House_Price_Prediction.ipynb

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      "authorship_tag": "ABX9TyMcy38MHOG+f/fwDDBTQCDS",
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kiananasiri/PRODIGY_ML_01/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": "markdown",
      "source": [
        "# House Price Prediction"
      ],
      "metadata": {
        "id": "b7JzvlnESGdM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Imports"
      ],
      "metadata": {
        "id": "b9cvdd_0SA5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import sklearn as sk\n",
        "import  matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error as MSE\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "metadata": {
        "id": "9NoxIlLmJHiW"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_two_scatter(x1 , y1 , x2 , y2 ):\n",
        "  plt.rcParams[\"figure.figsize\"] = (15,15)\n",
        "\n",
        "  plt.subplot(2,2,2)\n",
        "  plt.scatter(x1 , y1 ,c='b')\n",
        "  plt.title('Before Scalling')\n",
        "  plt.grid()\n",
        "\n",
        "  plt.subplot(2,2,1)\n",
        "  plt.scatter(x2 , y2,c='b')\n",
        "  plt.title('After Scalling')\n",
        "  plt.grid()\n",
        "\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "aRHYbWevrp_E"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns' , None)"
      ],
      "metadata": {
        "id": "HLFQzIzCSApk"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Loading and Analyzing"
      ],
      "metadata": {
        "id": "td8ldSZKZAcE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1lNcz5CSVmIz9mKV6BTHqy-fboaVaQ-Nl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OFoceE07MxBP",
        "outputId": "529bf2ae-a97a-4178-89d8-6792e2756304"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1lNcz5CSVmIz9mKV6BTHqy-fboaVaQ-Nl\n",
            "To: /content/house-prices-advanced-regression-techniques.zip\n",
            "\r  0% 0.00/204k [00:00<?, ?B/s]\r100% 204k/204k [00:00<00:00, 91.3MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/house-prices-advanced-regression-techniques.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7k0ThSeOFi-",
        "outputId": "0ca21924-bf34-4ac0-f4db-521082c690ce"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/house-prices-advanced-regression-techniques.zip\n",
            "  inflating: data_description.txt    \n",
            "  inflating: sample_submission.csv   \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv('/content/train.csv')"
      ],
      "metadata": {
        "id": "SI-VIb2iNH6y"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = ['LotArea','BedroomAbvGr' ,'FullBath' , 'HalfBath']"
      ],
      "metadata": {
        "id": "8601R56D3frk"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv('/content/test.csv')\n",
        "x_test = df_test[features]\n",
        "y_test = df_test\n",
        "df_test.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTprPxUhV-eN",
        "outputId": "edb74be9-e89f-4d09-c8b5-86f8ff992aa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
              "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
              "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
              "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
              "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
              "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
              "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
              "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
              "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
              "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
              "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
              "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
              "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
              "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
              "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
              "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
              "       'SaleCondition'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "ml1PXEYaWNxi",
        "outputId": "2df2310a-da91-44a8-9d4a-fa4a2d535271"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
              "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
              "0         Lvl    AllPub    Inside       Gtl        NAmes      Feedr   \n",
              "1         Lvl    AllPub    Corner       Gtl        NAmes       Norm   \n",
              "\n",
              "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
              "0       Norm     1Fam     1Story            5            6       1961   \n",
              "1       Norm     1Fam     1Story            6            6       1958   \n",
              "\n",
              "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
              "0          1961     Gable  CompShg     VinylSd     VinylSd        NaN   \n",
              "1          1958       Hip  CompShg     Wd Sdng     Wd Sdng    BrkFace   \n",
              "\n",
              "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
              "0         0.0        TA        TA     CBlock       TA       TA           No   \n",
              "1       108.0        TA        TA     CBlock       TA       TA           No   \n",
              "\n",
              "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
              "0          Rec       468.0          LwQ       144.0      270.0        882.0   \n",
              "1          ALQ       923.0          Unf         0.0      406.0       1329.0   \n",
              "\n",
              "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
              "0    GasA        TA          Y      SBrkr       896         0             0   \n",
              "1    GasA        TA          Y      SBrkr      1329         0             0   \n",
              "\n",
              "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
              "0        896           0.0           0.0         1         0             2   \n",
              "1       1329           0.0           0.0         1         1             3   \n",
              "\n",
              "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
              "0             1          TA             5        Typ           0         NaN   \n",
              "1             1          Gd             6        Typ           0         NaN   \n",
              "\n",
              "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
              "0     Attchd       1961.0          Unf         1.0       730.0         TA   \n",
              "1     Attchd       1958.0          Unf         1.0       312.0         TA   \n",
              "\n",
              "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
              "0         TA          Y         140            0              0          0   \n",
              "1         TA          Y         393           36              0          0   \n",
              "\n",
              "   ScreenPorch  PoolArea PoolQC  Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
              "0          120         0    NaN  MnPrv         NaN        0       6    2010   \n",
              "1            0         0    NaN    NaN        Gar2    12500       6    2010   \n",
              "\n",
              "  SaleType SaleCondition  \n",
              "0       WD        Normal  \n",
              "1       WD        Normal  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1461</td>\n",
              "      <td>20</td>\n",
              "      <td>RH</td>\n",
              "      <td>80.0</td>\n",
              "      <td>11622</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NAmes</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1961</td>\n",
              "      <td>1961</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>Rec</td>\n",
              "      <td>468.0</td>\n",
              "      <td>LwQ</td>\n",
              "      <td>144.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>882.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>896</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>896</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>5</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1961.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>1.0</td>\n",
              "      <td>730.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>140</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1462</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>81.0</td>\n",
              "      <td>14267</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NAmes</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1958</td>\n",
              "      <td>1958</td>\n",
              "      <td>Hip</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>108.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>923.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>406.0</td>\n",
              "      <td>1329.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1329</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1329</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1958.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>1.0</td>\n",
              "      <td>312.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>393</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gar2</td>\n",
              "      <td>12500</td>\n",
              "      <td>6</td>\n",
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              "      <td>WD</td>\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " df_train.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "sRJx8Fl3WcTo",
        "outputId": "a8221e09-5dc1-4ccd-8212-92431e61a75c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
              "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
              "\n",
              "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
              "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
              "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
              "\n",
              "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
              "0       Norm     1Fam     2Story            7            5       2003   \n",
              "1       Norm     1Fam     1Story            6            8       1976   \n",
              "\n",
              "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
              "0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
              "1          1976     Gable  CompShg     MetalSd     MetalSd        NaN   \n",
              "\n",
              "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
              "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
              "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
              "\n",
              "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
              "0          GLQ         706          Unf           0        150          856   \n",
              "1          ALQ         978          Unf           0        284         1262   \n",
              "\n",
              "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
              "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
              "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
              "\n",
              "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
              "0       1710             1             0         2         1             3   \n",
              "1       1262             0             1         2         0             3   \n",
              "\n",
              "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
              "0             1          Gd             8        Typ           0         NaN   \n",
              "1             1          TA             6        Typ           1          TA   \n",
              "\n",
              "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
              "0     Attchd       2003.0          RFn           2         548         TA   \n",
              "1     Attchd       1976.0          RFn           2         460         TA   \n",
              "\n",
              "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
              "0         TA          Y           0           61              0          0   \n",
              "1         TA          Y         298            0              0          0   \n",
              "\n",
              "   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
              "0            0         0    NaN   NaN         NaN        0       2    2008   \n",
              "1            0         0    NaN   NaN         NaN        0       5    2007   \n",
              "\n",
              "  SaleType SaleCondition  SalePrice  \n",
              "0       WD        Normal     208500  \n",
              "1       WD        Normal     181500  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d834f91f-53a5-42d6-a0f0-5a4212c8788d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
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              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>196.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>706</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>856</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>8</td>\n",
              "      <td>Typ</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>548</td>\n",
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              "      <td>2008</td>\n",
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              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>20</td>\n",
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              "      <td>80.0</td>\n",
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              "      <td>Veenker</td>\n",
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              "      <td>Norm</td>\n",
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              "      <td>MetalSd</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>978</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>284</td>\n",
              "      <td>1262</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>Attchd</td>\n",
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              "      <td>RFn</td>\n",
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              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>5</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train"
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          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.groupby( 'BsmtFullBath' ).count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "5u_-9cliZ64e",
        "outputId": "307938f0-c5cb-4458-d516-391677cb6f50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Id  MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  \\\n",
              "BsmtFullBath                                                                   \n",
              "0             856         856       856          723      856     856     72   \n",
              "1             588         588       588          464      588     588     19   \n",
              "2              15          15        15           13       15      15      0   \n",
              "3               1           1         1            1        1       1      0   \n",
              "\n",
              "              LotShape  LandContour  Utilities  LotConfig  LandSlope  \\\n",
              "BsmtFullBath                                                           \n",
              "0                  856          856        856        856        856   \n",
              "1                  588          588        588        588        588   \n",
              "2                   15           15         15         15         15   \n",
              "3                    1            1          1          1          1   \n",
              "\n",
              "              Neighborhood  Condition1  Condition2  BldgType  HouseStyle  \\\n",
              "BsmtFullBath                                                               \n",
              "0                      856         856         856       856         856   \n",
              "1                      588         588         588       588         588   \n",
              "2                       15          15          15        15          15   \n",
              "3                        1           1           1         1           1   \n",
              "\n",
              "              OverallQual  OverallCond  YearBuilt  YearRemodAdd  RoofStyle  \\\n",
              "BsmtFullBath                                                                 \n",
              "0                     856          856        856           856        856   \n",
              "1                     588          588        588           588        588   \n",
              "2                      15           15         15            15         15   \n",
              "3                       1            1          1             1          1   \n",
              "\n",
              "              RoofMatl  Exterior1st  Exterior2nd  MasVnrType  MasVnrArea  \\\n",
              "BsmtFullBath                                                               \n",
              "0                  856          856          856         300         854   \n",
              "1                  588          588          588         285         582   \n",
              "2                   15           15           15           3          15   \n",
              "3                    1            1            1           0           1   \n",
              "\n",
              "              ExterQual  ExterCond  Foundation  BsmtQual  BsmtCond  \\\n",
              "BsmtFullBath                                                         \n",
              "0                   856        856         856       819       819   \n",
              "1                   588        588         588       588       588   \n",
              "2                    15         15          15        15        15   \n",
              "3                     1          1           1         1         1   \n",
              "\n",
              "              BsmtExposure  BsmtFinType1  BsmtFinSF1  BsmtFinType2  \\\n",
              "BsmtFullBath                                                         \n",
              "0                      818           819         856           819   \n",
              "1                      588           588         588           587   \n",
              "2                       15            15          15            15   \n",
              "3                        1             1           1             1   \n",
              "\n",
              "              BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  Heating  HeatingQC  \\\n",
              "BsmtFullBath                                                           \n",
              "0                    856        856          856      856        856   \n",
              "1                    588        588          588      588        588   \n",
              "2                     15         15           15       15         15   \n",
              "3                      1          1            1        1          1   \n",
              "\n",
              "              CentralAir  Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
              "BsmtFullBath                                                             \n",
              "0                    856         855       856       856           856   \n",
              "1                    588         588       588       588           588   \n",
              "2                     15          15        15        15            15   \n",
              "3                      1           1         1         1             1   \n",
              "\n",
              "              GrLivArea  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
              "BsmtFullBath                                                              \n",
              "0                   856           856       856       856           856   \n",
              "1                   588           588       588       588           588   \n",
              "2                    15            15        15        15            15   \n",
              "3                     1             1         1         1             1   \n",
              "\n",
              "              KitchenAbvGr  KitchenQual  TotRmsAbvGrd  Functional  Fireplaces  \\\n",
              "BsmtFullBath                                                                    \n",
              "0                      856          856           856         856         856   \n",
              "1                      588          588           588         588         588   \n",
              "2                       15           15            15          15          15   \n",
              "3                        1            1             1           1           1   \n",
              "\n",
              "              FireplaceQu  GarageType  GarageYrBlt  GarageFinish  GarageCars  \\\n",
              "BsmtFullBath                                                                   \n",
              "0                     421         798          798           798         856   \n",
              "1                     340         568          568           568         588   \n",
              "2                       9          13           13            13          15   \n",
              "3                       0           0            0             0           1   \n",
              "\n",
              "              GarageArea  GarageQual  GarageCond  PavedDrive  WoodDeckSF  \\\n",
              "BsmtFullBath                                                               \n",
              "0                    856         798         798         856         856   \n",
              "1                    588         568         568         588         588   \n",
              "2                     15          13          13          15          15   \n",
              "3                      1           0           0           1           1   \n",
              "\n",
              "              OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  \\\n",
              "BsmtFullBath                                                                 \n",
              "0                     856            856        856          856       856   \n",
              "1                     588            588        588          588       588   \n",
              "2                      15             15         15           15        15   \n",
              "3                       1              1          1            1         1   \n",
              "\n",
              "              PoolQC  Fence  MiscFeature  MiscVal  MoSold  YrSold  SaleType  \\\n",
              "BsmtFullBath                                                                  \n",
              "0                  1    155           34      856     856     856       856   \n",
              "1                  5    124           18      588     588     588       588   \n",
              "2                  1      2            2       15      15      15        15   \n",
              "3                  0      0            0        1       1       1         1   \n",
              "\n",
              "              SaleCondition  SalePrice  \n",
              "BsmtFullBath                            \n",
              "0                       856        856  \n",
              "1                       588        588  \n",
              "2                        15         15  \n",
              "3                         1          1  "
            ],
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              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>723</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>72</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>300</td>\n",
              "      <td>854</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>819</td>\n",
              "      <td>819</td>\n",
              "      <td>818</td>\n",
              "      <td>819</td>\n",
              "      <td>856</td>\n",
              "      <td>819</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>855</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>421</td>\n",
              "      <td>798</td>\n",
              "      <td>798</td>\n",
              "      <td>798</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>798</td>\n",
              "      <td>798</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>1</td>\n",
              "      <td>155</td>\n",
              "      <td>34</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>464</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>19</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>285</td>\n",
              "      <td>582</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>587</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>340</td>\n",
              "      <td>568</td>\n",
              "      <td>568</td>\n",
              "      <td>568</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>568</td>\n",
              "      <td>568</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>5</td>\n",
              "      <td>124</td>\n",
              "      <td>18</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "      <td>588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-570f2a35-3291-4711-b16e-39fab9b3782a')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-570f2a35-3291-4711-b16e-39fab9b3782a 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-570f2a35-3291-4711-b16e-39fab9b3782a');\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",
              "\n",
              "\n",
              "<div id=\"df-ca5b5dcf-8b08-46a8-ac3e-a2795c9b1c5c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ca5b5dcf-8b08-46a8-ac3e-a2795c9b1c5c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ca5b5dcf-8b08-46a8-ac3e-a2795c9b1c5c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.columns  , 'Shape of train data:' , df_train.shape , 'Shape of test data: ' , df_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1hKU8fENbF9",
        "outputId": "69a36099-8580-4eae-a7dd-3a7b8d267278"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
              "        'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
              "        'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
              "        'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
              "        'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
              "        'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
              "        'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
              "        'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
              "        'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
              "        'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
              "        'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
              "        'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
              "        'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
              "        'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
              "        'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
              "        'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
              "        'SaleCondition', 'SalePrice'],\n",
              "       dtype='object'),\n",
              " 'Shape of train data:',\n",
              " (1460, 81),\n",
              " 'Shape of test data: ',\n",
              " (1459, 80))"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Thus as we see the `SalePrice` feature is not availble in test dataframe"
      ],
      "metadata": {
        "id": "lyhY8Lz6XmHJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "700eIpziNgFt",
        "outputId": "39183ea0-ff66-496c-f6ca-52ca70fff8bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
              "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
              "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
              "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
              "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
              "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
              "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
              "2         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
              "3         Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
              "4         Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
              "\n",
              "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
              "0       Norm     1Fam     2Story            7            5       2003   \n",
              "1       Norm     1Fam     1Story            6            8       1976   \n",
              "2       Norm     1Fam     2Story            7            5       2001   \n",
              "3       Norm     1Fam     2Story            7            5       1915   \n",
              "4       Norm     1Fam     2Story            8            5       2000   \n",
              "\n",
              "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
              "0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
              "1          1976     Gable  CompShg     MetalSd     MetalSd        NaN   \n",
              "2          2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
              "3          1970     Gable  CompShg     Wd Sdng     Wd Shng        NaN   \n",
              "4          2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
              "\n",
              "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
              "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
              "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
              "2       162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
              "3         0.0        TA        TA     BrkTil       TA       Gd           No   \n",
              "4       350.0        Gd        TA      PConc       Gd       TA           Av   \n",
              "\n",
              "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
              "0          GLQ         706          Unf           0        150          856   \n",
              "1          ALQ         978          Unf           0        284         1262   \n",
              "2          GLQ         486          Unf           0        434          920   \n",
              "3          ALQ         216          Unf           0        540          756   \n",
              "4          GLQ         655          Unf           0        490         1145   \n",
              "\n",
              "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
              "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
              "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
              "2    GasA        Ex          Y      SBrkr       920       866             0   \n",
              "3    GasA        Gd          Y      SBrkr       961       756             0   \n",
              "4    GasA        Ex          Y      SBrkr      1145      1053             0   \n",
              "\n",
              "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
              "0       1710             1             0         2         1             3   \n",
              "1       1262             0             1         2         0             3   \n",
              "2       1786             1             0         2         1             3   \n",
              "3       1717             1             0         1         0             3   \n",
              "4       2198             1             0         2         1             4   \n",
              "\n",
              "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
              "0             1          Gd             8        Typ           0         NaN   \n",
              "1             1          TA             6        Typ           1          TA   \n",
              "2             1          Gd             6        Typ           1          TA   \n",
              "3             1          Gd             7        Typ           1          Gd   \n",
              "4             1          Gd             9        Typ           1          TA   \n",
              "\n",
              "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
              "0     Attchd       2003.0          RFn           2         548         TA   \n",
              "1     Attchd       1976.0          RFn           2         460         TA   \n",
              "2     Attchd       2001.0          RFn           2         608         TA   \n",
              "3     Detchd       1998.0          Unf           3         642         TA   \n",
              "4     Attchd       2000.0          RFn           3         836         TA   \n",
              "\n",
              "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
              "0         TA          Y           0           61              0          0   \n",
              "1         TA          Y         298            0              0          0   \n",
              "2         TA          Y           0           42              0          0   \n",
              "3         TA          Y           0           35            272          0   \n",
              "4         TA          Y         192           84              0          0   \n",
              "\n",
              "   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
              "0            0         0    NaN   NaN         NaN        0       2    2008   \n",
              "1            0         0    NaN   NaN         NaN        0       5    2007   \n",
              "2            0         0    NaN   NaN         NaN        0       9    2008   \n",
              "3            0         0    NaN   NaN         NaN        0       2    2006   \n",
              "4            0         0    NaN   NaN         NaN        0      12    2008   \n",
              "\n",
              "  SaleType SaleCondition  SalePrice  \n",
              "0       WD        Normal     208500  \n",
              "1       WD        Normal     181500  \n",
              "2       WD        Normal     223500  \n",
              "3       WD       Abnorml     140000  \n",
              "4       WD        Normal     250000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79cdf58f-34d7-4393-b421-ce9f4953bfa1\" 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>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>196.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>706</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>856</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>8</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>548</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Veenker</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>978</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>284</td>\n",
              "      <td>1262</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>460</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>162.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Mn</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>486</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>434</td>\n",
              "      <td>920</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>608</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>Wd Shng</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>BrkTil</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>216</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>540</td>\n",
              "      <td>756</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>7</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>3</td>\n",
              "      <td>642</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>350.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Av</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>655</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>490</td>\n",
              "      <td>1145</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>9</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>3</td>\n",
              "      <td>836</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-79cdf58f-34d7-4393-b421-ce9f4953bfa1 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-79cdf58f-34d7-4393-b421-ce9f4953bfa1');\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",
              "\n",
              "\n",
              "<div id=\"df-d72b1170-be28-43c6-98fb-7fcebadc8015\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d72b1170-be28-43c6-98fb-7fcebadc8015')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d72b1170-be28-43c6-98fb-7fcebadc8015 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "-tjIOMT1OFjr",
        "outputId": "f8d301e3-675b-4491-e27c-7a4ca7aa3f53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
              "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
              "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
              "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
              "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
              "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
              "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
              "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
              "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
              "\n",
              "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  \\\n",
              "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000   \n",
              "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726   \n",
              "std       1.112799    30.202904     20.645407   181.066207   456.098091   \n",
              "min       1.000000  1872.000000   1950.000000     0.000000     0.000000   \n",
              "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000   \n",
              "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000   \n",
              "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000   \n",
              "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000   \n",
              "\n",
              "        BsmtFinSF2    BsmtUnfSF  TotalBsmtSF     1stFlrSF     2ndFlrSF  \\\n",
              "count  1460.000000  1460.000000  1460.000000  1460.000000  1460.000000   \n",
              "mean     46.549315   567.240411  1057.429452  1162.626712   346.992466   \n",
              "std     161.319273   441.866955   438.705324   386.587738   436.528436   \n",
              "min       0.000000     0.000000     0.000000   334.000000     0.000000   \n",
              "25%       0.000000   223.000000   795.750000   882.000000     0.000000   \n",
              "50%       0.000000   477.500000   991.500000  1087.000000     0.000000   \n",
              "75%       0.000000   808.000000  1298.250000  1391.250000   728.000000   \n",
              "max    1474.000000  2336.000000  6110.000000  4692.000000  2065.000000   \n",
              "\n",
              "       LowQualFinSF    GrLivArea  BsmtFullBath  BsmtHalfBath     FullBath  \\\n",
              "count   1460.000000  1460.000000   1460.000000   1460.000000  1460.000000   \n",
              "mean       5.844521  1515.463699      0.425342      0.057534     1.565068   \n",
              "std       48.623081   525.480383      0.518911      0.238753     0.550916   \n",
              "min        0.000000   334.000000      0.000000      0.000000     0.000000   \n",
              "25%        0.000000  1129.500000      0.000000      0.000000     1.000000   \n",
              "50%        0.000000  1464.000000      0.000000      0.000000     2.000000   \n",
              "75%        0.000000  1776.750000      1.000000      0.000000     2.000000   \n",
              "max      572.000000  5642.000000      3.000000      2.000000     3.000000   \n",
              "\n",
              "          HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd   Fireplaces  \\\n",
              "count  1460.000000   1460.000000   1460.000000   1460.000000  1460.000000   \n",
              "mean      0.382877      2.866438      1.046575      6.517808     0.613014   \n",
              "std       0.502885      0.815778      0.220338      1.625393     0.644666   \n",
              "min       0.000000      0.000000      0.000000      2.000000     0.000000   \n",
              "25%       0.000000      2.000000      1.000000      5.000000     0.000000   \n",
              "50%       0.000000      3.000000      1.000000      6.000000     1.000000   \n",
              "75%       1.000000      3.000000      1.000000      7.000000     1.000000   \n",
              "max       2.000000      8.000000      3.000000     14.000000     3.000000   \n",
              "\n",
              "       GarageYrBlt   GarageCars   GarageArea   WoodDeckSF  OpenPorchSF  \\\n",
              "count  1379.000000  1460.000000  1460.000000  1460.000000  1460.000000   \n",
              "mean   1978.506164     1.767123   472.980137    94.244521    46.660274   \n",
              "std      24.689725     0.747315   213.804841   125.338794    66.256028   \n",
              "min    1900.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%    1961.000000     1.000000   334.500000     0.000000     0.000000   \n",
              "50%    1980.000000     2.000000   480.000000     0.000000    25.000000   \n",
              "75%    2002.000000     2.000000   576.000000   168.000000    68.000000   \n",
              "max    2010.000000     4.000000  1418.000000   857.000000   547.000000   \n",
              "\n",
              "       EnclosedPorch    3SsnPorch  ScreenPorch     PoolArea       MiscVal  \\\n",
              "count    1460.000000  1460.000000  1460.000000  1460.000000   1460.000000   \n",
              "mean       21.954110     3.409589    15.060959     2.758904     43.489041   \n",
              "std        61.119149    29.317331    55.757415    40.177307    496.123024   \n",
              "min         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
              "25%         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
              "50%         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
              "75%         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
              "max       552.000000   508.000000   480.000000   738.000000  15500.000000   \n",
              "\n",
              "            MoSold       YrSold      SalePrice  \n",
              "count  1460.000000  1460.000000    1460.000000  \n",
              "mean      6.321918  2007.815753  180921.195890  \n",
              "std       2.703626     1.328095   79442.502883  \n",
              "min       1.000000  2006.000000   34900.000000  \n",
              "25%       5.000000  2007.000000  129975.000000  \n",
              "50%       6.000000  2008.000000  163000.000000  \n",
              "75%       8.000000  2009.000000  214000.000000  \n",
              "max      12.000000  2010.000000  755000.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c4765b6-e794-4b5d-8d08-3313aa1f0e80\" 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>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1201.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1452.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1379.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>730.500000</td>\n",
              "      <td>56.897260</td>\n",
              "      <td>70.049958</td>\n",
              "      <td>10516.828082</td>\n",
              "      <td>6.099315</td>\n",
              "      <td>5.575342</td>\n",
              "      <td>1971.267808</td>\n",
              "      <td>1984.865753</td>\n",
              "      <td>103.685262</td>\n",
              "      <td>443.639726</td>\n",
              "      <td>46.549315</td>\n",
              "      <td>567.240411</td>\n",
              "      <td>1057.429452</td>\n",
              "      <td>1162.626712</td>\n",
              "      <td>346.992466</td>\n",
              "      <td>5.844521</td>\n",
              "      <td>1515.463699</td>\n",
              "      <td>0.425342</td>\n",
              "      <td>0.057534</td>\n",
              "      <td>1.565068</td>\n",
              "      <td>0.382877</td>\n",
              "      <td>2.866438</td>\n",
              "      <td>1.046575</td>\n",
              "      <td>6.517808</td>\n",
              "      <td>0.613014</td>\n",
              "      <td>1978.506164</td>\n",
              "      <td>1.767123</td>\n",
              "      <td>472.980137</td>\n",
              "      <td>94.244521</td>\n",
              "      <td>46.660274</td>\n",
              "      <td>21.954110</td>\n",
              "      <td>3.409589</td>\n",
              "      <td>15.060959</td>\n",
              "      <td>2.758904</td>\n",
              "      <td>43.489041</td>\n",
              "      <td>6.321918</td>\n",
              "      <td>2007.815753</td>\n",
              "      <td>180921.195890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>421.610009</td>\n",
              "      <td>42.300571</td>\n",
              "      <td>24.284752</td>\n",
              "      <td>9981.264932</td>\n",
              "      <td>1.382997</td>\n",
              "      <td>1.112799</td>\n",
              "      <td>30.202904</td>\n",
              "      <td>20.645407</td>\n",
              "      <td>181.066207</td>\n",
              "      <td>456.098091</td>\n",
              "      <td>161.319273</td>\n",
              "      <td>441.866955</td>\n",
              "      <td>438.705324</td>\n",
              "      <td>386.587738</td>\n",
              "      <td>436.528436</td>\n",
              "      <td>48.623081</td>\n",
              "      <td>525.480383</td>\n",
              "      <td>0.518911</td>\n",
              "      <td>0.238753</td>\n",
              "      <td>0.550916</td>\n",
              "      <td>0.502885</td>\n",
              "      <td>0.815778</td>\n",
              "      <td>0.220338</td>\n",
              "      <td>1.625393</td>\n",
              "      <td>0.644666</td>\n",
              "      <td>24.689725</td>\n",
              "      <td>0.747315</td>\n",
              "      <td>213.804841</td>\n",
              "      <td>125.338794</td>\n",
              "      <td>66.256028</td>\n",
              "      <td>61.119149</td>\n",
              "      <td>29.317331</td>\n",
              "      <td>55.757415</td>\n",
              "      <td>40.177307</td>\n",
              "      <td>496.123024</td>\n",
              "      <td>2.703626</td>\n",
              "      <td>1.328095</td>\n",
              "      <td>79442.502883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>1300.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1872.000000</td>\n",
              "      <td>1950.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2006.000000</td>\n",
              "      <td>34900.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>365.750000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>7553.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1954.000000</td>\n",
              "      <td>1967.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>795.750000</td>\n",
              "      <td>882.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1129.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1961.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>334.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2007.000000</td>\n",
              "      <td>129975.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>730.500000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>9478.500000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1973.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>383.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>477.500000</td>\n",
              "      <td>991.500000</td>\n",
              "      <td>1087.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1464.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1980.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2008.000000</td>\n",
              "      <td>163000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1095.250000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>11601.500000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>712.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>808.000000</td>\n",
              "      <td>1298.250000</td>\n",
              "      <td>1391.250000</td>\n",
              "      <td>728.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1776.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2002.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>576.000000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2009.000000</td>\n",
              "      <td>214000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1460.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>313.000000</td>\n",
              "      <td>215245.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>1600.000000</td>\n",
              "      <td>5644.000000</td>\n",
              "      <td>1474.000000</td>\n",
              "      <td>2336.000000</td>\n",
              "      <td>6110.000000</td>\n",
              "      <td>4692.000000</td>\n",
              "      <td>2065.000000</td>\n",
              "      <td>572.000000</td>\n",
              "      <td>5642.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1418.000000</td>\n",
              "      <td>857.000000</td>\n",
              "      <td>547.000000</td>\n",
              "      <td>552.000000</td>\n",
              "      <td>508.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>738.000000</td>\n",
              "      <td>15500.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>755000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-1c4765b6-e794-4b5d-8d08-3313aa1f0e80 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-1c4765b6-e794-4b5d-8d08-3313aa1f0e80');\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",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
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              "  }\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5dcb06b5-85f8-4e60-93cb-01c667f0a9e0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n = {}\n",
        "obj = df_train.isnull().sum()\n",
        "for feature , nulls in obj.items():\n",
        "  n[feature] = nulls\n",
        "n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gidgGW90PBdx",
        "outputId": "5b054315-4fd4-4831-c7a4-35d9502c1bc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Id': 0,\n",
              " 'MSSubClass': 0,\n",
              " 'MSZoning': 0,\n",
              " 'LotFrontage': 259,\n",
              " 'LotArea': 0,\n",
              " 'Street': 0,\n",
              " 'Alley': 1369,\n",
              " 'LotShape': 0,\n",
              " 'LandContour': 0,\n",
              " 'Utilities': 0,\n",
              " 'LotConfig': 0,\n",
              " 'LandSlope': 0,\n",
              " 'Neighborhood': 0,\n",
              " 'Condition1': 0,\n",
              " 'Condition2': 0,\n",
              " 'BldgType': 0,\n",
              " 'HouseStyle': 0,\n",
              " 'OverallQual': 0,\n",
              " 'OverallCond': 0,\n",
              " 'YearBuilt': 0,\n",
              " 'YearRemodAdd': 0,\n",
              " 'RoofStyle': 0,\n",
              " 'RoofMatl': 0,\n",
              " 'Exterior1st': 0,\n",
              " 'Exterior2nd': 0,\n",
              " 'MasVnrType': 872,\n",
              " 'MasVnrArea': 8,\n",
              " 'ExterQual': 0,\n",
              " 'ExterCond': 0,\n",
              " 'Foundation': 0,\n",
              " 'BsmtQual': 37,\n",
              " 'BsmtCond': 37,\n",
              " 'BsmtExposure': 38,\n",
              " 'BsmtFinType1': 37,\n",
              " 'BsmtFinSF1': 0,\n",
              " 'BsmtFinType2': 38,\n",
              " 'BsmtFinSF2': 0,\n",
              " 'BsmtUnfSF': 0,\n",
              " 'TotalBsmtSF': 0,\n",
              " 'Heating': 0,\n",
              " 'HeatingQC': 0,\n",
              " 'CentralAir': 0,\n",
              " 'Electrical': 1,\n",
              " '1stFlrSF': 0,\n",
              " '2ndFlrSF': 0,\n",
              " 'LowQualFinSF': 0,\n",
              " 'GrLivArea': 0,\n",
              " 'BsmtFullBath': 0,\n",
              " 'BsmtHalfBath': 0,\n",
              " 'FullBath': 0,\n",
              " 'HalfBath': 0,\n",
              " 'BedroomAbvGr': 0,\n",
              " 'KitchenAbvGr': 0,\n",
              " 'KitchenQual': 0,\n",
              " 'TotRmsAbvGrd': 0,\n",
              " 'Functional': 0,\n",
              " 'Fireplaces': 0,\n",
              " 'FireplaceQu': 690,\n",
              " 'GarageType': 81,\n",
              " 'GarageYrBlt': 81,\n",
              " 'GarageFinish': 81,\n",
              " 'GarageCars': 0,\n",
              " 'GarageArea': 0,\n",
              " 'GarageQual': 81,\n",
              " 'GarageCond': 81,\n",
              " 'PavedDrive': 0,\n",
              " 'WoodDeckSF': 0,\n",
              " 'OpenPorchSF': 0,\n",
              " 'EnclosedPorch': 0,\n",
              " '3SsnPorch': 0,\n",
              " 'ScreenPorch': 0,\n",
              " 'PoolArea': 0,\n",
              " 'PoolQC': 1453,\n",
              " 'Fence': 1179,\n",
              " 'MiscFeature': 1406,\n",
              " 'MiscVal': 0,\n",
              " 'MoSold': 0,\n",
              " 'YrSold': 0,\n",
              " 'SaleType': 0,\n",
              " 'SaleCondition': 0,\n",
              " 'SalePrice': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.groupby('PoolQC').count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "wKKzFJbcMX4s",
        "outputId": "7b09ab75-dd23-4864-ae31-8f3689d6f17a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id  MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  \\\n",
              "PoolQC                                                                  \n",
              "Ex       2           2         2            2        2       2      0   \n",
              "Fa       2           2         2            2        2       2      0   \n",
              "Gd       3           3         3            2        3       3      0   \n",
              "\n",
              "        LotShape  LandContour  Utilities  LotConfig  LandSlope  Neighborhood  \\\n",
              "PoolQC                                                                         \n",
              "Ex             2            2          2          2          2             2   \n",
              "Fa             2            2          2          2          2             2   \n",
              "Gd             3            3          3          3          3             3   \n",
              "\n",
              "        Condition1  Condition2  BldgType  HouseStyle  OverallQual  \\\n",
              "PoolQC                                                              \n",
              "Ex               2           2         2           2            2   \n",
              "Fa               2           2         2           2            2   \n",
              "Gd               3           3         3           3            3   \n",
              "\n",
              "        OverallCond  YearBuilt  YearRemodAdd  RoofStyle  RoofMatl  \\\n",
              "PoolQC                                                              \n",
              "Ex                2          2             2          2         2   \n",
              "Fa                2          2             2          2         2   \n",
              "Gd                3          3             3          3         3   \n",
              "\n",
              "        Exterior1st  Exterior2nd  MasVnrType  MasVnrArea  ExterQual  \\\n",
              "PoolQC                                                                \n",
              "Ex                2            2           0           2          2   \n",
              "Fa                2            2           2           2          2   \n",
              "Gd                3            3           1           3          3   \n",
              "\n",
              "        ExterCond  Foundation  BsmtQual  BsmtCond  BsmtExposure  BsmtFinType1  \\\n",
              "PoolQC                                                                          \n",
              "Ex              2           2         2         2             2             2   \n",
              "Fa              2           2         2         2             2             2   \n",
              "Gd              3           3         3         3             3             3   \n",
              "\n",
              "        BsmtFinSF1  BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  Heating  \\\n",
              "PoolQC                                                                          \n",
              "Ex               2             2           2          2            2        2   \n",
              "Fa               2             2           2          2            2        2   \n",
              "Gd               3             3           3          3            3        3   \n",
              "\n",
              "        HeatingQC  CentralAir  Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
              "PoolQC                                                                        \n",
              "Ex              2           2           2         2         2             2   \n",
              "Fa              2           2           2         2         2             2   \n",
              "Gd              3           3           3         3         3             3   \n",
              "\n",
              "        GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  \\\n",
              "PoolQC                                                              \n",
              "Ex              2             2             2         2         2   \n",
              "Fa              2             2             2         2         2   \n",
              "Gd              3             3             3         3         3   \n",
              "\n",
              "        BedroomAbvGr  KitchenAbvGr  KitchenQual  TotRmsAbvGrd  Functional  \\\n",
              "PoolQC                                                                      \n",
              "Ex                 2             2            2             2           2   \n",
              "Fa                 2             2            2             2           2   \n",
              "Gd                 3             3            3             3           3   \n",
              "\n",
              "        Fireplaces  FireplaceQu  GarageType  GarageYrBlt  GarageFinish  \\\n",
              "PoolQC                                                                   \n",
              "Ex               2            2           2            2             2   \n",
              "Fa               2            2           2            2             2   \n",
              "Gd               3            3           3            3             3   \n",
              "\n",
              "        GarageCars  GarageArea  GarageQual  GarageCond  PavedDrive  \\\n",
              "PoolQC                                                               \n",
              "Ex               2           2           2           2           2   \n",
              "Fa               2           2           2           2           2   \n",
              "Gd               3           3           3           3           3   \n",
              "\n",
              "        WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
              "PoolQC                                                                   \n",
              "Ex               2            2              2          2            2   \n",
              "Fa               2            2              2          2            2   \n",
              "Gd               3            3              3          3            3   \n",
              "\n",
              "        PoolArea  Fence  MiscFeature  MiscVal  MoSold  YrSold  SaleType  \\\n",
              "PoolQC                                                                    \n",
              "Ex             2      2            0        2       2       2         2   \n",
              "Fa             2      2            1        2       2       2         2   \n",
              "Gd             3      2            0        3       3       3         3   \n",
              "\n",
              "        SaleCondition  SalePrice  \n",
              "PoolQC                            \n",
              "Ex                  2          2  \n",
              "Fa                  2          2  \n",
              "Gd                  3          3  "
            ],
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              "    <div>\n",
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              "      <th>MSSubClass</th>\n",
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              "      <th>LowQualFinSF</th>\n",
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              "      <th>KitchenQual</th>\n",
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              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
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              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
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              "      <th>GarageCond</th>\n",
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              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PoolQC</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ex</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fa</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gd</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04602fa4-9008-4f79-864b-7ee496edf8c4')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-04602fa4-9008-4f79-864b-7ee496edf8c4 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-04602fa4-9008-4f79-864b-7ee496edf8c4');\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",
              "\n",
              "\n",
              "<div id=\"df-535eab32-f9bf-467a-b5f3-8b45f09edeb2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-535eab32-f9bf-467a-b5f3-8b45f09edeb2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-535eab32-f9bf-467a-b5f3-8b45f09edeb2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.groupby('Fence').count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "8joWnmLEMsMe",
        "outputId": "f1a931dc-5495-4598-d625-047d869df26a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id  MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  \\\n",
              "Fence                                                                   \n",
              "GdPrv   59          59        59           48       59      59      5   \n",
              "GdWo    54          54        54           42       54      54      3   \n",
              "MnPrv  157         157       157          128      157     157      9   \n",
              "MnWw    11          11        11           10       11      11      1   \n",
              "\n",
              "       LotShape  LandContour  Utilities  LotConfig  LandSlope  Neighborhood  \\\n",
              "Fence                                                                         \n",
              "GdPrv        59           59         59         59         59            59   \n",
              "GdWo         54           54         54         54         54            54   \n",
              "MnPrv       157          157        157        157        157           157   \n",
              "MnWw         11           11         11         11         11            11   \n",
              "\n",
              "       Condition1  Condition2  BldgType  HouseStyle  OverallQual  OverallCond  \\\n",
              "Fence                                                                           \n",
              "GdPrv          59          59        59          59           59           59   \n",
              "GdWo           54          54        54          54           54           54   \n",
              "MnPrv         157         157       157         157          157          157   \n",
              "MnWw           11          11        11          11           11           11   \n",
              "\n",
              "       YearBuilt  YearRemodAdd  RoofStyle  RoofMatl  Exterior1st  Exterior2nd  \\\n",
              "Fence                                                                           \n",
              "GdPrv         59            59         59        59           59           59   \n",
              "GdWo          54            54         54        54           54           54   \n",
              "MnPrv        157           157        157       157          157          157   \n",
              "MnWw          11            11         11        11           11           11   \n",
              "\n",
              "       MasVnrType  MasVnrArea  ExterQual  ExterCond  Foundation  BsmtQual  \\\n",
              "Fence                                                                       \n",
              "GdPrv          22          59         59         59          59        58   \n",
              "GdWo           21          54         54         54          54        54   \n",
              "MnPrv          46         157        157        157         157       154   \n",
              "MnWw            4          11         11         11          11        11   \n",
              "\n",
              "       BsmtCond  BsmtExposure  BsmtFinType1  BsmtFinSF1  BsmtFinType2  \\\n",
              "Fence                                                                   \n",
              "GdPrv        58            58            58          59            58   \n",
              "GdWo         54            54            54          54            54   \n",
              "MnPrv       154           154           154         157           154   \n",
              "MnWw         11            11            11          11            11   \n",
              "\n",
              "       BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  Heating  HeatingQC  CentralAir  \\\n",
              "Fence                                                                       \n",
              "GdPrv          59         59           59       59         59          59   \n",
              "GdWo           54         54           54       54         54          54   \n",
              "MnPrv         157        157          157      157        157         157   \n",
              "MnWw           11         11           11       11         11          11   \n",
              "\n",
              "       Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  \\\n",
              "Fence                                                                          \n",
              "GdPrv          59        59        59            59         59            59   \n",
              "GdWo           54        54        54            54         54            54   \n",
              "MnPrv         157       157       157           157        157           157   \n",
              "MnWw           11        11        11            11         11            11   \n",
              "\n",
              "       BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  \\\n",
              "Fence                                                                 \n",
              "GdPrv            59        59        59            59            59   \n",
              "GdWo             54        54        54            54            54   \n",
              "MnPrv           157       157       157           157           157   \n",
              "MnWw             11        11        11            11            11   \n",
              "\n",
              "       KitchenQual  TotRmsAbvGrd  Functional  Fireplaces  FireplaceQu  \\\n",
              "Fence                                                                   \n",
              "GdPrv           59            59          59          59           36   \n",
              "GdWo            54            54          54          54           23   \n",
              "MnPrv          157           157         157         157           68   \n",
              "MnWw            11            11          11          11            3   \n",
              "\n",
              "       GarageType  GarageYrBlt  GarageFinish  GarageCars  GarageArea  \\\n",
              "Fence                                                                  \n",
              "GdPrv          58           58            58          59          59   \n",
              "GdWo           50           50            50          54          54   \n",
              "MnPrv         146          146           146         157         157   \n",
              "MnWw           11           11            11          11          11   \n",
              "\n",
              "       GarageQual  GarageCond  PavedDrive  WoodDeckSF  OpenPorchSF  \\\n",
              "Fence                                                                \n",
              "GdPrv          58          58          59          59           59   \n",
              "GdWo           50          50          54          54           54   \n",
              "MnPrv         146         146         157         157          157   \n",
              "MnWw           11          11          11          11           11   \n",
              "\n",
              "       EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  PoolQC  MiscFeature  \\\n",
              "Fence                                                                         \n",
              "GdPrv             59         59           59        59       4            5   \n",
              "GdWo              54         54           54        54       0            4   \n",
              "MnPrv            157        157          157       157       2           14   \n",
              "MnWw              11         11           11        11       0            1   \n",
              "\n",
              "       MiscVal  MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
              "Fence                                                               \n",
              "GdPrv       59      59      59        59             59         59  \n",
              "GdWo        54      54      54        54             54         54  \n",
              "MnPrv      157     157     157       157            157        157  \n",
              "MnWw        11      11      11        11             11         11  "
            ],
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              "\n",
              "  <div id=\"df-6f904bd5-6498-438b-95a8-67a14561b631\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fence</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>GdPrv</th>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>48</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>5</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>22</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>58</td>\n",
              "      <td>58</td>\n",
              "      <td>58</td>\n",
              "      <td>58</td>\n",
              "      <td>59</td>\n",
              "      <td>58</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>36</td>\n",
              "      <td>58</td>\n",
              "      <td>58</td>\n",
              "      <td>58</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>58</td>\n",
              "      <td>58</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GdWo</th>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>42</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>3</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>21</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>23</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MnPrv</th>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>128</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>9</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>46</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>154</td>\n",
              "      <td>154</td>\n",
              "      <td>154</td>\n",
              "      <td>154</td>\n",
              "      <td>157</td>\n",
              "      <td>154</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>68</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "      <td>157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MnWw</th>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f904bd5-6498-438b-95a8-67a14561b631')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6f904bd5-6498-438b-95a8-67a14561b631 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-6f904bd5-6498-438b-95a8-67a14561b631');\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",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4142dbec-d7a8-4dcb-81c2-bb3a0e3fe79f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "objects_of_data = df_train.select_dtypes('object').columns\n",
        "objects_of_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6jK2FW8Augh",
        "outputId": "77d13cff-f39d-4312-b62c-1c7e7ce98f94"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',\n",
              "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
              "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
              "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
              "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
              "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
              "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
              "       'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',\n",
              "       'SaleType', 'SaleCondition'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "enc = LabelEncoder()\n",
        "\n",
        "for i in df_train.columns:\n",
        "  if i in objects_of_data:\n",
        "    df_train[i] = enc.fit_transform(df_train[i])\n"
      ],
      "metadata": {
        "id": "X8hPcJlCBKBG"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.select_dtypes('object')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "QmSp6sjpC-NF",
        "outputId": "2473c0f3-053b-4325-e7a4-3074f70178e8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "[1460 rows x 0 columns]"
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              "          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",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "      const quickchartButtonEl =\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b9369837-8c1b-43d9-9d63-f1f8b963eddf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 1460,\n  \"fields\": []\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After labeling PoolQC feature:\n",
        "\n",
        "Fourth type is added which was null before"
      ],
      "metadata": {
        "id": "2VV2MgKiM6H-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.groupby('PoolQC').count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "toRXr-DrM3DR",
        "outputId": "bf7ff848-adb2-4eae-873a-d7f26a1819ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Id  MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  \\\n",
              "PoolQC                                                                    \n",
              "0          2           2         2            2        2       2      2   \n",
              "1          2           2         2            2        2       2      2   \n",
              "2          3           3         3            2        3       3      3   \n",
              "3       1453        1453      1453         1195     1453    1453   1453   \n",
              "\n",
              "        LotShape  LandContour  Utilities  LotConfig  LandSlope  Neighborhood  \\\n",
              "PoolQC                                                                         \n",
              "0              2            2          2          2          2             2   \n",
              "1              2            2          2          2          2             2   \n",
              "2              3            3          3          3          3             3   \n",
              "3           1453         1453       1453       1453       1453          1453   \n",
              "\n",
              "        Condition1  Condition2  BldgType  HouseStyle  OverallQual  \\\n",
              "PoolQC                                                              \n",
              "0                2           2         2           2            2   \n",
              "1                2           2         2           2            2   \n",
              "2                3           3         3           3            3   \n",
              "3             1453        1453      1453        1453         1453   \n",
              "\n",
              "        OverallCond  YearBuilt  YearRemodAdd  RoofStyle  RoofMatl  \\\n",
              "PoolQC                                                              \n",
              "0                 2          2             2          2         2   \n",
              "1                 2          2             2          2         2   \n",
              "2                 3          3             3          3         3   \n",
              "3              1453       1453          1453       1453      1453   \n",
              "\n",
              "        Exterior1st  Exterior2nd  MasVnrType  MasVnrArea  ExterQual  \\\n",
              "PoolQC                                                                \n",
              "0                 2            2           2           2          2   \n",
              "1                 2            2           2           2          2   \n",
              "2                 3            3           3           3          3   \n",
              "3              1453         1453        1453        1445       1453   \n",
              "\n",
              "        ExterCond  Foundation  BsmtQual  BsmtCond  BsmtExposure  BsmtFinType1  \\\n",
              "PoolQC                                                                          \n",
              "0               2           2         2         2             2             2   \n",
              "1               2           2         2         2             2             2   \n",
              "2               3           3         3         3             3             3   \n",
              "3            1453        1453      1453      1453          1453          1453   \n",
              "\n",
              "        BsmtFinSF1  BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  Heating  \\\n",
              "PoolQC                                                                          \n",
              "0                2             2           2          2            2        2   \n",
              "1                2             2           2          2            2        2   \n",
              "2                3             3           3          3            3        3   \n",
              "3             1453          1453        1453       1453         1453     1453   \n",
              "\n",
              "        HeatingQC  CentralAir  Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
              "PoolQC                                                                        \n",
              "0               2           2           2         2         2             2   \n",
              "1               2           2           2         2         2             2   \n",
              "2               3           3           3         3         3             3   \n",
              "3            1453        1453        1453      1453      1453          1453   \n",
              "\n",
              "        GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  \\\n",
              "PoolQC                                                              \n",
              "0               2             2             2         2         2   \n",
              "1               2             2             2         2         2   \n",
              "2               3             3             3         3         3   \n",
              "3            1453          1453          1453      1453      1453   \n",
              "\n",
              "        BedroomAbvGr  KitchenAbvGr  KitchenQual  TotRmsAbvGrd  Functional  \\\n",
              "PoolQC                                                                      \n",
              "0                  2             2            2             2           2   \n",
              "1                  2             2            2             2           2   \n",
              "2                  3             3            3             3           3   \n",
              "3               1453          1453         1453          1453        1453   \n",
              "\n",
              "        Fireplaces  FireplaceQu  GarageType  GarageYrBlt  GarageFinish  \\\n",
              "PoolQC                                                                   \n",
              "0                2            2           2            2             2   \n",
              "1                2            2           2            2             2   \n",
              "2                3            3           3            3             3   \n",
              "3             1453         1453        1453         1372          1453   \n",
              "\n",
              "        GarageCars  GarageArea  GarageQual  GarageCond  PavedDrive  \\\n",
              "PoolQC                                                               \n",
              "0                2           2           2           2           2   \n",
              "1                2           2           2           2           2   \n",
              "2                3           3           3           3           3   \n",
              "3             1453        1453        1453        1453        1453   \n",
              "\n",
              "        WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
              "PoolQC                                                                   \n",
              "0                2            2              2          2            2   \n",
              "1                2            2              2          2            2   \n",
              "2                3            3              3          3            3   \n",
              "3             1453         1453           1453       1453         1453   \n",
              "\n",
              "        PoolArea  Fence  MiscFeature  MiscVal  MoSold  YrSold  SaleType  \\\n",
              "PoolQC                                                                    \n",
              "0              2      2            2        2       2       2         2   \n",
              "1              2      2            2        2       2       2         2   \n",
              "2              3      3            3        3       3       3         3   \n",
              "3           1453   1453         1453     1453    1453    1453      1453   \n",
              "\n",
              "        SaleCondition  SalePrice  \n",
              "PoolQC                            \n",
              "0                   2          2  \n",
              "1                   2          2  \n",
              "2                   3          3  \n",
              "3                1453       1453  "
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1195</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1445</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1372</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "      <td>1453</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1d9e727-63e2-48d6-a816-a5bff0d917de')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-d1d9e727-63e2-48d6-a816-a5bff0d917de 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-d1d9e727-63e2-48d6-a816-a5bff0d917de');\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",
              "\n",
              "\n",
              "<div id=\"df-3e851017-b37e-4949-aa7f-49ec35416adf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e851017-b37e-4949-aa7f-49ec35416adf')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3e851017-b37e-4949-aa7f-49ec35416adf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing Id beacuse it wouldn't naturally affect price of houses"
      ],
      "metadata": {
        "id": "XxWsY54HNYRH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_train.drop(columns=['Id'])"
      ],
      "metadata": {
        "id": "LiB8VWlMLEqC"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('shape of data with null values' , df_train.shape)\n",
        "df_train = df_train.dropna()\n",
        "print('shape of data after removing null values' , df_train.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcoTvRE_DZzY",
        "outputId": "82a2d329-febd-4c8e-e7e3-b06a445a70e5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of data with null values (1121, 80)\n",
            "shape of data after removing null values (1121, 80)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n = {}\n",
        "obj = df_train.isnull().sum()\n",
        "for feature , nulls in obj.items():\n",
        "  n[feature] = nulls\n",
        "n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jUyIlHHDFkCh",
        "outputId": "d7899015-50f1-487c-8217-bc2c705924b3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'MSSubClass': 0,\n",
              " 'MSZoning': 0,\n",
              " 'LotFrontage': 0,\n",
              " 'LotArea': 0,\n",
              " 'Street': 0,\n",
              " 'Alley': 0,\n",
              " 'LotShape': 0,\n",
              " 'LandContour': 0,\n",
              " 'Utilities': 0,\n",
              " 'LotConfig': 0,\n",
              " 'LandSlope': 0,\n",
              " 'Neighborhood': 0,\n",
              " 'Condition1': 0,\n",
              " 'Condition2': 0,\n",
              " 'BldgType': 0,\n",
              " 'HouseStyle': 0,\n",
              " 'OverallQual': 0,\n",
              " 'OverallCond': 0,\n",
              " 'YearBuilt': 0,\n",
              " 'YearRemodAdd': 0,\n",
              " 'RoofStyle': 0,\n",
              " 'RoofMatl': 0,\n",
              " 'Exterior1st': 0,\n",
              " 'Exterior2nd': 0,\n",
              " 'MasVnrType': 0,\n",
              " 'MasVnrArea': 0,\n",
              " 'ExterQual': 0,\n",
              " 'ExterCond': 0,\n",
              " 'Foundation': 0,\n",
              " 'BsmtQual': 0,\n",
              " 'BsmtCond': 0,\n",
              " 'BsmtExposure': 0,\n",
              " 'BsmtFinType1': 0,\n",
              " 'BsmtFinSF1': 0,\n",
              " 'BsmtFinType2': 0,\n",
              " 'BsmtFinSF2': 0,\n",
              " 'BsmtUnfSF': 0,\n",
              " 'TotalBsmtSF': 0,\n",
              " 'Heating': 0,\n",
              " 'HeatingQC': 0,\n",
              " 'CentralAir': 0,\n",
              " 'Electrical': 0,\n",
              " '1stFlrSF': 0,\n",
              " '2ndFlrSF': 0,\n",
              " 'LowQualFinSF': 0,\n",
              " 'GrLivArea': 0,\n",
              " 'BsmtFullBath': 0,\n",
              " 'BsmtHalfBath': 0,\n",
              " 'FullBath': 0,\n",
              " 'HalfBath': 0,\n",
              " 'BedroomAbvGr': 0,\n",
              " 'KitchenAbvGr': 0,\n",
              " 'KitchenQual': 0,\n",
              " 'TotRmsAbvGrd': 0,\n",
              " 'Functional': 0,\n",
              " 'Fireplaces': 0,\n",
              " 'FireplaceQu': 0,\n",
              " 'GarageType': 0,\n",
              " 'GarageYrBlt': 0,\n",
              " 'GarageFinish': 0,\n",
              " 'GarageCars': 0,\n",
              " 'GarageArea': 0,\n",
              " 'GarageQual': 0,\n",
              " 'GarageCond': 0,\n",
              " 'PavedDrive': 0,\n",
              " 'WoodDeckSF': 0,\n",
              " 'OpenPorchSF': 0,\n",
              " 'EnclosedPorch': 0,\n",
              " '3SsnPorch': 0,\n",
              " 'ScreenPorch': 0,\n",
              " 'PoolArea': 0,\n",
              " 'PoolQC': 0,\n",
              " 'Fence': 0,\n",
              " 'MiscFeature': 0,\n",
              " 'MiscVal': 0,\n",
              " 'MoSold': 0,\n",
              " 'YrSold': 0,\n",
              " 'SaleType': 0,\n",
              " 'SaleCondition': 0,\n",
              " 'SalePrice': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Splitting and Scalling"
      ],
      "metadata": {
        "id": "-08gNZLYY34X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "`X` : includes all of features after removing irrelevent faetures to prices as `Id` ..."
      ],
      "metadata": {
        "id": "yngDxVhIOEI_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_train.drop('SalePrice' , axis=1)\n",
        "y = df_train['SalePrice']"
      ],
      "metadata": {
        "id": "F9oYR4opEoPe"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.dtypes , y.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYZnZO60d1cy",
        "outputId": "7c0ce89a-e3b5-4b0f-f5a4-f61bab23f2e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(MSSubClass         int64\n",
              " MSZoning           int64\n",
              " LotFrontage      float64\n",
              " LotArea            int64\n",
              " Street             int64\n",
              "                   ...   \n",
              " MiscVal            int64\n",
              " MoSold             int64\n",
              " YrSold             int64\n",
              " SaleType           int64\n",
              " SaleCondition      int64\n",
              " Length: 79, dtype: object,\n",
              " dtype('int64'))"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape , y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p31VQ6bkhpIB",
        "outputId": "0be66c45-46fb-48b6-dc47-fb1cc706fe5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1121, 79), (1121,))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df_train['LotArea'],y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "a6owYtEcQgnq",
        "outputId": "72e7bd9b-f5b6-4a73-bb97-99254d9e0785"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_area = df_train['LotArea'].mean()\n",
        "std_area = df_train['LotArea'].std()\n",
        "variance_area = df_train['LotArea'].var()\n",
        "\n",
        "print(\"Mean of Area:\", mean_area)\n",
        "print(\"Standard Deviation of Area:\", std_area)\n",
        "print(\"Variance of Area:\", variance_area)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWv3tS6Yl6UY",
        "outputId": "c5a114df-25b3-4941-d67f-d9663da8c838"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean of Area: 10122.956289027654\n",
            "Standard Deviation of Area: 8129.302640583311\n",
            "Variance of Area: 66085561.422194794\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lower_bound = mean_area - 2 * std_area\n",
        "upper_bound = mean_area + 2 * std_area\n",
        "\n",
        "# Filter out outliers\n",
        "#outliers = df_train[(df_train['LotArea'] < lower_bound) | (df_train['LotArea'] > upper_bound)]\n",
        "df_train_cln = df_train[(df_train['LotArea'] >= lower_bound) & (df_train['LotArea'] <= upper_bound)]\n",
        "y_cln = df_train_cln['SalePrice']\n",
        "X_cln = df_train_cln[X.columns]"
      ],
      "metadata": {
        "id": "mCidtCyemN4e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape ,X_cln.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwQHCwmBnBp1",
        "outputId": "4657317b-ddfd-499e-f175-8cebd13a635c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1121, 79), (1107, 79))"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stdScaler = StandardScaler()\n",
        "mmScaler = MinMaxScaler()\n",
        "X_cln_scalled = stdScaler.fit_transform(X_cln)\n",
        "X_cln_scalled = mmScaler.fit_transform(X_cln_scalled)\n",
        "X[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "FalWgPBJm5H_",
        "outputId": "00a62ed1-cd6d-4a6f-8159-0a12803f8323"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  LotShape  \\\n",
              "0          60         3         65.0     8450       1      2         3   \n",
              "1          20         3         80.0     9600       1      2         3   \n",
              "\n",
              "   LandContour  Utilities  LotConfig  ...  ScreenPorch  PoolArea  PoolQC  \\\n",
              "0            3          0          4  ...            0         0       3   \n",
              "1            3          0          2  ...            0         0       3   \n",
              "\n",
              "   Fence  MiscFeature  MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
              "0      4            4        0       2    2008         8              4  \n",
              "1      4            4        0       5    2007         8              4  \n",
              "\n",
              "[2 rows x 79 columns]"
            ],
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              "    <div>\n",
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              "      <th>MSSubClass</th>\n",
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              "<p>2 rows × 79 columns</p>\n",
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              "    [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-68462211-9ee5-4633-a3c7-e2be00f8a669 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-68462211-9ee5-4633-a3c7-e2be00f8a669');\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",
              "\n",
              "\n",
              "<div id=\"df-131dc1ca-ce0c-4e78-837d-4f499189cba0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-131dc1ca-ce0c-4e78-837d-4f499189cba0')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-131dc1ca-ce0c-4e78-837d-4f499189cba0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_two_scatter( df_train['LotArea'] , y , X_features_cln_scalled[:, 0] , y_features_cln  )\n",
        "print('Area')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "NK047FwBladT",
        "outputId": "f4040a47-ae7b-4808-bfd5-dea99851e7a7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Area\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_two_scatter(df_train['FullBath'] , y , X_features_cln_scalled[:, 2]  , y_features_cln )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "IdNhTyqUSdLO",
        "outputId": "16a5050e-30eb-4b44-91fe-277ecf74e964"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_two_scatter(df_train['BedroomAbvGr'] , y , X_features_cln_scalled[:, 1] , y_features_cln)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "yTmf9wNQs8Oa",
        "outputId": "7bac6b48-e79e-4dcd-9aeb-e794d3df3443"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Models trained with all features"
      ],
      "metadata": {
        "id": "cKzI4nGD-B4m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train , x_test , y_train , y_test = train_test_split(X_cln_scalled , y_cln , test_size = .33 , random_state=0 )"
      ],
      "metadata": {
        "id": "c8WXvSgsWjaI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape , x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_zF5O0N_Y_o",
        "outputId": "95455ca3-d2a0-42c7-fa8e-2c1d1945d68a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((741, 79), (366, 79))"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_lin = LinearRegression()\n",
        "model_lin.fit(x_train , y_train)\n",
        "predicts_lin = model_lin.predict(x_test)"
      ],
      "metadata": {
        "id": "KQnBBFIsVY1c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('MSE ERROR Linear Regressor on X:',  MSE(y_test , predicts_lin))\n",
        "print('R2 SCORE:' , r2_score(y_test , predicts_lin))\n",
        "print(predicts_lin[0] , y_test.iloc[0])"
      ],
      "metadata": {
        "id": "eoO0LO-kV0JJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfbd305e-a7e8-4505-d27e-b85525b5e447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE ERROR Linear Regressor on X: 1240729634.6361547\n",
            "R2 SCORE: 0.839422292503865\n",
            "192841.72047280404 186500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_gbr = GradientBoostingRegressor( max_depth=3 , max_features='sqrt')\n",
        "model_gbr.fit(x_train , y_train)\n",
        "predicts_gbr = model_gbr.predict(x_test)"
      ],
      "metadata": {
        "id": "QYFscoy6-E7p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('MSE ERROR Gredient Boosting Regressor on X:',  MSE(y_test , predicts_gbr))\n",
        "print('R2 SCORE:' , r2_score(y_test , predicts_gbr))\n",
        "print(predicts_gbr[0] , y_test.iloc[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLlGFRqC-is4",
        "outputId": "6dff0a28-159e-4824-800d-d9cbbfdf0559"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE ERROR Gredient Boosting Regressor on X: 951742690.2645457\n",
            "R2 SCORE: 0.8768235600548849\n",
            "189658.26636983472 186500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_random_forest = RandomForestRegressor(n_estimators = 160  , max_depth = 8 , max_samples = 700 )\n",
        "model_random_forest.fit(x_train , y_train)\n",
        "predicts_rf = model_random_forest.predict(x_test)"
      ],
      "metadata": {
        "id": "NhWp_XMq5bYo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('MSE ERROR Random Forest on X:',  MSE(y_test , predicts_rf))\n",
        "print('R2 SCORE:' , r2_score(y_test , predicts_rf))\n",
        "print(predicts_rf[0] , y_test.iloc[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJmmpBok52Fu",
        "outputId": "45d394ff-b9c6-4b28-fed1-7a08d5c55cbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE ERROR Random Forest on X: 1314755408.4375567\n",
            "R2 SCORE: 0.8298417290025004\n",
            "190095.9709253814 186500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(y_test, predicts_gbr , color='blue', marker='o', label='Actual vs. Predicted')\n",
        "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', linestyle='--', lw=2, label='Perfect Prediction')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('Actual vs. Predicted Values (Gradient Boosting Regression)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "xXqspDhRTbp8",
        "outputId": "219d60fd-e6b2-4d21-e6b4-b5a736ebb68d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Models trained with  only mentioned features"
      ],
      "metadata": {
        "id": "yDycuRJsP6HL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`features` :  features mentioned in Task Document of Prodigy\n"
      ],
      "metadata": {
        "id": "59N8hJ-hQRLy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43PcosbyQSCd",
        "outputId": "7406705c-1de8-42bd-a932-c9a1652b0f0e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['LotArea', 'BedroomAbvGr', 'FullBath', 'HalfBath']"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_features = df_train[features]\n",
        "y_features = df_train['SalePrice']\n",
        "X_features.shape , y_features.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9pzWEBm1P9l3",
        "outputId": "03866a22-4568-4fa2-866c-5e4cc9cd73e5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1121, 4), (1121,))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing outliers of Lot Area based on the formula of below"
      ],
      "metadata": {
        "id": "O2OCAEOmS0t9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lower_bound = mean_area - 2 * std_area\n",
        "upper_bound = mean_area + 2 * std_area\n",
        "\n",
        "# Filter out outliers\n",
        "df_train_cln = df_train[(df_train['LotArea'] >= lower_bound) & (df_train['LotArea'] <= upper_bound)]\n",
        "y_features_cln = df_train_cln['SalePrice']\n",
        "X_features_cln = df_train_cln[features]"
      ],
      "metadata": {
        "id": "8vOTP6aIQYcL"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stdScaler = StandardScaler()\n",
        "mmScaler = MinMaxScaler()\n",
        "X_features_cln_scalled = stdScaler.fit_transform(X_features_cln)\n",
        "X_features_cln_scalled = mmScaler.fit_transform(X_features_cln_scalled)\n",
        "X_features_cln_scalled[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tmUQW-5wQp1M",
        "outputId": "b0a92132-7c81-452a-9e47-fe98526754a0"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.28740252, 0.5       , 0.66666667, 0.5       ],\n",
              "       [0.33362811, 0.5       , 0.66666667, 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_f , x_test_f , y_train_f , y_test_f = train_test_split(X_features_cln_scalled , y_features_cln , test_size = .33 , random_state=0 )"
      ],
      "metadata": {
        "id": "atcf8o_cRFfz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_lin = LinearRegression()\n",
        "model_lin.fit(x_train_f , y_train_f)\n",
        "predicts_lin = model_lin.predict(x_test_f)"
      ],
      "metadata": {
        "id": "hqSrJcJ4RbfZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('MSE ERROR Linear Regressor on features mentioned:',  MSE(y_test_f  , predicts_lin))\n",
        "print('R2 SCORE:' , r2_score(y_test_f , predicts_lin))\n",
        "print(predicts_lin[0] , y_test.iloc[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XTkVmxdHRiC7",
        "outputId": "21194be0-a25b-445a-ad8f-0586369231c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE ERROR Linear Regressor on features mentioned: 4278642985.7542343\n",
            "R2 SCORE: 0.44624947880098575\n",
            "226361.2141205827 186500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_gbr = GradientBoostingRegressor( max_depth=4 , max_features='sqrt')\n",
        "model_gbr.fit(x_train_f , y_train_f)\n",
        "predicts_gbr = model_gbr.predict(x_test_f)"
      ],
      "metadata": {
        "id": "uE9jRYXKRs1W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('MSE ERROR GBR on features mentioned:',  MSE(y_test_f , predicts_gbr))\n",
        "print('R2 SCORE:' , r2_score(y_test_f , predicts_gbr))\n",
        "print(predicts_gbr[0] , y_test.iloc[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rzz9QbBWRvjk",
        "outputId": "e2931026-5d51-46b4-8d06-0eeba4423c42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE ERROR GBR on features mentioned: 3754815029.4211845\n",
            "R2 SCORE: 0.514044339181679\n",
            "195313.05723032524 186500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(y_test_f, predicts_gbr , color='blue', marker='o', label='Actual vs. Predicted')\n",
        "plt.plot([min(y_test_f), max(y_test_f)], [min(y_test_f), max(y_test_f)], color='red', linestyle='--', lw=2, label='Perfect Prediction')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('Actual vs. Predicted Values (Gradient Boosting Regression)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "w2S49QnwYMJ7",
        "outputId": "4058b353-f0d3-45f0-dbfb-5ac5de751552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_random_forest = RandomForestRegressor()\n",
        "model_random_forest.fit(x_train_f , y_train_f)\n",
        "predicts_rf_features = model_random_forest.predict(x_test_f)"
      ],
      "metadata": {
        "id": "lX2FzO7F61ZI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('MSE ERROR Random Forest on features mentioned :',  MSE(y_test_f , predicts_rf_features))\n",
        "print('R2 SCORE:' , r2_score(y_test_f , predicts_rf_features))\n",
        "print(predicts_rf_features[0] , y_test_f.iloc[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfwR-iSt6-tf",
        "outputId": "860ff1a8-a882-4dcf-8c3c-ae2ceea255a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE ERROR Random Forest on features mentioned : 4507207731.112164\n",
            "R2 SCORE: 0.4166681729310916\n",
            "179764.35 186500\n"
          ]
        }
      ]
    }
  ]
}