Project_House_Price_Prediction(Training).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Project 4 : House Price Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqN1B6AYN1pH"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jP9nQgrfNmcD"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G8fcvW0LOkMW"
      },
      "source": [
        "Importing the Boston House Price Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QN8fkbIbOiBJ"
      },
      "source": [
        "house_price_dataset = sklearn.datasets.load_boston()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KlhfTIBGOx1L",
        "outputId": "1435530a-f2f3-48ae-f0e8-23e305796a56"
      },
      "source": [
        "print(house_price_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
            "        4.9800e+00],\n",
            "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
            "        9.1400e+00],\n",
            "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
            "        4.0300e+00],\n",
            "       ...,\n",
            "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
            "        5.6400e+00],\n",
            "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
            "        6.4800e+00],\n",
            "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
            "        7.8800e+00]]), 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
            "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
            "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
            "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
            "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
            "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
            "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
            "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
            "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
            "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
            "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
            "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
            "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
            "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
            "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
            "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
            "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
            "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
            "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
            "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
            "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
            "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
            "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
            "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
            "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
            "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
            "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
            "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
            "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
            "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
            "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
            "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
            "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
            "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
            "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
            "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
            "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
            "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
            "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
            "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
            "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
            "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
            "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
            "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
            "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
            "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]), 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
            "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'), 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\", 'filename': '/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/boston_house_prices.csv'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-YGgzO-O0q_"
      },
      "source": [
        "# Loading the dataset to a Pandas DataFrame\n",
        "house_price_dataframe = pd.DataFrame(house_price_dataset.data, columns = house_price_dataset.feature_names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "iD9G4lZEPdHE",
        "outputId": "d73c1359-104f-49c4-e952-c70c04ed1955"
      },
      "source": [
        "# Print First 5 rows of our DataFrame\n",
        "house_price_dataframe.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</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",
              "  </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",
              "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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSwbOIOUPkOD"
      },
      "source": [
        "# add the target (price) column to the DataFrame\n",
        "house_price_dataframe['price'] = house_price_dataset.target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "k546Pkt7QH9a",
        "outputId": "a6b8e05d-0b96-47ed-9105-a6dbc989ed01"
      },
      "source": [
        "house_price_dataframe.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</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",
              "      <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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVYIEaeNQLTA",
        "outputId": "319c261b-3df6-4cdd-f42c-00822d3c71de"
      },
      "source": [
        "# checking the number of rows and Columns in the data frame\n",
        "house_price_dataframe.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQGYw-rnQWHb",
        "outputId": "3eb3ebe8-3a33-4cc6-a09d-5bbc695632b6"
      },
      "source": [
        "# check for missing values\n",
        "house_price_dataframe.isnull().sum()"
      ],
      "execution_count": null,
      "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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "kH9ooE_AQkY0",
        "outputId": "70e0ba39-f28b-40a3-8763-3d94e41eda5d"
      },
      "source": [
        "# statistical measures of the dataset\n",
        "house_price_dataframe.describe()"
      ],
      "execution_count": null,
      "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",
              "      <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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwS-1C_AQ-9l"
      },
      "source": [
        "Understanding the correlation between various features in the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cxE019_ERGH1"
      },
      "source": [
        "1. Positive Correlation\n",
        "\n",
        "2. Negative Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UF-yNAM8QzJm"
      },
      "source": [
        "correlation = house_price_dataframe.corr()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "aYTq75K4RcVE",
        "outputId": "26087034-706a-4d94-8fdf-2208a616e72a"
      },
      "source": [
        "# constructing a heatmap to nderstand 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": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f800f6b1cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "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": "BXTzFsxGTFs6"
      },
      "source": [
        "Splitting the data and Target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mv3Vgwq2SHp-"
      },
      "source": [
        "X = house_price_dataframe.drop(['price'], axis=1)\n",
        "Y = house_price_dataframe['price']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CEV3CxysTbiy",
        "outputId": "8e7e4526-fb14-4644-b983-1677b27fbf11"
      },
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        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]\n",
            "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\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AqJymd1aTjZ5"
      },
      "source": [
        "Splitting the data into Training data and Test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "24ESPrzMTeEu"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pm9nuQo_UVBR",
        "outputId": "e98793b3-77b0-4661-8be2-b947528c6099"
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(506, 13) (404, 13) (102, 13)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9okHig7HUirW"
      },
      "source": [
        "Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLBMl-35UlLy"
      },
      "source": [
        "XGBoost Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKg4RH1MUc1U"
      },
      "source": [
        "# loading the model\n",
        "model = XGBRegressor()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZpiwubXVEtI",
        "outputId": "9298ec5a-5fa3-441b-f2cb-16da06e4e9a5"
      },
      "source": [
        "# training the model with X_train\n",
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[16:17:24] 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": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flLAggHEVXk7"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "av8hCGqRVlcl"
      },
      "source": [
        "Prediction on training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzejmHLeVUKs"
      },
      "source": [
        "# accuracy for prediction on training data\n",
        "training_data_prediction = model.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JiBw0wOV80i",
        "outputId": "c1ffad12-6268-4433-85c2-93040c664a3f"
      },
      "source": [
        "print(training_data_prediction)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[23.360205  22.462858  20.84804   33.77895   15.333282  13.616525\n",
            " 21.71274   15.175322  11.724756  21.836252  16.08508    7.52517\n",
            " 31.094206  48.56228   32.623158  20.546066  22.177324  20.500404\n",
            " 31.666502  20.551508  25.74269    8.247894  45.200817  22.069397\n",
            " 20.698004  20.100042  19.873472  26.242834  23.39618   31.927258\n",
            " 21.493471   9.280926  18.504272  21.87202   12.504413  10.578829\n",
            " 13.054951  23.541336  19.164755  15.888303  23.768887  28.454714\n",
            " 15.539753  18.049202  16.23671   14.08383   25.33273   17.575668\n",
            " 49.566467  16.990675  21.738977  32.935143  16.125738  22.45393\n",
            " 20.776966  20.042227  22.898897  38.124043  30.607079  32.607468\n",
            " 20.919416  47.348038  14.524615   8.126455  19.581661   9.030508\n",
            " 26.462107  17.69918   20.546162  46.312218  39.689137  34.387108\n",
            " 22.11083   34.568977  24.873934  50.078335  14.5669775 20.525211\n",
            " 20.62971   23.202105  49.514477  23.12061   24.795782  20.319666\n",
            " 43.869396  17.110266  32.165016  34.75202    7.313497  20.309446\n",
            " 18.038298  12.008462  24.216425  47.90671   37.94349   20.759708\n",
            " 40.182804  18.249052  15.611586  26.39461   21.0571    20.421682\n",
            " 18.377089  17.338768  21.223648  22.653662  17.560051  32.635715\n",
            " 16.683764  13.004857  18.488163  20.659714  16.501846  20.648884\n",
            " 48.62411   15.977999  15.97522   18.581459  14.893438  32.871964\n",
            " 14.236945  43.612328  33.881115  19.073408  15.747335   9.4903965\n",
            " 10.153891  14.812717  18.655546   8.596755  22.666656  10.941623\n",
            " 20.534616  49.324417  22.710459  19.99658   31.663935  21.78586\n",
            " 30.9277    30.507492  15.054665  15.854853  48.532074  21.108742\n",
            " 15.687305  12.403721  49.90245   31.557863  11.709707  20.22495\n",
            " 26.214525  32.90807   22.90362    9.542897  24.487959  24.46598\n",
            " 22.509142  14.704502  27.895067  33.619015  14.888735  19.147383\n",
            " 26.40218   32.77208   29.293688  23.638102  10.448805  22.518728\n",
            " 21.47825   35.32415   23.002241  20.470022  18.918747  10.328174\n",
            " 22.244467  17.69918   20.918488  11.913417  42.572548  46.803394\n",
            " 14.652036  20.633188  23.285368  15.295161  20.861048  23.587011\n",
            " 32.94382   21.090906  24.898489  18.465925  31.454802  14.421506\n",
            " 15.421497  21.890705  23.64799   17.40471   26.111868  24.977922\n",
            " 27.56308   22.964123  18.823803  28.856464  14.080684  19.785515\n",
            " 17.007908  42.90537   26.354216  21.719929  23.784258  18.4141\n",
            " 17.923422  20.337881  22.936398  25.297531  17.572325  14.486319\n",
            " 20.739832  21.733093  11.1917715 18.290442  20.70475   20.929468\n",
            " 18.990923   8.7798395 21.141748  21.021317  15.49217   24.455221\n",
            " 31.499088  22.668139  14.862843  19.69585   24.746317  22.913176\n",
            " 48.144817  19.950285  30.148172  49.98047   16.743952  16.218952\n",
            "  9.891141  20.452726  17.06055   14.73646   17.539606  19.555712\n",
            " 30.26191   27.037518  18.43813   20.100842  24.147627  10.21256\n",
            " 25.064299  48.283043  20.977459  23.265625  20.141813  11.87677\n",
            " 17.84212   15.1286955 14.9789295 23.502743  16.092314  21.276255\n",
            " 26.55347   16.940031  23.485325  14.927286  20.90435   19.254526\n",
            " 24.397417  27.566774  23.607512  17.905067  22.675825  25.12203\n",
            " 15.141896  18.460642  23.440636  16.4928    23.372946  30.389936\n",
            " 15.330368  24.69199   17.316717  14.531138  10.496169  24.805672\n",
            " 15.659789  38.916733  20.403166  42.113743   8.544421  22.536352\n",
            " 15.654481  15.709977  17.263374  23.888586  21.690222  46.16276\n",
            " 15.304819  31.137545  25.326769  18.969254  26.29209   11.722559\n",
            " 40.65201   20.52522   17.135836  24.829275  15.565665  23.360205\n",
            "  8.280649  24.018639  19.57025   20.865868  23.611485  22.455328\n",
            " 17.646477  17.687094  14.59732   25.61237   13.333718  22.577513\n",
            " 20.657572  14.8804865 16.539358  23.276703  24.873934  22.52675\n",
            " 23.107155  31.871576  19.262531  19.536154  28.251024  23.817226\n",
            " 12.874959  22.59372   12.234834  10.024989  20.419611  10.369816\n",
            " 45.84478   24.873934  12.357825  16.367088  14.355771  28.338346\n",
            " 18.669233  20.334248  10.546778  21.30952   21.00914   20.669264\n",
            " 23.91886   25.009733  26.945326  13.288843  18.277857  20.95568\n",
            " 18.233625  23.807056  13.400126  23.875198  33.050533  27.785492\n",
            " 25.296518  19.071947  20.950756  11.507434  22.855497  15.573306\n",
            " 22.33747   20.807749  22.41908   17.212593  12.645366  35.121113\n",
            " 18.852188  48.823723  22.462465  24.267456  21.375692  19.38756\n",
            "  8.561088  20.726429  23.400837  21.41578   17.63176   25.232733\n",
            " 21.164701  26.444288  14.49171   49.559753  30.693232  23.20531\n",
            " 22.950115  16.84211   30.982431  16.259336  23.613512  20.93225\n",
            " 20.178421  22.782583 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKUKCqmvV_eV",
        "outputId": "3948f0fc-7617-49ea-e53f-bf390f115b20"
      },
      "source": [
        "# R squared error\n",
        "score_1 = metrics.r2_score(Y_train, training_data_prediction)\n",
        "\n",
        "# Mean Absolute Error\n",
        "score_2 = metrics.mean_absolute_error(Y_train, training_data_prediction)\n",
        "\n",
        "print(\"R squared error : \", score_1)\n",
        "print('Mean Absolute Error : ', score_2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R squared error :  0.9733349094832763\n",
            "Mean Absolute Error :  1.145314053261634\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pocHm9A9X7U_"
      },
      "source": [
        "Visualizing the actual Prices and predicted prices"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "v7xhpz-8X54x",
        "outputId": "68a9d6d9-a260-429b-b1dd-795aadb5509e"
      },
      "source": [
        "plt.scatter(Y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Prices\")\n",
        "plt.ylabel(\"Predicted Prices\")\n",
        "plt.title(\"Actual Price vs Predicted Price\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": "wQhfqLU6XeRx"
      },
      "source": [
        "Prediction on Test Data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4IAr7ERW-jb"
      },
      "source": [
        "# accuracy for prediction on test data\n",
        "test_data_prediction = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPeukqj-XmFL",
        "outputId": "385d597b-5610-42c3-e0ff-a852f3b6ae44"
      },
      "source": [
        "# R squared error\n",
        "score_1 = metrics.r2_score(Y_test, test_data_prediction)\n",
        "\n",
        "# Mean Absolute Error\n",
        "score_2 = metrics.mean_absolute_error(Y_test, test_data_prediction)\n",
        "\n",
        "print(\"R squared error : \", score_1)\n",
        "print('Mean Absolute Error : ', score_2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R squared error :  0.9115937697657654\n",
            "Mean Absolute Error :  1.9922956859364223\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIL1ojb5Xxr6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}