru54KkpjXfrNRth+VT33b2qk/9aso+6VyDd1wik0mmg9Fya2O7br5b6/Yy7d1V6m7uXXvK96GJSn/rJeVvKn6zp/w9u+QaGWUzbca1eg1ZEhOcMokuSW5GVzWtVU3r9x612b5h71G1qh9ld99N0Sd0Ii5Zg69sWWqZ5Zv2Kc9k1nVXNC+LcCstNzejmtavpfXbbK+Y+XPbXrVuUq/EfbJz84odT/7+YWM978oKD3c3RYQEyWS2aOX6rep+eSs5KzejUU3r1dCfO2yPG3/uiFarxnVK3CcnN7/YDK3Ctizl2Gy1WrX/2Cmnvrmjm5tRzRrW1fottt+L67fsVOtm9q+iczMaVS0sRK6uLlr623p1vbyNXOzcL8JqtSov31m/J+30yXOucjhXTm6eDC6l9cmSO6XVatX+o6cVGlhyct4ZuBmNalo3Sn/utF2j/89dB9TqnNn558rJzS+9LSXVrR6m+a8/rDmTHyx8dG/bVB2a1dOcyQ+qmp37d1RpFrMsccflUsv2XhCutZrKEnPhN0l3Ca8pa2bBSWBrUqyyZ72gnK9fLnyYD++Q5US0cr5+WdZ0+5MVqiI3o6ua1o7Un3uO2Gz/c88Rtapfw+6+m/Yf0/G4ZA065zf7+TzcjIoI8pfJbNEvm/eph50r+Ko6N1cXNY0M1PojtjcB3XAkTq1qlLz0YusaIYpPz1FWXtHx41hihlwMUoRf0QTIL9ZHa8baffrgxivVvLrz3xjTzeiqptWDtf5QjM32DYdi1KpmyZMpWtcKU3x6trLOuRfZscQ0uRgMiji7hGOrWmE6npRukw86lpiuMD8vkuiVncXC42If/1EXNCM9ODhY0dHRCg0NVVBQkN1LsJKSSr7Jxbk8PDyK3Yy0PJd1Kc1Pny7S0PuHKebIacUcOa0bxg1Xbk6ufv/+t8Iy499+WEmxifrq9S8lScMfGqnoLfsVc/S0vHy91f+O61W3WV3NmPRh4T4bV27U0AeGK+F0vI5HH1e95vU0YPQg/TJ3RYXXsbzd0q6uJi3drmYRAWpZPUgLdxxXbHq2hrYqGKi/u2af4jJy9XLfgoTE15uPqLq/l+qF+slktmjx3lP65UCs3hrQtvA9u9YP11ebj6pxuL9aRAbqRHKmPvwjWt3qRcjVyU5GHJu+VPUeHKTMw7HKOhKjeg8Oljk7V6cXFt14qMV79yk3NknRr8wu3OfyH55T3XEDFLdsk8L7tFdI18u0YcDzhfuEdm8pGQzKPHRa3nWqqfFzNyvzUIxOfbu6sMyR939U6+kPKvnPvUpau1uhV7dW2DXt9NfgFyuq+g6XlZWt4ydPFz4/dfqM9kUfUoC/nyKrOe+agBcq5/u58n3kaZkO7Jdp32559Okv17BwpS9dJEnyvu1uuYSEKePtVyWdTaI//JQyZ7wn0749Mvy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",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(tr.corr(numeric_only=True), annot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "714380a1-4e6d-4711-81ae-0a506a1a532f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BodyFat    0.998886\n",
       "Abdomen    0.806419\n",
       "Chest      0.683622\n",
       "Hip        0.612034\n",
       "Weight     0.582952\n",
       "Thigh      0.550080\n",
       "Biceps     0.458876\n",
       "Knee       0.439740\n",
       "Neck       0.432339\n",
       "Forearm    0.261861\n",
       "Age        0.233494\n",
       "Wrist      0.227499\n",
       "Ankle      0.123453\n",
       "Height     0.149804\n",
       "Density    1.000000\n",
       "Name: Density, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(tr.corr(numeric_only=True)['Density'].sort_values(ascending=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8884cce4-8c3e-4a76-aaf4-6a034e38cc60",
   "metadata": {},
   "source": [
    "BodyFat is almost perfectly negatively correlated with Density, while abdomen, chest, hip, and weight also show strong correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "121badf1-58fa-4575-9ee4-6b03583ea3e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(x=tr['Density'], fill=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd42d19-77cb-4f50-ae7a-8b71c6ddc0fb",
   "metadata": {},
   "source": [
    "The target variable Density shows a roughly normal distribution, making it suitable for regression modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b3478a75-52fa-4e3b-8740-836483e7b3f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tr.select_dtypes('number').plot(kind='box', figsize=(16,10), vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "38b1faed-699b-4a05-b21a-3d3ea25d9fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MzMwWm/u93FnX7fLz8wWA5OTkWLQ7Ql0i966tzvHjxwWAFBQUiIj9amu2zzhv3bqFLVu2YPLkydBoNMjPz0dxcTGeeuoppY+7uzsee+wxHDlypLmmYRP9+/fHF198gQsXLgAAzpw5g8OHD2PYsGEA4NC11dbWwmQywcPDw6Jdp9Ph8OHDDl3b7RpSx1dffYWamhqLPoGBgejWrZtD1XonZ6qrvLwcGo0G7du3B2C/2prtZm07d+5EWVkZJk2aBAAoLi4GAKsbj/n7+6OgoKC5pmETs2fPRnl5Obp06QKtVguTyYTFixdj/PjxABy7Ni8vL/Tr1w9vv/02oqKi4O/vj23btuHLL79Ex44dHbq22zWkjuLiYri5ueGhhx6y6lO3vSNylrqqq6vx5ptvYsKECcqFPuxVW7O94kxOTsbTTz+NwMBAi/Y7L0EnIg26LJ09paWlYcuWLfjwww9x6tQpbNy4EStWrMDGjRst+jlibQCwefNmiAiCgoLg7u6O9957DxMmTIBWq1X6OGptd2pMHY5a6/04Ul01NTUYN24czGYz1qxZc9/+zV1bswRnQUEBsrKy8PLLLyttBoMBAKz+L/D999+3+tvfvvHGG3jzzTcxbtw4dO/eHRMnTsTvfvc7LFmyBIBj1wYAHTp0wKFDh3D9+nVcuXIFx48fR01NDSIiIhy+tjoNqcNgMODWrVsoLS2tt48jcvS6ampq8NxzzyE/Px/79++3uKycvWprluBMSUnBT3/6UwwfPlxpq3sS1p1pB374HPTQoUOIiYlpjmnYTFVVldVFTbVarfJ1JEeu7Xaenp4ICAhAaWkp9u7di1/+8pdOU1tD6nj00Ufh6upq0aeoqAj//Oc/HarWOzlyXXWhefHiRWRlZcHX19divd1qs/XZJpPJJKGhoTJ79myrdUuXLhW9Xi/p6ely9uxZGT9+vAQEBIjRaLT1NGzqpZdekqCgIMnIyJD8/HxJT08XPz8/+cMf/qD0cdTaREQyMzNlz5498s0338i+ffukZ8+e0qdPH7l165aIOE5tFRUVkpOTIzk5OQJA3nnnHcnJyVHOwDakjt/+9rcSHBwsWVlZcurUKXniiSekZ8+eUltba6+y7ltXSUmJ5OTkyO7duwWAbN++XXJycqSoqEgZozXWJXLv2mpqaiQhIUGCg4Pl9OnTUlRUpDxu3rypjGGP2mwenHv37hUAkpeXZ7XObDZLYmKiGAwGcXd3l4EDB8rZs2dtPQWbMxqN8tprr0loaKh4eHhIZGSkzJ071+I/nqPWJiKSlpYmkZGR4ubmJgaDQaZNmyZlZWXKekep7eDBgwLA6vHSSy+JSMPquHHjhrz66qvi4+MjOp1O4uPj5fLly3ao5kf3qyslJeWu6xMTE5UxWmNdIveure7rVXd7HDx4UBnDHrXxsnJERCrxt+pERCoxOImIVGJwEhGpxOAkIlKJwUlEpBKDk4hIJQYnEZFKDE4iIpUYnGQTf/vb36DRaFBWVqZqu9TUVOXaikSOgsFJqhw5cgRarRZDhw6191SI7IbBSaps2LAB06dPx+HDh3H58mV7T4fILhic1GCVlZX46KOPMGXKFMTHxyM1NdWqzz/+8Q/07NkTHh4e6Nu3L86ePWuxPjU1FaGhoWjbti1GjRqFkpISqzHef/99dOjQAW5ubujcuTM2b95ssV6j0eCDDz5AfHw82rZti6ioKBw9ehSXLl3CoEGD4OnpiX79+uHrr7+22G7Xrl149NFH4eHhgcjISCQlJaG2ttZi3PXr12PUqFFo27YtOnbsiM8++6wJR4ycVrNeQoScSnJysvTq1UtERHbt2iXh4eFiNptF5Mer3ERFRcm+ffskNzdX4uPjJTw8XLk83bFjx0Sj0ciSJUskLy9P/vKXv0j79u1Fr9cr+0hPTxdXV1dZvXq15OXlycqVK0Wr1cqBAweUPgAkKChI0tLSJC8vT0aOHCnh4eHyxBNPSGZmpvzrX/+SX/ziFzJ06FBlm8zMTPH29pbU1FT5+uuvZd++fRIeHi4LFiywGDc4OFg+/PBDuXjxosyYMUPatWsnJSUlzXlYyQExOKnBYmJi5M9//rOIiNTU1Iifn5/s379fRH4MztvvNlhSUiI6nU7S0tJERGT8+PEWYSYiMnbsWIvgjImJkVdeecWiz5gxY2TYsGHKMgCZN2+esnz06FEBIMnJyUrbtm3bxMPDQ1keMGCA/PGPf7QYd/PmzRIQEFDvuNevXxeNRiN79uy5z5GhBw3fqlOD5OXl4fjx4xg3bhwAoE2bNhg7diw2bNhg0a9fv37Kv/v4+KBz5844f/48AOD8+fMW6+/sX9cnNjbWoi02NlYZo06PHj2Uf6+7RUL37t0t2qqrq2E0GgH8cDfEhQsXol27dsrjlVdeQVFREaqqqu46rqenJ7y8vPD999/f69DQA6jZ7nJJziU5ORm1tbUICgpS2kQErq6uVvd7uVPdTbOkgZd+bcgN1VxdXa36362t7vYmZrMZSUlJGD16tNX+br818u1j1I1TNwZRHQYn3VdtbS02bdqElStXWty/GgCeeeYZbN26Fd26dQMAHDt2DKGhoQCA0tJSXLhwAV26dAEA/PznP8exY8cstr9zOSoqCocPH8aLL76otB05cgRRUVFNquGRRx5BXl4efvaznzVpHCKAwUkNkJGRgdLSUvz617+GXq+3WPfss88iOTkZ7777LgBg4cKF8PX1hb+/P+bOnQs/Pz+MHDkSADBjxgzExMRg+fLlGDlyJPbt24fMzEyL8d544w0899xzeOSRR/Dkk09i165dSE9PR1ZWVpNqeOuttxAfH4+QkBCMGTMGLi4uyM3NxdmzZ7Fo0aImjU0PIDt/xkoOID4+3uLkzO2++uorASArV64UALJr1y7p2rWruLm5Se/eveX06dMW/ZOTkyU4OFh0Op2MGDFCVqxYYXFySERkzZo1EhkZKa6urtKpUyfZtGmTxXoAsmPHDmW57t40OTk5SlvdyarS0lKlLTMzU2JiYkSn04m3t7f06dNH1q1bV++4IiJ6vV5SUlLue4zowcJ7DhERqcSz6kREKjE4iYhUYnASEanE4CQiUonBSUSkEoOTiEglBicRkUoMTiIilRicREQqMTiJiFRicBIRqfR/dYDlGaI8a8cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU4AAACgCAYAAABjaoUvAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAD7NJREFUeJzt3XtMU3f/B/B3i7XCBnXwSFsmIJihk4mamSmw4LZ4W4C5uHgh7veTeVmcxJlNF3VegAwzd3H/TMXoLg7BSzK3ZSZuZk6QTTEyjcGwBXFFMCKaeaFsAir9Pn88obGDFr6lcHpO36+ESM/18+HbvD2np+3RCSEEiIio1/RKF0BEpDYMTiIiSQxOIiJJDE4iIkkMTiIiSQxOIiJJDE4iIkkMTiIiSQxOIiJJDE7yGZ1O16ufsrIylJWVQafT4euvv+5xu9nZ2RgxYoRXNeXl5UGn0+Gvv/7yan2i7gxSugDSjoqKCpfH7733HkpLS3H8+HGX6WPGjMG5c+d6vd2NGzdi5cqVPqmRyBcYnOQzkydPdnk8bNgw6PX6LtNljRw5sk/rE/kaT9VJUffv38f69esRFRWFsLAwTJ06FTU1NS7LdHeqfufOHSxevBjh4eF49NFHkZ6eDpvNBp1Oh7y8vC77uX79OrKysmAymWA2m7Fo0SI0Nzf3Y2ekZQxOUtS7776L+vp6fPbZZ9i1axdqa2uRmZmJjo4Ot+s4HA5kZmZi3759WLNmDb799ltMmjQJM2fOdLvOK6+8goSEBBw6dAhr167Fvn378NZbb/VHSxQAeKpOihozZgyKi4udj4OCgjB37lxUVla6PcX/8ccf8euvv6KwsBDLli0DAEybNg2DBw/GunXrul1n8eLFeOeddwAAU6dOxaVLl/DFF1/g888/h06n83FXpHU84iRFvfTSSy6Pk5KSAAD19fVu1zlx4gQAYO7cuS7Ts7KypPbT1taGGzduSNVLBDA4SWEREREuj41GIwCgtbXV7To3b97EoEGDEB4e7jLdbDb7dD9E7jA4SXUiIiLw4MED3Lp1y2V6U1OTQhVRoGFwkupMmTIFAHDw4EGX6QcOHFCiHApAvDhEqjNz5kykpqZi1apVsNvtePrpp1FRUYGioiIAgF7P4wHqX3yGkero9XocPnwY8+fPx5YtWzBr1iz88ssvzqvzQ4cOVbZA0jwd73JJWrFv3z4sWLAAJ0+eREpKitLlkIYxOEmV9u/fj6tXr2Ls2LHQ6/U4ffo0PvroI0yYMMH5diWi/sLXOEmVQkNDceDAARQUFOCff/6B1WpFdnY2CgoKlC6NAgCPOImIJPHiEBGRJAYnEZEkBicRkSSvLw45HA40NjYiNDSU3y5DRJoghEBLSwuioqI8fpDC6+BsbGxEdHS0t6sTEfmtK1euYPjw4W7nex2coaGhzh2EhYV5uxkiIr9ht9sRHR3tzDd3vA7OztPzsLAwBicRaUpPLz/y4hARkSQGJxGRJAYnEZEkflZdpa5fv87b2/ag81bARL7G4FSh69ev49X/+3/cv9eudCl+zTDYiOK9RQxP8jkGpwo1Nzfj/r12tMZPgWOISbE69K13EFxXjta4NDiChypWR3f0bc2A7QSam5sZnORzDE4VcwwxwfHIf5QuA47goX5RB9FA4cUhIiJJDE4iIkkMTiIiSQxOIiJJDE4iIkkMTiIiSQxOIiJJDE4iIkkMTiIiSQxOIiJJDE4iIkkMTiIiSQMenG1tbbh48SLa2toGetdE5IfUmAkDHpwNDQ14/fXX0dDQMNC7JiI/pMZM4Kk6EZEkBicRkSQGJxGRJAYnEZEkBicRkSQGJxGRJAYnEZEkBicRkSQGJxGRJN5XnYg0paOjA1VVVbh16xbCw8ORlJSEoKAgn+6DwUlEmlFeXo4dO3agqanJOc1isWD58uVIS0vz2X54qk5EmlBeXo7c3FzEx8dj+/btOHLkCLZv3474+Hjk5uaivLzcZ/ticBKR6nV0dGDHjh1ITk5GQUEBEhMTERISgsTERBQUFCA5ORmFhYXo6Ojwyf56fare3t6O9vZ252O73d6nHdfX1/dp/UDGv13v8W/l/3wxRlVVVWhqasLGjRuh17seD+r1eixYsAA5OTmoqqrChAkT+ry/Xgfn+++/j/z8/D7vsNPmzZt9ti0id/g8Cwy3bt0CAMTFxXU7v3N653J91evgXLduHd5++23nY7vdjujoaK93vH79esTGxnq9fiCrr69nIPQSn2f+zxfP5/DwcABAXV0dEhMTu8yvq6tzWa6veh2cRqMRRqPRJzsFgNjYWCQkJPhse0Td4fMsMCQlJcFisaCkpAQFBQUup+sOhwMlJSWwWq1ISkryyf54cYiIVC8oKAjLly9HRUUFNmzYgOrqaty9exfV1dXYsGEDKioq8MYbb/js/Zx8HycRaUJaWhry8/OxY8cO5OTkOKdbrVbk5+f79H2cDE4i0oy0tDSkpqbyk0NERDKCgoJ88pYjT/gaJxGRJAYnEZEkBicRkSQGJxGRJAYnEZEkBicRkSQGJxGRJAYnEZEkBicRkSQGJxGRpAEPzpiYGOzatQsxMTEDvWsi8kNqzIQB/6z6kCFD+P2IROSkxkzgqToRkSQGJxGRJAYnEZEkBicRkSQGJxGRJAYnEZEkBicRkSQGJxGRJAYnEZEkBicRkSQGJxGRJN5XXcX0bc3K7r/1jsu//kTpvw1pG4NThUwmEwyDjYDthNKlAACC68qVLqFbhsFGmEwmpcsgDWJwqpDZbEbx3iI0N/OoyhOTyQSz2ax0GaRBDE6VMpvNDAUihfDiEBGRJAYnEZEkBicRkSQGJxGRJK8vDgkhAAB2u91nxRARKakzzzrzzR2vg7OlpQUAEB0d7e0miIj8UktLi8f3AOtET9HqhsPhQGNjI0JDQ6HT6bwu8N/sdjuio6Nx5coVhIWF+Wy7/or9alcg9Qpoo18hBFpaWhAVFQW93v0rmV4fcer1egwfPtzb1XsUFham2j++N9ivdgVSr4D6++3Np814cYiISBKDk4hIkt8Fp9FoRG5uLoxGo9KlDAj2q12B1CsQWP16fXGIiChQ+d0RJxGRv2NwEhFJYnASEUlicBIRSVIkOPPy8qDT6Vx+LBaLc74QAnl5eYiKikJwcDCee+45VFdXK1GqT/TUb3Z2dpf5kydPVrDivrt69SpeffVVREREICQkBOPHj8fZs2ed87U2xj31q5UxHjFiRJc+dDodcnJyAGhvXN1R7IgzMTER165dc/5cuHDBOe/DDz/EJ598gm3btqGyshIWiwXTpk1zfj5ejTz1CwAzZ850mX/kyBGFKu2727dvIzU1FQaDAT/88AN+//13bN26FUOHDnUuo6Ux7k2/gDbGuLKy0qWHn376CQAwZ84cANoaV4+EAnJzc8W4ceO6nedwOITFYhFbtmxxTmtraxMmk0ns3LlzgCr0LU/9CiHEwoULxaxZswasnv62Zs0a8eyzz7qdr7Ux7qlfIbQ3xp1WrlwpRo4cKRwOh+bG1RPFjjhra2sRFRWFuLg4zJ8/HzabDQBQV1eHpqYmTJ8+3bms0WjElClTcOrUKaXK7TN3/XYqKytDZGQkEhISsHTpUty4cUOhSvvu+++/x8SJEzFnzhxERkZiwoQJ2L17t3O+1sa4p347aWmMAeDevXsoLi7GokWLoNPpNDeunigSnJMmTUJRURGOHj2K3bt3o6mpCSkpKbh58yaampoAoMuNyMxms3Oe2njqFwBefPFFlJSU4Pjx49i6dSsqKyvxwgsvoL29XeHKvWOz2VBYWIgnnngCR48exbJly/Dmm2+iqKgIADQ3xj31C2hvjAHgu+++w507d5CdnQ1Ae+PqkdKHvEII8ffffwuz2Sy2bt0qTp48KQCIxsZGl2WWLFkiZsyYoVCFvvVwv91pbGwUBoNBHDp0aIAr8w2DwSCSk5Ndpq1YsUJMnjxZCCE0N8Y99dsdtY+xEEJMnz5dZGRkOB9rbVw98Yu3Iz3yyCMYO3YsamtrnVeb//0/1I0bNzRzO9yH++2O1WpFbGys2/n+zmq1YsyYMS7TnnzySTQ0NACA5sa4p37draPmMa6vr8exY8ewZMkS5zStjasnfhGc7e3t+OOPP2C1WhEXFweLxeK8Wgf877WUEydOICUlRcEqfefhfrtz8+ZNXLlyxe18f5eamoqamhqXaRcvXkRsbCwAaG6Me+q3O2of4y+//BKRkZFIT093TtPauHqkxGHuqlWrRFlZmbDZbOL06dMiIyNDhIaGisuXLwshhNiyZYswmUzim2++ERcuXBBZWVnCarUKu92uRLl95qnflpYWsWrVKnHq1ClRV1cnSktLRXJysnj88cdV2++ZM2fEoEGDxObNm0Vtba0oKSkRISEhori42LmMlsa4p361NsYdHR0iJiZGrFmzpss8LY2rJ4oE57x584TVahUGg0FERUWJ2bNni+rqaud8h8MhcnNzhcViEUajUaSlpYkLFy4oUapPeOr37t27Yvr06WLYsGHCYDCImJgYsXDhQtHQ0KBw1X1z+PBh8dRTTwmj0ShGjx4tdu3a5TJfa2PsqV+tjfHRo0cFAFFTU9NlntbG1R1+rRwRkSS/eI2TiEhNGJxERJIYnEREkhicRESSGJxERJIYnEREkhicRESSGJxERJIYnKSYy5cvQ6fT4fz5826X2bNnT5dvUu9JdnY2Xn755T7VRuQJg5P6RXf3pXn4p/M7HHsyb948XLx4sX+LJZI0SOkCSJuuXbvm/P3gwYPYtGmTyzcIBQcH4/bt2z1uJzg4GMHBwf1SI5G3eMRJ/cJisTh/TCaT886eD0/rZLPZ8PzzzyMkJATjxo1DRUWFc153p+oFBQWIjIxEaGgolixZgrVr12L8+PFdavj4449htVoRERGBnJwc3L9/v7/apQDD4CTFrV+/HqtXr8b58+eRkJCArKwsPHjwoNtlS0pKsHnzZnzwwQc4e/YsYmJiUFhY2GW50tJS/PnnnygtLcVXX32FPXv2YM+ePf3cCQUKBicpbvXq1UhPT0dCQgLy8/NRX1+PS5cudbvsp59+isWLF+O1115DQkICNm3ahLFjx3ZZ7rHHHsO2bdswevRoZGRkID09HT///HN/t0IBgsFJiktKSnL+3vmN6O7uAFlTU4NnnnnGZdq/HwP/u499UFCQy3bVfldJ8h8MTlKcwWBw/q7T6QAADofD7fKdy3Tq7itlH95m5zqetkkkg8FJqjJq1CicOXPGZdpvv/2mUDUUqPh2JFKVFStWYOnSpZg4cSJSUlJw8OBBVFVVIT4+XunSKIAwOElVFixYAJvNhtWrV6OtrQ1z585FdnZ2l6NQov7Eew6R6k2bNg0WiwV79+5VuhQKEDziJFW5e/cudu7ciRkzZiAoKAj79+/HsWPHXO7lTdTfeMRJqtLa2orMzEycO3cO7e3tGDVqFDZs2IDZs2crXRoFEAYnEZEkvh2JiEgSg5OISBKDk4hIEoOTiEgSg5OISBKDk4hIEoOTiEgSg5OISNJ/AQofS4bazzAfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CZGZmIicnxyVI/z2KXLduHQwGA9RqNfR6Pd59910AgMlkQk1NDbKzs51PHiVyFwYneYXJZILFYnG+tlgsMJlMMBqNzvb29naUlpbCbDYDAE6dOoXLly+jsLAQ33333X3rPHLkCD7++GPs2bMHV65cQX5+PsaOHQsAOHr0KKKjo7FhwwbnE0iJ3IX34ySvMJlMyM7ORmdnJ+x2O86dO4f09HTcuXMHubm5AICysjLY7XaYzWaUlJQgJCQEn376KQIDAx+4zuvXryMyMhIZGRkICAiAwWDAhAkTAMD5GAqtVuu8iziRu3DESV5hNpvR0tKC8vJylJSUICEhAcOGDYPRaER5eTlaWlpgtVphMBgwfPhwAMDYsWO7DE0AmDt3Lux2O4YPH45FixYhLy/P5QIUkacwOMkrRowYgejoaFgsFlgsFhiNRgB3nykTFxeH06dPw2KxYMqUKc5lQkJCul1nTEwMKisrsWPHDmg0GmRlZSE9PR0dHR0e3RciBid5jdlshtVqhdVqhclkcrYbjUacPHkSZWVlzvObvaXRaPDyyy8jNzcXVqsVpaWlqKioAAAEBgbizp077twFIgA8x0leZDabsWTJEnR0dDhHnMDd4HznnXfQ2toqFZwHDhzAnTt3MHHiRAQHB+PLL790PtsIuHsFvri4GK+++irUajWGDh3q9n0i38QRJ3mN2WyG3W7HiBEjXB7tajQa0dTUhPj4eMTExPR6fYMGDcK+ffuQmpqKcePG4dSpUzh+/DiGDBkCANiwYQOuXbuG+Ph4hIeHu31/yHfxmUNERJI44iQiksTgJCKSxOAkIpLE4CQiksTgJCKSxOAkIpLE4CQiksTgJCKSxOAkIpLE4CQiksTgJCKS9F9zMITvP9SD/AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in tr.select_dtypes('number').columns:\n",
    "    plt.figure(figsize=(4,1))\n",
    "    sns.boxplot(x=tr[col])\n",
    "    plt.title(col)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c8c223c-da77-4dce-a337-e14d7eb4b38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.boxplot(x=train['holiday']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1673d9d0-b34f-46a6-9f68-3e55516259d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.boxplot(x=train['holiday']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6268ba52-5b7b-43b5-a1b2-93fe7e308926",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.boxplot(x=train['holiday']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d2c4919-6a64-4223-8172-c1d437eac206",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.boxplot(x=train['holiday']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962af3b1-94e3-48b5-a981-c7cea3dd1f49",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "598368c1-7508-4e22-bbe8-a5bddaf598ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "tr['Waist_hip']=tr['Abdomen'] / tr['Hip']\n",
    "te['Waist_hip']=te['Abdomen'] / te['Hip']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7041ee8-07bb-40c4-bd44-03cf53b7f5ef",
   "metadata": {},
   "source": [
    "This feature captures fat distribution by comparing waist size to hip size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13cbc48d-417f-4a9b-9de5-6e0cecf3c877",
   "metadata": {},
   "outputs": [],
   "source": [
    "tr['Body_Index']=703 * tr['Weight'] / (tr['Height'] ** 2)\n",
    "te['Body_Index']=703 * te['Weight'] / (te['Height'] ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7634c184-6df4-499b-a20a-06c94a579f21",
   "metadata": {},
   "source": [
    "Weight and height measurements were identified as being recorded in pounds and inches, and Body Mass Index (BMI) was therefore calculated using the standard conversion factor (703 × weight / height²)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1b60624-b860-4843-8db8-6d1b58b0a4bd",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "557a5bcb-0307-45b8-b542-98c7b6f9483c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=tr.drop(['Density','BodyFat','Id'],axis=1)\n",
    "y=tr['Density']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6f27d1d2-6152-44cb-b789-308c72b54712",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_val,y_train,y_val=train_test_split(x,y,test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bef408b-d407-4f45-b78a-14e4fd72b6cf",
   "metadata": {},
   "source": [
    "### 1th LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f43c9679-e455-4ae7-a763-9c73eb45bf3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "87f69413-7bb5-4262-912d-d122ea11db71",
   "metadata": {},
   "outputs": [
    {
     "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",
       "  display: none;\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",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\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",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy_X&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "792e6411-37f0-4843-9a1d-85cd55a4f741",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin=lr.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "aa2ea506-ad0c-4e0b-96db-0e42a731eb32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6788087153347366"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_val,tahmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "909ea64e-8a19-474d-a918-d540d4aff614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010029021125474751"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_val,tahmin)**.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8ce0cc40-76fa-4ce5-aa94-f60f3c6fbcbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option(\"display.max_columns\",100)\n",
    "\n",
    "from sklearn.linear_model import LinearRegression,SGDRegressor,Ridge,Lasso,ElasticNet\n",
    "from sklearn.neighbors import KNeighborsRegressor, RadiusNeighborsRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor,AdaBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor, plot_tree, ExtraTreeRegressor\n",
    "#pip install xgboost\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error,r2_score,mean_absolute_error\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "def algo_test(x,y):\n",
    "        #Bütün modelleri tanımlıyorum\n",
    "        L=LinearRegression()\n",
    "        R=Ridge()\n",
    "        Lass=Lasso()\n",
    "        E=ElasticNet()\n",
    "        sgd=SGDRegressor()\n",
    "        ETR=ExtraTreeRegressor()\n",
    "        GBR=GradientBoostingRegressor()\n",
    "        kn=KNeighborsRegressor()\n",
    "        rkn=RadiusNeighborsRegressor(radius=1.0)\n",
    "        ada=AdaBoostRegressor()\n",
    "        dt=DecisionTreeRegressor()\n",
    "        xgb=XGBRegressor()\n",
    "        svr=SVR()\n",
    "        mlp_regressor = MLPRegressor()\n",
    "\n",
    "       \n",
    "        \n",
    "        algos=[L,R,Lass,E,sgd,ETR,GBR,ada,kn,dt,xgb,svr,mlp_regressor]\n",
    "        algo_names=['Linear','Ridge','Lasso','ElasticNet','SGD','Extra Tree','Gradient Boosting',\n",
    "                    'KNeighborsRegressor','AdaBoost','Decision Tree','XGBRegressor','SVR','mlp_regressor']\n",
    "        x=MinMaxScaler().fit_transform(x)\n",
    "        x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=.20,random_state=42)\n",
    "        \n",
    "        r_squared= []\n",
    "        rmse= []\n",
    "        mae= []\n",
    "        \n",
    "        #Hata ve doğruluk oranlarını bir tablo haline getirmek için bir dataframe oluşturuyorum\n",
    "        result=pd.DataFrame(columns=['R_Squared','RMSE','MAE'],index=algo_names)\n",
    "        \n",
    "        \n",
    "        for algo in algos:\n",
    "            p=algo.fit(x_train,y_train).predict(x_test)\n",
    "            r_squared.append(r2_score(y_test,p))\n",
    "            rmse.append(mean_squared_error(y_test,p)**.5)\n",
    "            mae.append(mean_absolute_error(y_test,p))\n",
    "        \n",
    "            \n",
    "\n",
    "        #result adlı tabloya doğruluk ve hata oranlarımı yerleştiriyorum\n",
    "        result.R_Squared=r_squared\n",
    "        result.RMSE=rmse\n",
    "        result.MAE=mae\n",
    "        \n",
    "       #oluşturduğum result tablosunu doğruluk oranına (r2_score) göre sıralayıp dönüyor\n",
    "        rtable=result.sort_values('R_Squared',ascending=False)\n",
    "        return rtable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "14513f2b-caa2-45a5-ade2-91b4a2869143",
   "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>R_Squared</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>0.699385</td>\n",
       "      <td>0.009702</td>\n",
       "      <td>0.007925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Linear</th>\n",
       "      <td>0.678809</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.007917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoost</th>\n",
       "      <td>0.626732</td>\n",
       "      <td>0.010812</td>\n",
       "      <td>0.009194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting</th>\n",
       "      <td>0.598033</td>\n",
       "      <td>0.011219</td>\n",
       "      <td>0.009126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsRegressor</th>\n",
       "      <td>0.583284</td>\n",
       "      <td>0.011423</td>\n",
       "      <td>0.009383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBRegressor</th>\n",
       "      <td>0.421059</td>\n",
       "      <td>0.013465</td>\n",
       "      <td>0.010471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extra Tree</th>\n",
       "      <td>0.111568</td>\n",
       "      <td>0.016680</td>\n",
       "      <td>0.012841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.023294</td>\n",
       "      <td>0.017489</td>\n",
       "      <td>0.013938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>-0.067556</td>\n",
       "      <td>0.018284</td>\n",
       "      <td>0.015853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElasticNet</th>\n",
       "      <td>-0.067556</td>\n",
       "      <td>0.018284</td>\n",
       "      <td>0.015853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR</th>\n",
       "      <td>-0.785655</td>\n",
       "      <td>0.023647</td>\n",
       "      <td>0.019821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGD</th>\n",
       "      <td>-76.930382</td>\n",
       "      <td>0.156218</td>\n",
       "      <td>0.126189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp_regressor</th>\n",
       "      <td>-137.410740</td>\n",
       "      <td>0.208191</td>\n",
       "      <td>0.165875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      R_Squared      RMSE       MAE\n",
       "Ridge                  0.699385  0.009702  0.007925\n",
       "Linear                 0.678809  0.010029  0.007917\n",
       "AdaBoost               0.626732  0.010812  0.009194\n",
       "Gradient Boosting      0.598033  0.011219  0.009126\n",
       "KNeighborsRegressor    0.583284  0.011423  0.009383\n",
       "XGBRegressor           0.421059  0.013465  0.010471\n",
       "Extra Tree             0.111568  0.016680  0.012841\n",
       "Decision Tree          0.023294  0.017489  0.013938\n",
       "Lasso                 -0.067556  0.018284  0.015853\n",
       "ElasticNet            -0.067556  0.018284  0.015853\n",
       "SVR                   -0.785655  0.023647  0.019821\n",
       "SGD                  -76.930382  0.156218  0.126189\n",
       "mlp_regressor       -137.410740  0.208191  0.165875"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo_test(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26cfa99c-b53f-4946-b865-614c4edab836",
   "metadata": {},
   "source": [
    "### 2th RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "4bc09963-8f01-424a-b308-d62c6c0e28c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d7114336-5036-4d59-bd86-1a19508f2e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\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-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8 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-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 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-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">criterion&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_features&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('bootstrap',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('oob_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">oob_score&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_samples&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c60bd9e3-c4ea-4cde-ba4c-8be28986baef",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin3=rf.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "cd0ac918-b6f0-4cfa-a137-436b8d717742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6207558611982726"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_val,tahmin3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d58b269a-e0fd-4a83-b7bc-33b269ec8ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010897731629861148"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_val,tahmin3)**.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbf7e10-346d-47a8-890d-bcdc3fcd89e8",
   "metadata": {},
   "source": [
    "### 3th XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "070c4109-37b8-405a-b46f-cf6f5383e7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "xg=XGBRegressor(n_estimators=300,learning_rate=0.05,max_depth=4,subsample=0.8,colsample_bytree=0.8,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "42695895-356b-4c71-bbcc-a8f6fb09b8b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\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-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7 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-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 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-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-7\" 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=0.8, 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=0.05, max_bin=None, max_cat_threshold=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=4,\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_3.1.0/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\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('objective',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">objective&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;reg:squarederror&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('base_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">base_score&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('booster',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">booster&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('callbacks',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">callbacks&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bylevel',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">colsample_bylevel&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bynode',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">colsample_bynode&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
       "            <td class=\"value\">0.8</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('device',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">device&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('early_stopping_rounds',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">early_stopping_rounds&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('enable_categorical',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">enable_categorical&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('eval_metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">eval_metric&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_types',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">feature_types&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">feature_weights&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('gamma',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">gamma&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('grow_policy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">grow_policy&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('importance_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">importance_type&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('interaction_constraints',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">interaction_constraints&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">0.05</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_bin',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_bin&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_cat_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_cat_threshold&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_cat_to_onehot',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_cat_to_onehot&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_delta_step',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_delta_step&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">4</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaves',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_leaves&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('missing',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">missing&nbsp;</td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotone_constraints',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">monotone_constraints&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('multi_strategy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">multi_strategy&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">300</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('num_parallel_tree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">num_parallel_tree&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_lambda',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sampling_method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">sampling_method&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scale_pos_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">scale_pos_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample&nbsp;</td>\n",
       "            <td class=\"value\">0.8</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tree_method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tree_method&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validate_parameters',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">validate_parameters&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbosity',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbosity&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.8, 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=0.05, max_bin=None, max_cat_threshold=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=4,\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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bb0416c5-fe3f-4465-af9d-59eed1a1d94b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin4=xg.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d61060f2-bc14-4137-9161-4318958f20aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5688693856515825"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_val,tahmin4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8295e542-67ee-4921-86a2-53550819fca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.011619330404760113"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_val,tahmin4)**.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1580ff60-62cb-46d7-b3fc-3e3d0e24f0cb",
   "metadata": {},
   "source": [
    "### 4th Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "5bce36aa-c417-4d3e-9562-f37cee085303",
   "metadata": {},
   "outputs": [],
   "source": [
    "r=Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f9f50e9f-164c-4bdb-b1f1-503e96a1fae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {\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-12 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12 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-12 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 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-12 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-12 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-12 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-12 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-12 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-12 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-12 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-12 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-12 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 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-12 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-12 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-12 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-12 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge()</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-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Ridge</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">alpha&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy_X&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">solver&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "2d7418e5-2fbf-4305-a07f-5e8b6d6817da",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin2=r.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "2403c67d-7242-40e3-8a68-ca1651e56cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6840092546225316"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_val,tahmin2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "c6680565-c100-4458-b161-57542f76c72e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009947497795587533"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_val,tahmin2)**.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "bf972196-15b4-4996-8b7d-285d233c3cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ridge_model.pkl']"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(r,'ridge_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "50407de7-33b4-4271-aba1-b97a36032bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-9.46207927e-05,  1.81063454e-04,  3.67335778e-04,  7.73223913e-04,\n",
       "       -1.66092293e-05, -1.83902824e-03, -4.61274362e-04, -3.15116556e-04,\n",
       "        6.91272782e-04, -5.47278257e-04, -9.68074017e-05, -5.99023315e-04,\n",
       "        3.85908913e-03, -1.21704841e-04,  7.37112343e-06])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "be099f93-117b-4fb2-9543-f6b74dcaa18b",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance=pd.DataFrame({'Feature':x_train.columns, 'Coefs':r.coef_[0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4286d824-6e82-442c-a886-d1915ff4ddcc",
   "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>Feature</th>\n",
       "      <th>Coefs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Weight</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Height</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Neck</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chest</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abdomen</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hip</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thigh</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Knee</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ankle</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Biceps</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Forearm</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Wrist</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Waist_hip</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Body_Index</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Feature     Coefs\n",
       "0          Age -0.000095\n",
       "1       Weight -0.000095\n",
       "2       Height -0.000095\n",
       "3         Neck -0.000095\n",
       "4        Chest -0.000095\n",
       "5      Abdomen -0.000095\n",
       "6          Hip -0.000095\n",
       "7        Thigh -0.000095\n",
       "8         Knee -0.000095\n",
       "9        Ankle -0.000095\n",
       "10      Biceps -0.000095\n",
       "11     Forearm -0.000095\n",
       "12       Wrist -0.000095\n",
       "13   Waist_hip -0.000095\n",
       "14  Body_Index -0.000095"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "dcba77af-293a-4ddb-a2f9-6dbfc8fd6a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "feature_importance.plot(kind='barh')\n",
    "plt.title('Feature Importance (Ridge)')\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ef0b2730-fb70-4f25-8301-6b16a51c6c34",
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals=y_val-tahmin2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "5c2a3348-4e45-466f-8638-becdec77c04b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137    0.023754\n",
       "30     0.014922\n",
       "119    0.001336\n",
       "29     0.005851\n",
       "142   -0.002293\n",
       "161   -0.010416\n",
       "164   -0.003306\n",
       "51     0.012052\n",
       "105    0.000326\n",
       "60     0.016469\n",
       "15    -0.013330\n",
       "156    0.001883\n",
       "133    0.001322\n",
       "45     0.009487\n",
       "68    -0.001182\n",
       "85     0.002630\n",
       "24     0.010170\n",
       "109    0.016834\n",
       "75    -0.002141\n",
       "108   -0.005740\n",
       "19     0.010346\n",
       "16    -0.009000\n",
       "31     0.014602\n",
       "18     0.001570\n",
       "12    -0.005695\n",
       "9     -0.002339\n",
       "82     0.008815\n",
       "98     0.003348\n",
       "76    -0.015605\n",
       "55    -0.015191\n",
       "42    -0.014007\n",
       "56    -0.001662\n",
       "148   -0.001595\n",
       "111    0.009391\n",
       "Name: Density, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e962b8f-d438-4900-a3f5-7b6785c6d308",
   "metadata": {},
   "source": [
    "The residuals are small and centered around zero, showing that the model predictions are accurate and unbiased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "3648cb9b-e1ed-463f-9a55-1011c1c6c002",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub=pd.read_csv('submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "aaa53297-b49b-4e17-88fe-9a96944af625",
   "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>Id</th>\n",
       "      <th>BodyFat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Person68</td>\n",
       "      <td>17.476335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Person252</td>\n",
       "      <td>27.606467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Person232</td>\n",
       "      <td>21.851720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Person162</td>\n",
       "      <td>18.921831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Person92</td>\n",
       "      <td>18.284241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id    BodyFat\n",
       "0   Person68  17.476335\n",
       "1  Person252  27.606467\n",
       "2  Person232  21.851720\n",
       "3  Person162  18.921831\n",
       "4   Person92  18.284241"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77b77391-3ec1-43d9-9d24-6153f9f307c5",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "94102b0e-71da-4238-aeee-d8b4b2c0bc27",
   "metadata": {},
   "outputs": [],
   "source": [
    "testids=te['Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "77e63f51-17fe-4545-a604-4a51b8ed99b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=te.drop(['Id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "3f804ab1-0cb9-4372-8d28-cffd79c1acb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((168, 15), (84, 15))"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "343a8760-eb34-4a94-9f34-14a913a8e5ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns.equals(x_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f85fcd41-c7f5-4c97-8312-e8aea12ea8d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {\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-11 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11 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-11 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 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-11 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-11 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-11 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-11 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-11 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-11 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-11 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-11 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-11 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 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-11 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-11 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-11 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-11 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
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       "</style><body><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge()</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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Ridge</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
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       "                <summary>Parameters</summary>\n",
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       "                    \n",
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       "    \n",
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       "            <td class=\"value\">True</td>\n",
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       "    \n",
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       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
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       "    \n",
       "\n",
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       "            <td class=\"param\">solver&nbsp;</td>\n",
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       "    \n",
       "\n",
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       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
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       "    \n",
       "\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
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      ],
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "e61d2094-932d-48aa-a969-7b42cd44da29",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin5=r.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "9e8704c8-80d6-4e9d-b916-0e2ea239e03b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bodyfat_pred=495 / tahmin5 - 450"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d85cd55-c9b3-4230-b7e4-e186c35c430e",
   "metadata": {},
   "source": [
    "Predicted body fat density values were converted into body fat percentage using the standard Siri equation (BodyFat = 495 / Density − 450)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "ac2d8154-be09-4a20-b3c3-c4c300554487",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission=pd.DataFrame({'Id': testids,'BodyFat': bodyfat_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "397334e7-3d30-453f-82fe-31dd8b8c90a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "fbe0beab-64ba-4f81-baf5-fe596cb141b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>BodyFat</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>14.976977</td>\n",
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       "      <th>4</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          Id    BodyFat\n",
       "0   Person68  14.976977\n",
       "1  Person252  26.096265\n",
       "2  Person232  20.670358\n",
       "3  Person162  17.837957\n",
       "4   Person92  17.797319"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7a666f-0846-49c1-baff-7b970ea18c72",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "Different regression models were trained and evaluated using RMSE as the main metric.\n",
    "Ridge Regression performed best, achieving an RMSE of around 0.01 and explaining approximately 68% of the variance in body fat density.\n",
    "This shows that the model provides accurate and stable predictions, and the final outputs were converted to body fat percentage using the Siri formula for competition submission."
   ]
  }
 ],
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