Problem_02.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Problem_02.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM0BEvHjzPaHWFoYc2V8fhI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prernamittal/MLDLS/blob/main/Problem_02.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**House Sales in King County, USA**"
      ],
      "metadata": {
        "id": "q0DSkj4awnRP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "SFYOyJiTwSPd",
        "outputId": "40ff0265-b1a5-496f-8ab8-fe89e8e44529"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1b887f12-4bf0-4db9-8897-fe10159c7696\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1b887f12-4bf0-4db9-8897-fe10159c7696\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving house_data.csv to house_data (1).csv\n",
            "User uploaded file \"house_data.csv\" with length 2673776 bytes\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing the libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "IdZNqVyKwhrm"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing the dataset\n",
        "df= pd.read_csv('house_data.csv')"
      ],
      "metadata": {
        "id": "M3Wg2diawhwb"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O34IPx6cxKpH",
        "outputId": "e89aed29-3aa7-4e18-a84e-76cff73e196a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Unnamed: 0          id             date     price  bedrooms  bathrooms  \\\n",
            "0           0  7129300520  20141013T000000  221900.0       3.0       1.00   \n",
            "1           1  6414100192  20141209T000000  538000.0       3.0       2.25   \n",
            "2           2  5631500400  20150225T000000  180000.0       2.0       1.00   \n",
            "3           3  2487200875  20141209T000000  604000.0       4.0       3.00   \n",
            "4           4  1954400510  20150218T000000  510000.0       3.0       2.00   \n",
            "\n",
            "   sqft_living  sqft_lot  floors  waterfront  ...  grade  sqft_above  \\\n",
            "0         1180      5650     1.0           0  ...      7        1180   \n",
            "1         2570      7242     2.0           0  ...      7        2170   \n",
            "2          770     10000     1.0           0  ...      6         770   \n",
            "3         1960      5000     1.0           0  ...      7        1050   \n",
            "4         1680      8080     1.0           0  ...      8        1680   \n",
            "\n",
            "   sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
            "0              0      1955             0    98178  47.5112 -122.257   \n",
            "1            400      1951          1991    98125  47.7210 -122.319   \n",
            "2              0      1933             0    98028  47.7379 -122.233   \n",
            "3            910      1965             0    98136  47.5208 -122.393   \n",
            "4              0      1987             0    98074  47.6168 -122.045   \n",
            "\n",
            "   sqft_living15  sqft_lot15  \n",
            "0           1340        5650  \n",
            "1           1690        7639  \n",
            "2           2720        8062  \n",
            "3           1360        5000  \n",
            "4           1800        7503  \n",
            "\n",
            "[5 rows x 22 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "z4l7JYCJxK4g"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GlhWFmMxmwA",
        "outputId": "39984c13-cd7f-411a-e21d-a883b3e1cd7c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0         int64\n",
              "id                 int64\n",
              "date              object\n",
              "price            float64\n",
              "bedrooms         float64\n",
              "bathrooms        float64\n",
              "sqft_living        int64\n",
              "sqft_lot           int64\n",
              "floors           float64\n",
              "waterfront         int64\n",
              "view               int64\n",
              "condition          int64\n",
              "grade              int64\n",
              "sqft_above         int64\n",
              "sqft_basement      int64\n",
              "yr_built           int64\n",
              "yr_renovated       int64\n",
              "zipcode            int64\n",
              "lat              float64\n",
              "long             float64\n",
              "sqft_living15      int64\n",
              "sqft_lot15         int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "glR8pc41yyPu",
        "outputId": "f5515fe6-9c2a-4e3e-bec2-a070d631466c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Unnamed: 0            id         price      bedrooms     bathrooms  \\\n",
              "count  21613.00000  2.161300e+04  2.161300e+04  21600.000000  21603.000000   \n",
              "mean   10806.00000  4.580302e+09  5.400881e+05      3.372870      2.115736   \n",
              "std     6239.28002  2.876566e+09  3.671272e+05      0.926657      0.768996   \n",
              "min        0.00000  1.000102e+06  7.500000e+04      1.000000      0.500000   \n",
              "25%     5403.00000  2.123049e+09  3.219500e+05      3.000000      1.750000   \n",
              "50%    10806.00000  3.904930e+09  4.500000e+05      3.000000      2.250000   \n",
              "75%    16209.00000  7.308900e+09  6.450000e+05      4.000000      2.500000   \n",
              "max    21612.00000  9.900000e+09  7.700000e+06     33.000000      8.000000   \n",
              "\n",
              "        sqft_living      sqft_lot        floors    waterfront          view  \\\n",
              "count  21613.000000  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
              "mean    2079.899736  1.510697e+04      1.494309      0.007542      0.234303   \n",
              "std      918.440897  4.142051e+04      0.539989      0.086517      0.766318   \n",
              "min      290.000000  5.200000e+02      1.000000      0.000000      0.000000   \n",
              "25%     1427.000000  5.040000e+03      1.000000      0.000000      0.000000   \n",
              "50%     1910.000000  7.618000e+03      1.500000      0.000000      0.000000   \n",
              "75%     2550.000000  1.068800e+04      2.000000      0.000000      0.000000   \n",
              "max    13540.000000  1.651359e+06      3.500000      1.000000      4.000000   \n",
              "\n",
              "       ...         grade    sqft_above  sqft_basement      yr_built  \\\n",
              "count  ...  21613.000000  21613.000000   21613.000000  21613.000000   \n",
              "mean   ...      7.656873   1788.390691     291.509045   1971.005136   \n",
              "std    ...      1.175459    828.090978     442.575043     29.373411   \n",
              "min    ...      1.000000    290.000000       0.000000   1900.000000   \n",
              "25%    ...      7.000000   1190.000000       0.000000   1951.000000   \n",
              "50%    ...      7.000000   1560.000000       0.000000   1975.000000   \n",
              "75%    ...      8.000000   2210.000000     560.000000   1997.000000   \n",
              "max    ...     13.000000   9410.000000    4820.000000   2015.000000   \n",
              "\n",
              "       yr_renovated       zipcode           lat          long  sqft_living15  \\\n",
              "count  21613.000000  21613.000000  21613.000000  21613.000000   21613.000000   \n",
              "mean      84.402258  98077.939805     47.560053   -122.213896    1986.552492   \n",
              "std      401.679240     53.505026      0.138564      0.140828     685.391304   \n",
              "min        0.000000  98001.000000     47.155900   -122.519000     399.000000   \n",
              "25%        0.000000  98033.000000     47.471000   -122.328000    1490.000000   \n",
              "50%        0.000000  98065.000000     47.571800   -122.230000    1840.000000   \n",
              "75%        0.000000  98118.000000     47.678000   -122.125000    2360.000000   \n",
              "max     2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n",
              "\n",
              "          sqft_lot15  \n",
              "count   21613.000000  \n",
              "mean    12768.455652  \n",
              "std     27304.179631  \n",
              "min       651.000000  \n",
              "25%      5100.000000  \n",
              "50%      7620.000000  \n",
              "75%     10083.000000  \n",
              "max    871200.000000  \n",
              "\n",
              "[8 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-97463343-ce98-41e7-bbe5-a2336ec39f6c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Unnamed: 0</th>\n",
              "      <th>id</th>\n",
              "      <th>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>floors</th>\n",
              "      <th>waterfront</th>\n",
              "      <th>view</th>\n",
              "      <th>...</th>\n",
              "      <th>grade</th>\n",
              "      <th>sqft_above</th>\n",
              "      <th>sqft_basement</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>yr_renovated</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>sqft_living15</th>\n",
              "      <th>sqft_lot15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>21613.00000</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>21600.000000</td>\n",
              "      <td>21603.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10806.00000</td>\n",
              "      <td>4.580302e+09</td>\n",
              "      <td>5.400881e+05</td>\n",
              "      <td>3.372870</td>\n",
              "      <td>2.115736</td>\n",
              "      <td>2079.899736</td>\n",
              "      <td>1.510697e+04</td>\n",
              "      <td>1.494309</td>\n",
              "      <td>0.007542</td>\n",
              "      <td>0.234303</td>\n",
              "      <td>...</td>\n",
              "      <td>7.656873</td>\n",
              "      <td>1788.390691</td>\n",
              "      <td>291.509045</td>\n",
              "      <td>1971.005136</td>\n",
              "      <td>84.402258</td>\n",
              "      <td>98077.939805</td>\n",
              "      <td>47.560053</td>\n",
              "      <td>-122.213896</td>\n",
              "      <td>1986.552492</td>\n",
              "      <td>12768.455652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6239.28002</td>\n",
              "      <td>2.876566e+09</td>\n",
              "      <td>3.671272e+05</td>\n",
              "      <td>0.926657</td>\n",
              "      <td>0.768996</td>\n",
              "      <td>918.440897</td>\n",
              "      <td>4.142051e+04</td>\n",
              "      <td>0.539989</td>\n",
              "      <td>0.086517</td>\n",
              "      <td>0.766318</td>\n",
              "      <td>...</td>\n",
              "      <td>1.175459</td>\n",
              "      <td>828.090978</td>\n",
              "      <td>442.575043</td>\n",
              "      <td>29.373411</td>\n",
              "      <td>401.679240</td>\n",
              "      <td>53.505026</td>\n",
              "      <td>0.138564</td>\n",
              "      <td>0.140828</td>\n",
              "      <td>685.391304</td>\n",
              "      <td>27304.179631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000102e+06</td>\n",
              "      <td>7.500000e+04</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>5.200000e+02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98001.000000</td>\n",
              "      <td>47.155900</td>\n",
              "      <td>-122.519000</td>\n",
              "      <td>399.000000</td>\n",
              "      <td>651.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5403.00000</td>\n",
              "      <td>2.123049e+09</td>\n",
              "      <td>3.219500e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>1427.000000</td>\n",
              "      <td>5.040000e+03</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1190.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1951.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98033.000000</td>\n",
              "      <td>47.471000</td>\n",
              "      <td>-122.328000</td>\n",
              "      <td>1490.000000</td>\n",
              "      <td>5100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10806.00000</td>\n",
              "      <td>3.904930e+09</td>\n",
              "      <td>4.500000e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>1910.000000</td>\n",
              "      <td>7.618000e+03</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1560.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1975.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98065.000000</td>\n",
              "      <td>47.571800</td>\n",
              "      <td>-122.230000</td>\n",
              "      <td>1840.000000</td>\n",
              "      <td>7620.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16209.00000</td>\n",
              "      <td>7.308900e+09</td>\n",
              "      <td>6.450000e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2550.000000</td>\n",
              "      <td>1.068800e+04</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2210.000000</td>\n",
              "      <td>560.000000</td>\n",
              "      <td>1997.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98118.000000</td>\n",
              "      <td>47.678000</td>\n",
              "      <td>-122.125000</td>\n",
              "      <td>2360.000000</td>\n",
              "      <td>10083.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21612.00000</td>\n",
              "      <td>9.900000e+09</td>\n",
              "      <td>7.700000e+06</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13540.000000</td>\n",
              "      <td>1.651359e+06</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9410.000000</td>\n",
              "      <td>4820.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>98199.000000</td>\n",
              "      <td>47.777600</td>\n",
              "      <td>-121.315000</td>\n",
              "      <td>6210.000000</td>\n",
              "      <td>871200.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97463343-ce98-41e7-bbe5-a2336ec39f6c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-97463343-ce98-41e7-bbe5-a2336ec39f6c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-97463343-ce98-41e7-bbe5-a2336ec39f6c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking Null values in dataset\n",
        "df.isnull().sum()\n",
        "sns.heatmap(df.isnull(), yticklabels = False, cbar = False, cmap = 'viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "ZFvegsS4d9w_",
        "outputId": "8232dc48-ff0c-4e8a-d83e-8ce56b6b2a3f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0a770f850>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "df.isnull().sum()\n",
        "sns.heatmap(df.isnull(), yticklabels = False, cbar = False, cmap = 'viridis')"
      ],
      "metadata": {
        "id": "bct8bQq511_I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Making dependent and independent features to test model and predict for future values. id and date do not affect the prices of house.\n",
        "#yr_built , yr_renovated and zipcode should be removed as they will decrease the accurracy for the prediction of model.\n",
        "X = df.iloc[:, [3,4,5,6,7,8,9,10,11,12,13,17,18,19,20]].values\n",
        "y = df.iloc[:, 2].values"
      ],
      "metadata": {
        "id": "IFDbeuSa12Eb"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting the dataset into the Training set and Test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
      ],
      "metadata": {
        "id": "LxlgxGW-12G8"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Traing the train dataset on Random Forest Regression\n",
        "regressor = RandomForestRegressor(n_estimators = 30)\n",
        "regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "un91QLI412Jq",
        "outputId": "a8161d41-55b1-43c2-8060-ad20c0fe683f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-767feae228eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mregressor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sparse multilabel-indicator for y is not supported.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m         X, y = self._validate_data(\n\u001b[0;32m--> 328\u001b[0;31m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    329\u001b[0m         )\n\u001b[1;32m    330\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    579\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    974\u001b[0m         \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m         \u001b[0mensure_min_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 976\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    977\u001b[0m     )\n\u001b[1;32m    978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    744\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    745\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 746\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    747\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m                 raise ValueError(\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '20150316T000000'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating the R^2 for our training set to see how well the model is adapted to the train dataset to predict housing price. \n",
        "y_pred_train = regressor.predict(X_train)\n",
        "print(r2_score(y_train,y_pred_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "MyzMRtR83_a0",
        "outputId": "20ce3b5f-724f-47f2-a812-c157ad76b570"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-292426324226>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_pred_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_pred_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    988\u001b[0m         \u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m             \u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_accumulate_prediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0my_hat\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    991\u001b[0m         )\n\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'RandomForestRegressor' object has no attribute 'estimators_'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting the Test set results\n",
        "y_pred_test = regressor.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "6TbF_TQO3_do",
        "outputId": "01e66490-3807-4d8b-ee31-3918942e0e20"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-6c86751e4f1a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_pred_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    988\u001b[0m         \u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m             \u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_accumulate_prediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0my_hat\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    991\u001b[0m         )\n\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'RandomForestRegressor' object has no attribute 'estimators_'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating r2 Score for model trained on test dataset\n",
        "print(r2_score(y_test,y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "KtDUvbyx41fw",
        "outputId": "b73f31e2-1e82-4dbd-ecec-8027689a3137"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-9608b34e8e33>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_pred_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'y_pred_test' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "k1Vu4FvW41jC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}