House_Price_Prediction_ML_Project_3.ipynb

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  "metadata": {
    "colab": {
      "name": "House Price Prediction-ML Project 3",
      "provenance": [],
      "authorship_tag": "ABX9TyODq4HtfsJNiwvFqCYhmvzZ",
      "include_colab_link": true
    },
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Joykareko/Data-Science-Projects/blob/main/House_Price_Prediction_ML_Project_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkmSoAu3sp6x"
      },
      "source": [
        "Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxjgVvVGqxHF"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\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"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hyawb4wqtgLV"
      },
      "source": [
        "#importing the dataset\n",
        "housing_dataset = sklearn.datasets.load_boston()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtoZrOPGtxfs"
      },
      "source": [
        "#importing the dataset as a pandas dataframe\n",
        "housing_dataset_df = pd.DataFrame(housing_dataset.data, columns = housing_dataset.feature_names)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 143
        },
        "id": "F_0KfzCG0n9D",
        "outputId": "ce542dde-53e5-406a-c926-d7bc50132603"
      },
      "source": [
        "housing_dataset_df.head(3)"
      ],
      "execution_count": 8,
      "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",
              "    </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",
              "  </tbody>\n",
              "</table>\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",
              "\n",
              "[3 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nQfFyKX073J"
      },
      "source": [
        "#adding the target column to the dataframe\n",
        "housing_dataset_df['Price'] = housing_dataset.target"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQ47rFxu1PUc",
        "outputId": "f0c77a5a-9d2a-442f-bfd1-9cfc396ccaa8"
      },
      "source": [
        "housing_dataset_df.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
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          "height": 143
        },
        "id": "11YcgP3U1SW8",
        "outputId": "0b607c12-0f41-4526-d150-9128265528c4"
      },
      "source": [
        "housing_dataset_df.head(3)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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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",
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              "    <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",
              "  </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",
              "\n",
              "[3 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GkcVIaBd1YdM",
        "outputId": "21f3a393-4cdd-4535-9729-a71e38a473b5"
      },
      "source": [
        "#checking for missing values\n",
        "housing_dataset_df.isnull().sum()"
      ],
      "execution_count": 13,
      "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": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "znyQ6_651mZa",
        "outputId": "e2155e1e-8d5e-434f-a9ae-c02532d5feb5"
      },
      "source": [
        "#statistical measures\n",
        "housing_dataset_df.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sD0Sln6G1uB0"
      },
      "source": [
        "#understanding the correlation between various variables/features\n",
        "correlation = housing_dataset_df.corr()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "kqBuFt2c2dmk",
        "outputId": "6fa5a726-7aea-4e01-c8d8-81da056526a7"
      },
      "source": [
        "#creating a heatmap to understand the correlation\n",
        "plt.figure(figsize = (10,10))\n",
        "sns.heatmap(correlation,cbar = True, square = True , fmt = '.1f',annot = True, annot_kws={'size':8}, cmap = 'Blues')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a75224790>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_WF0H0Y_5ykE",
        "outputId": "75d072de-6ae6-4821-c082-30650f1c3f07"
      },
      "source": [
        "#splitting the dataset to X and Y\n",
        "X = housing_dataset_df.drop(columns = 'Price', axis = 1)\n",
        "Y = housing_dataset_df['Price']\n",
        "print(X.shape,Y.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(506, 13) (506,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2d7bj2KN6G7j"
      },
      "source": [
        "#splitting the data into training and testing dataset\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y, test_size = 0.2 , random_state = 2)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dG6POPY7FES",
        "outputId": "a94d5394-b006-45bb-d54a-b61c932886f7"
      },
      "source": [
        "print(X.shape,X_train.shape,X_test.shape,Y_train.shape,Y_test.shape)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(506, 13) (404, 13) (102, 13) (404,) (102,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRCA32tk8CyM"
      },
      "source": [
        "#training our model\n",
        "#loading our model\n",
        "model = XGBRegressor()\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oeXG_3fO8i8c",
        "outputId": "3f803b5e-fee6-467b-f2c3-afd1c0410307"
      },
      "source": [
        "#fitting the model for training\n",
        "model.fit(X_train,Y_train)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[08:07:26] 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()"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82TkBAiv8ywf"
      },
      "source": [
        "#evaluation\n",
        "training_data_prediction = model.predict(X_train)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2l33MAL79gBQ",
        "outputId": "f1e5450f-b8c5-4e5b-9c4b-2625b186b60c"
      },
      "source": [
        "# R squared error\n",
        "score_1 = metrics.r2_score(Y_train, training_data_prediction)\n",
        "print('Rsquared value: ',score_1)\n",
        "score_2 = metrics.mean_absolute_error(Y_train,training_data_prediction)\n",
        "print('Mean Absolute Error: ', score_2)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rsquared value:  0.9733349094832763\n",
            "Mean Absolute Error:  1.145314053261634\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56MoG7tu-7S0"
      },
      "source": [
        "#testing data \n",
        "testing_data_prediction = model.predict(X_test)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CAyXexZW_FEN",
        "outputId": "56b7378d-fc8c-4e2a-86fe-bb68e739eae6"
      },
      "source": [
        "# R squared error\n",
        "score_1 = metrics.r2_score(Y_test, testing_data_prediction)\n",
        "print('Rsquared value: ',score_1)\n",
        "score_2 = metrics.mean_absolute_error(Y_test,testing_data_prediction)\n",
        "print('Mean Absolute Error: ', score_2)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rsquared value:  0.9115937697657654\n",
            "Mean Absolute Error:  1.9922956859364223\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "aA5uL_4r_SiD",
        "outputId": "9f08c096-0456-443d-ebfa-64e664af9f8f"
      },
      "source": [
        "#creating a visualization between perdicted values and original prices\n",
        "plt.scatter(Y_train, training_data_prediction)\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predicted Training Price')\n",
        "plt.title('Actual Prices Vs Predicted Prices')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "S7z_o0OIAmMQ",
        "outputId": "d390b747-d883-41fa-ead7-ad4d3aff4c4a"
      },
      "source": [
        "#creating a visualization between predicted values and original prices\n",
        "plt.scatter(Y_test, testing_data_prediction)\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predicted Testing Price')\n",
        "plt.title('Actual Prices Vs Predicted Prices')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}