House_Price_Prediction.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "House_Price_Prediction.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harshchan/AI-ML-python-codes/blob/main/House_Price_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mpNSwXh4jvlT"
      },
      "source": [
        "#Get all essential libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import io"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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": 73
        },
        "id": "E6FuiRuGjvlj",
        "outputId": "a0842681-6b13-4501-9049-b632c94cf1b3"
      },
      "source": [
        "#Get the data\n",
        "from  google.colab  import  files \n",
        "uploaded = files.upload()\n",
        "#houses = pd.read_csv(\"/home/sagar/Desktop/Desk_folders/Artificial Intelligence - Machine Learning/4.Case_Study_Regression_Methods/Regression/archive/kc_house_data.csv\")\n",
        "#houses.head()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-6b7c5c48-1ad9-4c64-a841-571d422fdd82\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-6b7c5c48-1ad9-4c64-a841-571d422fdd82\">\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> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kc_house_data.csv to kc_house_data (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SC4McPcNluaW"
      },
      "source": [
        "houses = pd.read_csv(io.BytesIO(uploaded['kc_house_data.csv']))"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "CsDOaQZVjvln",
        "outputId": "7fd9c5c4-a3b8-4261-9a93-1f22b3acd14b"
      },
      "source": [
        "#Have a look at different stats\n",
        "houses.describe()"
      ],
      "execution_count": 53,
      "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>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>condition</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>2.161300e+04</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</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>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",
              "      <td>21613.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.580302e+09</td>\n",
              "      <td>5.400881e+05</td>\n",
              "      <td>3.370842</td>\n",
              "      <td>2.114757</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>3.409430</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>2.876566e+09</td>\n",
              "      <td>3.671272e+05</td>\n",
              "      <td>0.930062</td>\n",
              "      <td>0.770163</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>0.650743</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>1.000102e+06</td>\n",
              "      <td>7.500000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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>1.000000</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>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>3.000000</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>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>3.000000</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>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>4.000000</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>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>5.000000</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",
              "</div>"
            ],
            "text/plain": [
              "                 id         price  ...  sqft_living15     sqft_lot15\n",
              "count  2.161300e+04  2.161300e+04  ...   21613.000000   21613.000000\n",
              "mean   4.580302e+09  5.400881e+05  ...    1986.552492   12768.455652\n",
              "std    2.876566e+09  3.671272e+05  ...     685.391304   27304.179631\n",
              "min    1.000102e+06  7.500000e+04  ...     399.000000     651.000000\n",
              "25%    2.123049e+09  3.219500e+05  ...    1490.000000    5100.000000\n",
              "50%    3.904930e+09  4.500000e+05  ...    1840.000000    7620.000000\n",
              "75%    7.308900e+09  6.450000e+05  ...    2360.000000   10083.000000\n",
              "max    9.900000e+09  7.700000e+06  ...    6210.000000  871200.000000\n",
              "\n",
              "[8 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEha6dnPjvlo",
        "outputId": "dcd56463-c9f7-464e-8e14-a4c4972f4377"
      },
      "source": [
        "houses.info()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21613 entries, 0 to 21612\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   id             21613 non-null  int64  \n",
            " 1   date           21613 non-null  object \n",
            " 2   price          21613 non-null  float64\n",
            " 3   bedrooms       21613 non-null  int64  \n",
            " 4   bathrooms      21613 non-null  float64\n",
            " 5   sqft_living    21613 non-null  int64  \n",
            " 6   sqft_lot       21613 non-null  int64  \n",
            " 7   floors         21613 non-null  float64\n",
            " 8   waterfront     21613 non-null  int64  \n",
            " 9   view           21613 non-null  int64  \n",
            " 10  condition      21613 non-null  int64  \n",
            " 11  grade          21613 non-null  int64  \n",
            " 12  sqft_above     21613 non-null  int64  \n",
            " 13  sqft_basement  21613 non-null  int64  \n",
            " 14  yr_built       21613 non-null  int64  \n",
            " 15  yr_renovated   21613 non-null  int64  \n",
            " 16  zipcode        21613 non-null  int64  \n",
            " 17  lat            21613 non-null  float64\n",
            " 18  long           21613 non-null  float64\n",
            " 19  sqft_living15  21613 non-null  int64  \n",
            " 20  sqft_lot15     21613 non-null  int64  \n",
            "dtypes: float64(5), int64(15), object(1)\n",
            "memory usage: 3.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1UCg6N9jvlp",
        "outputId": "3658b632-863b-49d3-a764-2ce58b08259f"
      },
      "source": [
        "#Check for na values\n",
        "print(houses.isna().sum())"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "id               0\n",
            "date             0\n",
            "price            0\n",
            "bedrooms         0\n",
            "bathrooms        0\n",
            "sqft_living      0\n",
            "sqft_lot         0\n",
            "floors           0\n",
            "waterfront       0\n",
            "view             0\n",
            "condition        0\n",
            "grade            0\n",
            "sqft_above       0\n",
            "sqft_basement    0\n",
            "yr_built         0\n",
            "yr_renovated     0\n",
            "zipcode          0\n",
            "lat              0\n",
            "long             0\n",
            "sqft_living15    0\n",
            "sqft_lot15       0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "E00FNgJVjvlq",
        "outputId": "a8fb61c9-3434-470c-b31f-ddd1e6580fbd"
      },
      "source": [
        "# check for any correlations between variables\n",
        "corr = houses.corr()\n",
        "sns.heatmap(corr)\n",
        "# sqft_living, grade, sqft_above and sqft_living15 seem to have a \n",
        "# high influence in price"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c06ae42e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kdc3cUjIjvlr"
      },
      "source": [
        "# create x and y\n",
        "#from diagram above we can see tht there are highly correlated with each other and thus \n",
        "#these will be the main aruguments of our datasets\n",
        "#from these later we can create our independent variables which will be used to find y later\n",
        "#feature_cols = ['sqft_living','grade','sqft_above','sqft_living15',\"bathrooms\",'view'] \n",
        "#x = houses[feature_cols] # predictor\n",
        "#y = houses.price # response"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "wZMEzp2ljvls",
        "outputId": "03f80c4a-ff7b-4d7f-e816-7179ff69d81f"
      },
      "source": [
        "houses.drop(['id','date','zipcode'],axis =1,inplace = True )"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-77-c1a7b7d362bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhouses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'zipcode'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\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/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4172\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4173\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4174\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4175\u001b[0m         )\n\u001b[1;32m   4176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3887\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\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   3888\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\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-> 3889\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   3890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3891\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\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/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3921\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3922\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-> 3923\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   3924\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\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   3925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   5285\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\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   5286\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5287\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\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   5288\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5289\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['id' 'date' 'zipcode'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzTutZdXjvlt"
      },
      "source": [
        "\n",
        "x = houses.drop('price',axis = 1,inplace = False)\n",
        "y = houses.price"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "Ebfs8IZvjvlu",
        "outputId": "5dae7fb2-32d6-434d-9c13-fbe30a74f2e3"
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 79,
      "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>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>condition</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>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>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1180</td>\n",
              "      <td>5650</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1180</td>\n",
              "      <td>0</td>\n",
              "      <td>1955</td>\n",
              "      <td>0</td>\n",
              "      <td>47.5112</td>\n",
              "      <td>-122.257</td>\n",
              "      <td>1340</td>\n",
              "      <td>5650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>2.25</td>\n",
              "      <td>2570</td>\n",
              "      <td>7242</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>2170</td>\n",
              "      <td>400</td>\n",
              "      <td>1951</td>\n",
              "      <td>1991</td>\n",
              "      <td>47.7210</td>\n",
              "      <td>-122.319</td>\n",
              "      <td>1690</td>\n",
              "      <td>7639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1.00</td>\n",
              "      <td>770</td>\n",
              "      <td>10000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>770</td>\n",
              "      <td>0</td>\n",
              "      <td>1933</td>\n",
              "      <td>0</td>\n",
              "      <td>47.7379</td>\n",
              "      <td>-122.233</td>\n",
              "      <td>2720</td>\n",
              "      <td>8062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1960</td>\n",
              "      <td>5000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1050</td>\n",
              "      <td>910</td>\n",
              "      <td>1965</td>\n",
              "      <td>0</td>\n",
              "      <td>47.5208</td>\n",
              "      <td>-122.393</td>\n",
              "      <td>1360</td>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1680</td>\n",
              "      <td>8080</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>1680</td>\n",
              "      <td>0</td>\n",
              "      <td>1987</td>\n",
              "      <td>0</td>\n",
              "      <td>47.6168</td>\n",
              "      <td>-122.045</td>\n",
              "      <td>1800</td>\n",
              "      <td>7503</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   bedrooms  bathrooms  sqft_living  ...     long  sqft_living15  sqft_lot15\n",
              "0         3       1.00         1180  ... -122.257           1340        5650\n",
              "1         3       2.25         2570  ... -122.319           1690        7639\n",
              "2         2       1.00          770  ... -122.233           2720        8062\n",
              "3         4       3.00         1960  ... -122.393           1360        5000\n",
              "4         3       2.00         1680  ... -122.045           1800        7503\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QScpxC8Hjvlv",
        "outputId": "e0ae7520-7fc8-4a71-a002-ff16945ec475"
      },
      "source": [
        "y.head()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    221900.0\n",
              "1    538000.0\n",
              "2    180000.0\n",
              "3    604000.0\n",
              "4    510000.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6pWj4G8jvlw"
      },
      "source": [
        "# split data into train and test\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2,random_state = 2) \n",
        "# the test set will be 20% of the whole data set"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3eTMTEkjvlx",
        "outputId": "c752ffdf-12ef-4597-ab05-7ca080a00d42"
      },
      "source": [
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(17290, 17)\n",
            "(4323, 17)\n",
            "(17290,)\n",
            "(4323,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6qWaU07jvly",
        "outputId": "3a59945a-3d82-4e5e-997e-fd29186852f9"
      },
      "source": [
        "# instantiate, fit\n",
        "linreg = LinearRegression()\n",
        "linreg.fit(x_train, y_train)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "feuiItz_jvlz"
      },
      "source": [
        "#Predict\n",
        "#these figures are x wale means \n",
        "#sqft_living\tgrade\tsqft_above\tsqft_living15\tbathrooms\tview these values\n",
        "#linreg.predict(np.array([[1000,5,1000,1000,2,1]]))"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cruwX0S1jvlz",
        "outputId": "80a42ab5-de92-42eb-f600-7917320f1758"
      },
      "source": [
        "#Mean squared error\n",
        "mse = mean_squared_error(y_test, linreg.predict(x_test))\n",
        "print(mse)\n",
        "print(np.sqrt(mse))"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "40161301936.67338\n",
            "200402.8491231434\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lShx3NG9jvl0",
        "outputId": "af8e22b0-a292-4a82-f588-918a2d510210"
      },
      "source": [
        "#Mean absolute error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "mae = mean_absolute_error(y_test, linreg.predict(x_test))\n",
        "print(mae)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "126571.3154957219\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DNFsQhIMjvl1",
        "outputId": "8ebc1918-9f69-4905-e19c-5a44778d06b8"
      },
      "source": [
        "#R2 score\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_test, linreg.predict(x_test))\n",
        "print(r2)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7123284864369755\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TW699yTjjvl2",
        "outputId": "aeb251b3-7651-408d-bf38-72de3d73c537"
      },
      "source": [
        "#Accuracy \n",
        "#score of model is more preferred term\n",
        "print(linreg.score(x_test,y_test))\n",
        "print(\"Accuracy is \",linreg.score(x_test,y_test)*100,\" % \")"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7123284864369755\n",
            "Accuracy is  71.23284864369755  % \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtrxOESpjvl2",
        "outputId": "a5c254f8-c3e3-495d-9b28-84a39306c36c"
      },
      "source": [
        "#Cross validation score\n",
        "scores = cross_val_score(linreg, x, y, cv=10)\n",
        "scores"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.69220712, 0.69346393, 0.68362091, 0.68860914, 0.67797598,\n",
              "       0.69790231, 0.70777426, 0.70216492, 0.69660973, 0.66860173])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z92PewUZjvl3",
        "outputId": "5c1790e0-78af-4917-ca3e-6ea43698c2ea"
      },
      "source": [
        "scores.max()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7077742570503065"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPsxOpaQo0jp"
      },
      "source": [
        "#as we increase the number of the variables we get accuracy as 70 % as compared to  the 59% \r\n",
        "#but still 70# is less\r\n"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKKoTov-trTd"
      },
      "source": [
        "y_pred=linreg.predict(x_test)"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "Vib2oN-9tLFR",
        "outputId": "4960f327-0a58-41be-fb29-310103ab073b"
      },
      "source": [
        "sns.residplot(x=y_test,y=y_pred,color='g')"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c068e1748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBhR3TFxqS1Y"
      },
      "source": [
        ""
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chg0kKnUpGKj"
      },
      "source": [
        ""
      ]
    }
  ]
}