Boston_House.ipynb

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    "colab": {
      "name": "Boston_House.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "hTW9aYRS9ol3"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn.datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn import metrics\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QuZN9P4XWImP"
      },
      "source": [
        "Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNgtpBfa-RDL"
      },
      "source": [
        "dataset = sklearn.datasets.load_boston()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBYIf7if-bQJ"
      },
      "source": [
        "house_price_df = pd.DataFrame(dataset.data, columns = dataset.feature_names)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "rGvVI_na-cgB",
        "outputId": "b1c636e0-a595-4e37-aa50-026335e2ab55"
      },
      "source": [
        "house_price_df.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX  ...  RAD    TAX  PTRATIO       B  LSTAT\n",
              "0  0.00632  18.0   2.31   0.0  0.538  ...  1.0  296.0     15.3  396.90   4.98\n",
              "1  0.02731   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  396.90   9.14\n",
              "2  0.02729   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  392.83   4.03\n",
              "3  0.03237   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  394.63   2.94\n",
              "4  0.06905   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  396.90   5.33\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUsGrY_SXJME"
      },
      "source": [
        "house_price_df['price'] = dataset.target"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "BFihZD_CXujS",
        "outputId": "202739cb-f1a5-41f9-a240-e83ea6186105"
      },
      "source": [
        "house_price_df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX  ...    TAX  PTRATIO       B  LSTAT  price\n",
              "0  0.00632  18.0   2.31   0.0  0.538  ...  296.0     15.3  396.90   4.98   24.0\n",
              "1  0.02731   0.0   7.07   0.0  0.469  ...  242.0     17.8  396.90   9.14   21.6\n",
              "2  0.02729   0.0   7.07   0.0  0.469  ...  242.0     17.8  392.83   4.03   34.7\n",
              "3  0.03237   0.0   2.18   0.0  0.458  ...  222.0     18.7  394.63   2.94   33.4\n",
              "4  0.06905   0.0   2.18   0.0  0.458  ...  222.0     18.7  396.90   5.33   36.2\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
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      "metadata": {
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        },
        "id": "9tREYyKTXwHq",
        "outputId": "267efa95-0709-4f93-915d-31a8fd8cc94b"
      },
      "source": [
        "house_price_df.isna().sum()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM       0\n",
              "ZN         0\n",
              "INDUS      0\n",
              "CHAS       0\n",
              "NOX        0\n",
              "RM         0\n",
              "AGE        0\n",
              "DIS        0\n",
              "RAD        0\n",
              "TAX        0\n",
              "PTRATIO    0\n",
              "B          0\n",
              "LSTAT      0\n",
              "price      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
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      "metadata": {
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          "height": 317
        },
        "id": "eMlwXAAXYA0j",
        "outputId": "55b5e3c7-50f2-411d-fb47-b200380f1ff8"
      },
      "source": [
        "house_price_df.describe()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.069170</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>22.532806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.601545</td>\n",
              "      <td>23.322453</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.253994</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>9.197104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.082045</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>17.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.677083</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             CRIM          ZN       INDUS  ...           B       LSTAT       price\n",
              "count  506.000000  506.000000  506.000000  ...  506.000000  506.000000  506.000000\n",
              "mean     3.613524   11.363636   11.136779  ...  356.674032   12.653063   22.532806\n",
              "std      8.601545   23.322453    6.860353  ...   91.294864    7.141062    9.197104\n",
              "min      0.006320    0.000000    0.460000  ...    0.320000    1.730000    5.000000\n",
              "25%      0.082045    0.000000    5.190000  ...  375.377500    6.950000   17.025000\n",
              "50%      0.256510    0.000000    9.690000  ...  391.440000   11.360000   21.200000\n",
              "75%      3.677083   12.500000   18.100000  ...  396.225000   16.955000   25.000000\n",
              "max     88.976200  100.000000   27.740000  ...  396.900000   37.970000   50.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fv6S6F38Yi0p"
      },
      "source": [
        "Understanding the correlation between the various features of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58byu6bxYY3V"
      },
      "source": [
        "corln = house_price_df.corr()"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ur2UKZGGZI8k"
      },
      "source": [
        "Heat Map"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "J03hLPvBZEd7",
        "outputId": "c9672e95-6550-430b-e2a0-d2bb6f3e62f6"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(corln, cbar=True, square=True, fmt='.2f', annot=True, annot_kws={'size':8}, cmap='Blues')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f217058bc90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GbojcMFda4h1"
      },
      "source": [
        "Splitting the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "70sda-qCa1gZ"
      },
      "source": [
        "X = house_price_df.drop('price', axis=1)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fj9_0eEybBHp"
      },
      "source": [
        "Y = house_price_df['price']"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "--ceHoXobFuQ",
        "outputId": "53957d2e-ee96-4333-d9ef-60f3eb61f73a"
      },
      "source": [
        "X"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>0.06263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.593</td>\n",
              "      <td>69.1</td>\n",
              "      <td>2.4786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>391.99</td>\n",
              "      <td>9.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>0.04527</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.120</td>\n",
              "      <td>76.7</td>\n",
              "      <td>2.2875</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>0.06076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.976</td>\n",
              "      <td>91.0</td>\n",
              "      <td>2.1675</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>0.10959</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.794</td>\n",
              "      <td>89.3</td>\n",
              "      <td>2.3889</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>393.45</td>\n",
              "      <td>6.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>505</th>\n",
              "      <td>0.04741</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.030</td>\n",
              "      <td>80.8</td>\n",
              "      <td>2.5050</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>7.88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        CRIM    ZN  INDUS  CHAS    NOX  ...  RAD    TAX  PTRATIO       B  LSTAT\n",
              "0    0.00632  18.0   2.31   0.0  0.538  ...  1.0  296.0     15.3  396.90   4.98\n",
              "1    0.02731   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  396.90   9.14\n",
              "2    0.02729   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  392.83   4.03\n",
              "3    0.03237   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  394.63   2.94\n",
              "4    0.06905   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  396.90   5.33\n",
              "..       ...   ...    ...   ...    ...  ...  ...    ...      ...     ...    ...\n",
              "501  0.06263   0.0  11.93   0.0  0.573  ...  1.0  273.0     21.0  391.99   9.67\n",
              "502  0.04527   0.0  11.93   0.0  0.573  ...  1.0  273.0     21.0  396.90   9.08\n",
              "503  0.06076   0.0  11.93   0.0  0.573  ...  1.0  273.0     21.0  396.90   5.64\n",
              "504  0.10959   0.0  11.93   0.0  0.573  ...  1.0  273.0     21.0  393.45   6.48\n",
              "505  0.04741   0.0  11.93   0.0  0.573  ...  1.0  273.0     21.0  396.90   7.88\n",
              "\n",
              "[506 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23MY2HS4bHTZ",
        "outputId": "52b95324-64f2-4886-bd7d-39ee5898a0ea"
      },
      "source": [
        "Y"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      24.0\n",
              "1      21.6\n",
              "2      34.7\n",
              "3      33.4\n",
              "4      36.2\n",
              "       ... \n",
              "501    22.4\n",
              "502    20.6\n",
              "503    23.9\n",
              "504    22.0\n",
              "505    11.9\n",
              "Name: price, Length: 506, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjnN24QtbJjJ"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2, random_state=2)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCRoqmu4c3Ci",
        "outputId": "0830693a-ff31-4c5a-963f-ac00a8a6a4e1"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(404, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T4F2I9REc-na",
        "outputId": "ffea4087-9bfe-44ba-d99c-97ea74a2a1d8"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(102, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvrH_a1NdVNP"
      },
      "source": [
        "Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OsJ78s5Mdbzx"
      },
      "source": [
        "#XGBRegressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlDh90xvdAPb"
      },
      "source": [
        "#loading the model\n",
        "model = XGBRegressor()"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsjSkxzSdaFL",
        "outputId": "396d4951-494b-4ad7-f527-d800c87efa4c"
      },
      "source": [
        "model.fit(X_train,Y_train)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[00:27:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
              "             max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
              "             n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
              "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "             silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5h7Ih0meW6n"
      },
      "source": [
        "Model Prediction on training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUW-A7h6d-2k"
      },
      "source": [
        "# Accuracy for the prediction on training value\n",
        "training_data_prediction = model.predict(X_train)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-DApRJQeIL1"
      },
      "source": [
        "#MAE\n",
        "score_1 = metrics.mean_absolute_error(training_data_prediction,Y_train)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lJiKi9U8esQl",
        "outputId": "30678107-4da2-46b5-ef6b-d125b1055549"
      },
      "source": [
        "score_1"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.145314053261634"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmKboV6_fGIa"
      },
      "source": [
        "#R square error\n",
        "score_2 = metrics.r2_score(training_data_prediction,Y_train)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6w5NA6aEfR6H",
        "outputId": "a1ac0b52-3672-4dd4-a725-b620469953a2"
      },
      "source": [
        "score_2"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9711526796929283"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QaqoT1FDhMWK"
      },
      "source": [
        "#TEST DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ak8O3LVJfZAE"
      },
      "source": [
        "testing_data_prediction = model.predict(X_test)\n",
        "score_3 = metrics.mean_absolute_error(testing_data_prediction,Y_test)\n",
        "score_4 = metrics.r2_score(testing_data_prediction,Y_test)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "foXHbTG6gwoj",
        "outputId": "41f927ae-4d88-4e45-b107-80741f441881"
      },
      "source": [
        "score_3"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.9922956859364223"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GzC6Bwutgxdi",
        "outputId": "3e01466a-b680-4d58-db04-86622170d57e"
      },
      "source": [
        "score_4"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9028193795196178"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BfwymZmmhQSa"
      },
      "source": [
        "#VISUALIZING THE DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "OValrqU8g493",
        "outputId": "1208d123-afeb-4fba-d15c-078f744d317a"
      },
      "source": [
        "plt.scatter(Y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\"Actual vs predicted price\")\n",
        "plt.show()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1qQQkfrh66G"
      },
      "source": [
        "#RANDOM FOREST METHOD"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRYMA8jmhhGg"
      },
      "source": [
        "model2 = RandomForestRegressor()"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TXKcPqGwiF6D",
        "outputId": "b8d0a675-acba-4e6e-89b2-7d4292e33221"
      },
      "source": [
        "model2.fit(X_train, Y_train)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JSukxrxiof6"
      },
      "source": [
        "model2_training_res = model2.predict(X_train)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCj163PzixCe"
      },
      "source": [
        "#r square\n",
        "score_rf = metrics.r2_score(model2_training_res, Y_train)\n",
        "\n",
        "#mae score\n",
        "score_rf_mae = metrics.mean_absolute_error(model2_training_res, Y_train)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4JB-D-Kix13",
        "outputId": "c3571c78-2713-4b76-c5ed-3f0e1355b1f0"
      },
      "source": [
        "score_rf"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9801744614398685"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SlgYl89BjHze",
        "outputId": "90abd046-754b-42b8-8cc5-92b4d9b49ced"
      },
      "source": [
        "score_rf_mae"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8301930693069299"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0y9c5WcjJDd"
      },
      "source": [
        "model2_test_res = model2.predict(X_test)\n",
        "#r square\n",
        "score_rf_test = metrics.r2_score(model2_test_res, Y_test)\n",
        "\n",
        "#mae score\n",
        "score_rf_mae_test = metrics.mean_absolute_error(model2_test_res, Y_test)"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXoAjga2jYUe",
        "outputId": "90e33da7-f74d-4f00-a71a-fdeda1989120"
      },
      "source": [
        "score_rf_test"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8815845549556273"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gAJ1ZAXRjZee",
        "outputId": "514bbe4f-c248-4597-8556-73fb5c55afa1"
      },
      "source": [
        "score_rf_mae_test"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.067196078431371"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "8nSUPhyEjcOd",
        "outputId": "116161e2-d258-483d-86a7-fbc12408f9c7"
      },
      "source": [
        "plt.scatter(Y_train, model2_training_res)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\"Actual vs predicted price\")\n",
        "plt.show()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81e65av-jk6a",
        "outputId": "54d71ee8-58bf-4882-a278-1274fce029c9"
      },
      "source": [
        "training_data_prediction"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([23.360205 , 22.462858 , 20.84804  , 33.77895  , 15.333282 ,\n",
              "       13.616525 , 21.71274  , 15.175322 , 11.724756 , 21.836252 ,\n",
              "       16.08508  ,  7.52517  , 31.094206 , 48.56228  , 32.623158 ,\n",
              "       20.546066 , 22.177324 , 20.500404 , 31.666502 , 20.551508 ,\n",
              "       25.74269  ,  8.247894 , 45.200817 , 22.069397 , 20.698004 ,\n",
              "       20.100042 , 19.873472 , 26.242834 , 23.39618  , 31.927258 ,\n",
              "       21.493471 ,  9.280926 , 18.504272 , 21.87202  , 12.504413 ,\n",
              "       10.578829 , 13.054951 , 23.541336 , 19.164755 , 15.888303 ,\n",
              "       23.768887 , 28.454714 , 15.539753 , 18.049202 , 16.23671  ,\n",
              "       14.08383  , 25.33273  , 17.575668 , 49.566467 , 16.990675 ,\n",
              "       21.738977 , 32.935143 , 16.125738 , 22.45393  , 20.776966 ,\n",
              "       20.042227 , 22.898897 , 38.124043 , 30.607079 , 32.607468 ,\n",
              "       20.919416 , 47.348038 , 14.524615 ,  8.126455 , 19.581661 ,\n",
              "        9.030508 , 26.462107 , 17.69918  , 20.546162 , 46.312218 ,\n",
              "       39.689137 , 34.387108 , 22.11083  , 34.568977 , 24.873934 ,\n",
              "       50.078335 , 14.5669775, 20.525211 , 20.62971  , 23.202105 ,\n",
              "       49.514477 , 23.12061  , 24.795782 , 20.319666 , 43.869396 ,\n",
              "       17.110266 , 32.165016 , 34.75202  ,  7.313497 , 20.309446 ,\n",
              "       18.038298 , 12.008462 , 24.216425 , 47.90671  , 37.94349  ,\n",
              "       20.759708 , 40.182804 , 18.249052 , 15.611586 , 26.39461  ,\n",
              "       21.0571   , 20.421682 , 18.377089 , 17.338768 , 21.223648 ,\n",
              "       22.653662 , 17.560051 , 32.635715 , 16.683764 , 13.004857 ,\n",
              "       18.488163 , 20.659714 , 16.501846 , 20.648884 , 48.62411  ,\n",
              "       15.977999 , 15.97522  , 18.581459 , 14.893438 , 32.871964 ,\n",
              "       14.236945 , 43.612328 , 33.881115 , 19.073408 , 15.747335 ,\n",
              "        9.4903965, 10.153891 , 14.812717 , 18.655546 ,  8.596755 ,\n",
              "       22.666656 , 10.941623 , 20.534616 , 49.324417 , 22.710459 ,\n",
              "       19.99658  , 31.663935 , 21.78586  , 30.9277   , 30.507492 ,\n",
              "       15.054665 , 15.854853 , 48.532074 , 21.108742 , 15.687305 ,\n",
              "       12.403721 , 49.90245  , 31.557863 , 11.709707 , 20.22495  ,\n",
              "       26.214525 , 32.90807  , 22.90362  ,  9.542897 , 24.487959 ,\n",
              "       24.46598  , 22.509142 , 14.704502 , 27.895067 , 33.619015 ,\n",
              "       14.888735 , 19.147383 , 26.40218  , 32.77208  , 29.293688 ,\n",
              "       23.638102 , 10.448805 , 22.518728 , 21.47825  , 35.32415  ,\n",
              "       23.002241 , 20.470022 , 18.918747 , 10.328174 , 22.244467 ,\n",
              "       17.69918  , 20.918488 , 11.913417 , 42.572548 , 46.803394 ,\n",
              "       14.652036 , 20.633188 , 23.285368 , 15.295161 , 20.861048 ,\n",
              "       23.587011 , 32.94382  , 21.090906 , 24.898489 , 18.465925 ,\n",
              "       31.454802 , 14.421506 , 15.421497 , 21.890705 , 23.64799  ,\n",
              "       17.40471  , 26.111868 , 24.977922 , 27.56308  , 22.964123 ,\n",
              "       18.823803 , 28.856464 , 14.080684 , 19.785515 , 17.007908 ,\n",
              "       42.90537  , 26.354216 , 21.719929 , 23.784258 , 18.4141   ,\n",
              "       17.923422 , 20.337881 , 22.936398 , 25.297531 , 17.572325 ,\n",
              "       14.486319 , 20.739832 , 21.733093 , 11.1917715, 18.290442 ,\n",
              "       20.70475  , 20.929468 , 18.990923 ,  8.7798395, 21.141748 ,\n",
              "       21.021317 , 15.49217  , 24.455221 , 31.499088 , 22.668139 ,\n",
              "       14.862843 , 19.69585  , 24.746317 , 22.913176 , 48.144817 ,\n",
              "       19.950285 , 30.148172 , 49.98047  , 16.743952 , 16.218952 ,\n",
              "        9.891141 , 20.452726 , 17.06055  , 14.73646  , 17.539606 ,\n",
              "       19.555712 , 30.26191  , 27.037518 , 18.43813  , 20.100842 ,\n",
              "       24.147627 , 10.21256  , 25.064299 , 48.283043 , 20.977459 ,\n",
              "       23.265625 , 20.141813 , 11.87677  , 17.84212  , 15.1286955,\n",
              "       14.9789295, 23.502743 , 16.092314 , 21.276255 , 26.55347  ,\n",
              "       16.940031 , 23.485325 , 14.927286 , 20.90435  , 19.254526 ,\n",
              "       24.397417 , 27.566774 , 23.607512 , 17.905067 , 22.675825 ,\n",
              "       25.12203  , 15.141896 , 18.460642 , 23.440636 , 16.4928   ,\n",
              "       23.372946 , 30.389936 , 15.330368 , 24.69199  , 17.316717 ,\n",
              "       14.531138 , 10.496169 , 24.805672 , 15.659789 , 38.916733 ,\n",
              "       20.403166 , 42.113743 ,  8.544421 , 22.536352 , 15.654481 ,\n",
              "       15.709977 , 17.263374 , 23.888586 , 21.690222 , 46.16276  ,\n",
              "       15.304819 , 31.137545 , 25.326769 , 18.969254 , 26.29209  ,\n",
              "       11.722559 , 40.65201  , 20.52522  , 17.135836 , 24.829275 ,\n",
              "       15.565665 , 23.360205 ,  8.280649 , 24.018639 , 19.57025  ,\n",
              "       20.865868 , 23.611485 , 22.455328 , 17.646477 , 17.687094 ,\n",
              "       14.59732  , 25.61237  , 13.333718 , 22.577513 , 20.657572 ,\n",
              "       14.8804865, 16.539358 , 23.276703 , 24.873934 , 22.52675  ,\n",
              "       23.107155 , 31.871576 , 19.262531 , 19.536154 , 28.251024 ,\n",
              "       23.817226 , 12.874959 , 22.59372  , 12.234834 , 10.024989 ,\n",
              "       20.419611 , 10.369816 , 45.84478  , 24.873934 , 12.357825 ,\n",
              "       16.367088 , 14.355771 , 28.338346 , 18.669233 , 20.334248 ,\n",
              "       10.546778 , 21.30952  , 21.00914  , 20.669264 , 23.91886  ,\n",
              "       25.009733 , 26.945326 , 13.288843 , 18.277857 , 20.95568  ,\n",
              "       18.233625 , 23.807056 , 13.400126 , 23.875198 , 33.050533 ,\n",
              "       27.785492 , 25.296518 , 19.071947 , 20.950756 , 11.507434 ,\n",
              "       22.855497 , 15.573306 , 22.33747  , 20.807749 , 22.41908  ,\n",
              "       17.212593 , 12.645366 , 35.121113 , 18.852188 , 48.823723 ,\n",
              "       22.462465 , 24.267456 , 21.375692 , 19.38756  ,  8.561088 ,\n",
              "       20.726429 , 23.400837 , 21.41578  , 17.63176  , 25.232733 ,\n",
              "       21.164701 , 26.444288 , 14.49171  , 49.559753 , 30.693232 ,\n",
              "       23.20531  , 22.950115 , 16.84211  , 30.982431 , 16.259336 ,\n",
              "       23.613512 , 20.93225  , 20.178421 , 22.782583 ], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "368WJJjCkPT9",
        "outputId": "ed68f56f-4fa0-4565-f8f9-d38eb7fd04a8"
      },
      "source": [
        "model2_training_res"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([23.561, 21.022, 21.465, 35.221, 15.062, 14.629, 21.588, 14.667,\n",
              "       12.363, 21.828, 14.101,  7.042, 29.08 , 44.522, 33.834, 20.747,\n",
              "       22.526, 19.747, 32.923, 19.66 , 26.751,  8.723, 45.665, 21.629,\n",
              "       23.539, 19.381, 20.109, 25.805, 22.539, 32.072, 19.52 ,  9.126,\n",
              "       18.353, 21.185, 12.584, 10.348, 12.31 , 23.938, 19.393, 15.114,\n",
              "       24.067, 27.21 , 15.251, 17.059, 15.621, 13.789, 25.221, 17.059,\n",
              "       48.742, 17.893, 21.433, 32.315, 16.549, 22.854, 19.759, 19.065,\n",
              "       22.755, 35.022, 30.154, 32.975, 21.663, 46.745, 13.924,  6.388,\n",
              "       18.796,  9.737, 26.519, 18.736, 20.681, 46.407, 39.823, 34.074,\n",
              "       20.965, 34.112, 25.065, 48.997, 13.343, 18.348, 19.725, 22.523,\n",
              "       48.399, 23.783, 25.025, 22.137, 43.516, 17.915, 31.274, 35.093,\n",
              "        8.017, 20.012, 18.93 , 12.469, 24.484, 47.693, 36.144, 21.567,\n",
              "       37.735, 18.029, 15.586, 28.386, 21.847, 19.898, 18.059, 19.368,\n",
              "       19.351, 21.941, 20.675, 33.152, 20.581, 13.184, 19.115, 20.897,\n",
              "       17.756, 19.997, 47.689, 16.095, 16.296, 18.475, 14.254, 32.593,\n",
              "       15.115, 43.55 , 34.463, 19.884, 15.047,  8.638, 11.583, 12.683,\n",
              "       18.834,  8.085, 22.855, 12.097, 20.14 , 45.877, 22.504, 19.657,\n",
              "       31.497, 20.734, 32.094, 31.406, 14.62 , 16.022, 49.283, 20.933,\n",
              "       16.438, 12.808, 48.782, 31.62 , 11.262, 19.526, 27.789, 32.462,\n",
              "       23.03 ,  9.612, 24.133, 23.872, 22.105, 12.874, 27.808, 33.149,\n",
              "       14.557, 19.287, 26.752, 34.825, 29.75 , 23.286, 10.777, 22.921,\n",
              "       23.068, 35.353, 23.47 , 20.132, 19.375, 10.317, 22.004, 19.544,\n",
              "       20.291, 11.14 , 42.281, 46.886, 13.07 , 20.217, 24.316, 15.783,\n",
              "       21.667, 22.129, 32.661, 20.983, 24.941, 18.768, 32.024, 14.445,\n",
              "       14.664, 22.505, 23.599, 17.743, 26.271, 25.483, 27.413, 21.696,\n",
              "       18.6  , 27.243, 14.454, 20.91 , 17.862, 41.129, 27.089, 20.17 ,\n",
              "       24.908, 18.621, 17.989, 19.031, 23.75 , 25.409, 17.637, 14.113,\n",
              "       21.767, 21.925, 10.657, 19.14 , 21.41 , 19.617, 18.703,  8.727,\n",
              "       22.839, 20.515, 15.15 , 23.338, 29.563, 21.804, 14.028, 19.838,\n",
              "       29.507, 21.955, 46.01 , 17.321, 30.567, 47.871, 16.874, 17.343,\n",
              "        9.681, 20.236, 16.657, 16.102, 16.273, 19.686, 30.078, 28.337,\n",
              "       19.318, 21.755, 24.716, 10.457, 25.701, 47.231, 21.075, 22.838,\n",
              "       20.319, 12.991, 19.002, 16.018, 13.775, 22.775, 15.265, 20.943,\n",
              "       26.305, 17.976, 23.816, 14.544, 21.503, 20.028, 25.75 , 27.872,\n",
              "       23.308, 18.776, 23.245, 24.796, 14.973, 19.233, 24.518, 16.868,\n",
              "       24.067, 30.59 , 15.965, 24.431, 16.283, 13.683, 11.69 , 24.293,\n",
              "       14.678, 37.655, 19.734, 44.144,  9.222, 23.193, 14.479, 15.062,\n",
              "       17.455, 22.734, 22.549, 45.467, 20.39 , 30.1  , 26.242, 18.305,\n",
              "       25.339, 11.985, 40.429, 20.648, 17.577, 24.805, 14.567, 24.36 ,\n",
              "        7.849, 23.789, 18.752, 21.588, 23.403, 23.575, 17.698, 18.202,\n",
              "       14.02 , 26.371, 13.812, 22.427, 20.452, 15.161, 16.835, 22.539,\n",
              "       24.691, 21.721, 23.203, 30.453, 20.947, 20.023, 27.959, 23.611,\n",
              "       12.895, 24.091, 12.246,  8.747, 20.89 , 10.003, 47.23 , 25.349,\n",
              "       12.422, 17.318, 14.552, 27.91 , 19.301, 21.317,  9.508, 20.894,\n",
              "       21.465, 19.956, 23.696, 25.15 , 27.684, 13.877, 16.68 , 20.539,\n",
              "       19.053, 24.341, 14.323, 25.667, 33.933, 26.514, 24.705, 19.254,\n",
              "       19.917, 14.876, 23.356, 13.731, 23.869, 21.895, 21.202, 17.406,\n",
              "       11.927, 32.917, 19.234, 49.214, 22.968, 23.444, 21.067, 18.744,\n",
              "        8.545, 21.208, 23.378, 21.326, 17.47 , 26.561, 21.133, 27.207,\n",
              "       14.774, 48.139, 31.092, 23.948, 22.103, 17.71 , 31.04 , 15.616,\n",
              "       22.949, 21.397, 19.896, 23.137])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stpOH2LMkX0t",
        "outputId": "0c7f6ddf-e6da-4623-add9-1947be7111b2"
      },
      "source": [
        "Y_train"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "321    23.1\n",
              "37     21.0\n",
              "286    20.1\n",
              "2      34.7\n",
              "25     13.9\n",
              "       ... \n",
              "22     15.2\n",
              "72     22.8\n",
              "493    21.8\n",
              "15     19.9\n",
              "168    23.8\n",
              "Name: price, Length: 404, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiZAY_w6kebz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}