Housing.ipynb

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  "metadata": {
    "colab": {
      "name": "Housing.ipynb",
      "provenance": [],
      "mount_file_id": "1kiIpAcj7PsB3i6lCtxUr0CZRHRtxKjxU",
      "authorship_tag": "ABX9TyPoTF/XkvgEWgi3zlPYm2/4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tatha04/Housing-Prices-Advanced-Regressoin-Techniques/blob/main/Housing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTz9y4OWWXY0"
      },
      "source": [
        "# Housing Prices: Advanced Regression Analysis\n",
        "\n",
        "This notebook explores the housing dataset from [Kaggle](https://www.kaggle.com/c/home-data-for-ml-course/overview).\n",
        "The dataset contains 79 variables describing various aspects of residential homes in Ames, Iowa. The data has been separated into training and test sets. The goal is to predict the final Sale Price of the houses in the test data set. \n",
        "\n",
        "Advanced feature engineering is used to infer missing values in the data set. The categorical features are transformed into numerical features using one hot encoding. \n",
        "The performances of various regression techniques such as Random Forest, Stochastic Gradient Descent, Gradient Boosting, and Xtremme Gradient Boosting are compared. Using the best fit model, I got a score of 0.13472 in the Kaggle competition."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYYVkOj3GRnQ"
      },
      "source": [
        "## Import necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0Hz_B9JLqF7"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "pd.set_option('display.max_rows', None)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQ8jUqLdI8qP"
      },
      "source": [
        "## Read data files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2Mnal94L7ch"
      },
      "source": [
        "df_Train = pd.read_csv(\"/content/drive/My Drive/Data Science/Data_Sets/Housing/train.csv\")\n",
        "df_Test = pd.read_csv(\"/content/drive/My Drive/Data Science/Data_Sets/Housing/test.csv\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "Mi9kAkOmMEPL",
        "outputId": "bbcbc41a-8d65-4bc3-c663-1788a151fd50"
      },
      "source": [
        "df_Train.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>...</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>...</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>None</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>...</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>...</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>None</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>...</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>...</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",
              "<p>5 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
              "0   1          60       RL  ...        WD         Normal    208500\n",
              "1   2          20       RL  ...        WD         Normal    181500\n",
              "2   3          60       RL  ...        WD         Normal    223500\n",
              "3   4          70       RL  ...        WD        Abnorml    140000\n",
              "4   5          60       RL  ...        WD         Normal    250000\n",
              "\n",
              "[5 rows x 81 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "xPZEP67pMRbX",
        "outputId": "8d1a8d7a-29dc-4bc7-bc09-a3d8d0169cde"
      },
      "source": [
        "df_Train.describe()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        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>"
            ],
            "text/plain": [
              "                Id   MSSubClass  ...       YrSold      SalePrice\n",
              "count  1460.000000  1460.000000  ...  1460.000000    1460.000000\n",
              "mean    730.500000    56.897260  ...  2007.815753  180921.195890\n",
              "std     421.610009    42.300571  ...     1.328095   79442.502883\n",
              "min       1.000000    20.000000  ...  2006.000000   34900.000000\n",
              "25%     365.750000    20.000000  ...  2007.000000  129975.000000\n",
              "50%     730.500000    50.000000  ...  2008.000000  163000.000000\n",
              "75%    1095.250000    70.000000  ...  2009.000000  214000.000000\n",
              "max    1460.000000   190.000000  ...  2010.000000  755000.000000\n",
              "\n",
              "[8 rows x 38 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "5STnjjwpMYfE",
        "outputId": "f3e68856-f67c-4bf1-cd0d-922edf3d3561"
      },
      "source": [
        "df_Test.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1232.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1444.000000</td>\n",
              "      <td>1458.000000</td>\n",
              "      <td>1458.000000</td>\n",
              "      <td>1458.000000</td>\n",
              "      <td>1458.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1457.000000</td>\n",
              "      <td>1457.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.00000</td>\n",
              "      <td>1381.000000</td>\n",
              "      <td>1458.000000</td>\n",
              "      <td>1458.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "      <td>1459.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2190.000000</td>\n",
              "      <td>57.378341</td>\n",
              "      <td>68.580357</td>\n",
              "      <td>9819.161069</td>\n",
              "      <td>6.078821</td>\n",
              "      <td>5.553804</td>\n",
              "      <td>1971.357779</td>\n",
              "      <td>1983.662783</td>\n",
              "      <td>100.709141</td>\n",
              "      <td>439.203704</td>\n",
              "      <td>52.619342</td>\n",
              "      <td>554.294925</td>\n",
              "      <td>1046.117970</td>\n",
              "      <td>1156.534613</td>\n",
              "      <td>325.967786</td>\n",
              "      <td>3.543523</td>\n",
              "      <td>1486.045922</td>\n",
              "      <td>0.434454</td>\n",
              "      <td>0.065202</td>\n",
              "      <td>1.570939</td>\n",
              "      <td>0.377656</td>\n",
              "      <td>2.854010</td>\n",
              "      <td>1.042495</td>\n",
              "      <td>6.385195</td>\n",
              "      <td>0.58122</td>\n",
              "      <td>1977.721217</td>\n",
              "      <td>1.766118</td>\n",
              "      <td>472.768861</td>\n",
              "      <td>93.174777</td>\n",
              "      <td>48.313914</td>\n",
              "      <td>24.243317</td>\n",
              "      <td>1.794380</td>\n",
              "      <td>17.064428</td>\n",
              "      <td>1.744345</td>\n",
              "      <td>58.167923</td>\n",
              "      <td>6.104181</td>\n",
              "      <td>2007.769705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>421.321334</td>\n",
              "      <td>42.746880</td>\n",
              "      <td>22.376841</td>\n",
              "      <td>4955.517327</td>\n",
              "      <td>1.436812</td>\n",
              "      <td>1.113740</td>\n",
              "      <td>30.390071</td>\n",
              "      <td>21.130467</td>\n",
              "      <td>177.625900</td>\n",
              "      <td>455.268042</td>\n",
              "      <td>176.753926</td>\n",
              "      <td>437.260486</td>\n",
              "      <td>442.898624</td>\n",
              "      <td>398.165820</td>\n",
              "      <td>420.610226</td>\n",
              "      <td>44.043251</td>\n",
              "      <td>485.566099</td>\n",
              "      <td>0.530648</td>\n",
              "      <td>0.252468</td>\n",
              "      <td>0.555190</td>\n",
              "      <td>0.503017</td>\n",
              "      <td>0.829788</td>\n",
              "      <td>0.208472</td>\n",
              "      <td>1.508895</td>\n",
              "      <td>0.64742</td>\n",
              "      <td>26.431175</td>\n",
              "      <td>0.775945</td>\n",
              "      <td>217.048611</td>\n",
              "      <td>127.744882</td>\n",
              "      <td>68.883364</td>\n",
              "      <td>67.227765</td>\n",
              "      <td>20.207842</td>\n",
              "      <td>56.609763</td>\n",
              "      <td>30.491646</td>\n",
              "      <td>630.806978</td>\n",
              "      <td>2.722432</td>\n",
              "      <td>1.301740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1461.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1879.000000</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>407.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>407.000000</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>3.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1895.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1825.500000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>7391.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1953.000000</td>\n",
              "      <td>1963.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>219.250000</td>\n",
              "      <td>784.000000</td>\n",
              "      <td>873.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1117.500000</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>0.00000</td>\n",
              "      <td>1959.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>318.000000</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>4.000000</td>\n",
              "      <td>2007.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2190.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>9399.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1973.000000</td>\n",
              "      <td>1992.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>350.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>460.000000</td>\n",
              "      <td>988.000000</td>\n",
              "      <td>1079.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1432.000000</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>0.00000</td>\n",
              "      <td>1979.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>28.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2554.500000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>11517.500000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2001.000000</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>753.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>797.750000</td>\n",
              "      <td>1305.000000</td>\n",
              "      <td>1382.500000</td>\n",
              "      <td>676.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1721.000000</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>1.00000</td>\n",
              "      <td>2002.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>576.000000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>72.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2919.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>56600.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>1290.000000</td>\n",
              "      <td>4010.000000</td>\n",
              "      <td>1526.000000</td>\n",
              "      <td>2140.000000</td>\n",
              "      <td>5095.000000</td>\n",
              "      <td>5095.000000</td>\n",
              "      <td>1862.000000</td>\n",
              "      <td>1064.000000</td>\n",
              "      <td>5095.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>2207.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1488.000000</td>\n",
              "      <td>1424.000000</td>\n",
              "      <td>742.000000</td>\n",
              "      <td>1012.000000</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>576.000000</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Id   MSSubClass  ...       MoSold       YrSold\n",
              "count  1459.000000  1459.000000  ...  1459.000000  1459.000000\n",
              "mean   2190.000000    57.378341  ...     6.104181  2007.769705\n",
              "std     421.321334    42.746880  ...     2.722432     1.301740\n",
              "min    1461.000000    20.000000  ...     1.000000  2006.000000\n",
              "25%    1825.500000    20.000000  ...     4.000000  2007.000000\n",
              "50%    2190.000000    50.000000  ...     6.000000  2008.000000\n",
              "75%    2554.500000    70.000000  ...     8.000000  2009.000000\n",
              "max    2919.000000   190.000000  ...    12.000000  2010.000000\n",
              "\n",
              "[8 rows x 37 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3qOK6P9UT04",
        "outputId": "233e314c-962c-4122-dd57-ee63d2bedbad"
      },
      "source": [
        "df_Train.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1460 entries, 0 to 1459\n",
            "Data columns (total 81 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             1460 non-null   int64  \n",
            " 1   MSSubClass     1460 non-null   int64  \n",
            " 2   MSZoning       1460 non-null   object \n",
            " 3   LotFrontage    1201 non-null   float64\n",
            " 4   LotArea        1460 non-null   int64  \n",
            " 5   Street         1460 non-null   object \n",
            " 6   Alley          91 non-null     object \n",
            " 7   LotShape       1460 non-null   object \n",
            " 8   LandContour    1460 non-null   object \n",
            " 9   Utilities      1460 non-null   object \n",
            " 10  LotConfig      1460 non-null   object \n",
            " 11  LandSlope      1460 non-null   object \n",
            " 12  Neighborhood   1460 non-null   object \n",
            " 13  Condition1     1460 non-null   object \n",
            " 14  Condition2     1460 non-null   object \n",
            " 15  BldgType       1460 non-null   object \n",
            " 16  HouseStyle     1460 non-null   object \n",
            " 17  OverallQual    1460 non-null   int64  \n",
            " 18  OverallCond    1460 non-null   int64  \n",
            " 19  YearBuilt      1460 non-null   int64  \n",
            " 20  YearRemodAdd   1460 non-null   int64  \n",
            " 21  RoofStyle      1460 non-null   object \n",
            " 22  RoofMatl       1460 non-null   object \n",
            " 23  Exterior1st    1460 non-null   object \n",
            " 24  Exterior2nd    1460 non-null   object \n",
            " 25  MasVnrType     1452 non-null   object \n",
            " 26  MasVnrArea     1452 non-null   float64\n",
            " 27  ExterQual      1460 non-null   object \n",
            " 28  ExterCond      1460 non-null   object \n",
            " 29  Foundation     1460 non-null   object \n",
            " 30  BsmtQual       1423 non-null   object \n",
            " 31  BsmtCond       1423 non-null   object \n",
            " 32  BsmtExposure   1422 non-null   object \n",
            " 33  BsmtFinType1   1423 non-null   object \n",
            " 34  BsmtFinSF1     1460 non-null   int64  \n",
            " 35  BsmtFinType2   1422 non-null   object \n",
            " 36  BsmtFinSF2     1460 non-null   int64  \n",
            " 37  BsmtUnfSF      1460 non-null   int64  \n",
            " 38  TotalBsmtSF    1460 non-null   int64  \n",
            " 39  Heating        1460 non-null   object \n",
            " 40  HeatingQC      1460 non-null   object \n",
            " 41  CentralAir     1460 non-null   object \n",
            " 42  Electrical     1459 non-null   object \n",
            " 43  1stFlrSF       1460 non-null   int64  \n",
            " 44  2ndFlrSF       1460 non-null   int64  \n",
            " 45  LowQualFinSF   1460 non-null   int64  \n",
            " 46  GrLivArea      1460 non-null   int64  \n",
            " 47  BsmtFullBath   1460 non-null   int64  \n",
            " 48  BsmtHalfBath   1460 non-null   int64  \n",
            " 49  FullBath       1460 non-null   int64  \n",
            " 50  HalfBath       1460 non-null   int64  \n",
            " 51  BedroomAbvGr   1460 non-null   int64  \n",
            " 52  KitchenAbvGr   1460 non-null   int64  \n",
            " 53  KitchenQual    1460 non-null   object \n",
            " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
            " 55  Functional     1460 non-null   object \n",
            " 56  Fireplaces     1460 non-null   int64  \n",
            " 57  FireplaceQu    770 non-null    object \n",
            " 58  GarageType     1379 non-null   object \n",
            " 59  GarageYrBlt    1379 non-null   float64\n",
            " 60  GarageFinish   1379 non-null   object \n",
            " 61  GarageCars     1460 non-null   int64  \n",
            " 62  GarageArea     1460 non-null   int64  \n",
            " 63  GarageQual     1379 non-null   object \n",
            " 64  GarageCond     1379 non-null   object \n",
            " 65  PavedDrive     1460 non-null   object \n",
            " 66  WoodDeckSF     1460 non-null   int64  \n",
            " 67  OpenPorchSF    1460 non-null   int64  \n",
            " 68  EnclosedPorch  1460 non-null   int64  \n",
            " 69  3SsnPorch      1460 non-null   int64  \n",
            " 70  ScreenPorch    1460 non-null   int64  \n",
            " 71  PoolArea       1460 non-null   int64  \n",
            " 72  PoolQC         7 non-null      object \n",
            " 73  Fence          281 non-null    object \n",
            " 74  MiscFeature    54 non-null     object \n",
            " 75  MiscVal        1460 non-null   int64  \n",
            " 76  MoSold         1460 non-null   int64  \n",
            " 77  YrSold         1460 non-null   int64  \n",
            " 78  SaleType       1460 non-null   object \n",
            " 79  SaleCondition  1460 non-null   object \n",
            " 80  SalePrice      1460 non-null   int64  \n",
            "dtypes: float64(3), int64(35), object(43)\n",
            "memory usage: 924.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMZSi1GtMpsh",
        "outputId": "318f470b-964a-4ef9-c006-efb0d06e10de"
      },
      "source": [
        "print(\"Shape\")\n",
        "print(\"Training data : \", df_Train.shape)\n",
        "print(\"Test data     : \", df_Test.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape\n",
            "Training data :  (1460, 81)\n",
            "Test data     :  (1459, 80)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5jJrrmU4QZcF",
        "outputId": "1ecc2194-593b-47aa-a61d-fd6f1f049562"
      },
      "source": [
        "s = (df_Train.dtypes == 'object')\n",
        "object_cols = list(s[s].index)\n",
        "print(\"Categorical variables:\")\n",
        "print(object_cols)\n",
        "\n",
        "s = (df_Train.dtypes == 'int')\n",
        "num_cols = list(s[s].index)\n",
        "print(\"Integer variables:\")\n",
        "print(num_cols)\n",
        "\n",
        "s = (df_Train.dtypes == 'float')\n",
        "num_cols = list(s[s].index)\n",
        "print(\"Real variables:\")\n",
        "print(num_cols)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Categorical variables:\n",
            "['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']\n",
            "Integer variables:\n",
            "['Id', 'MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SalePrice']\n",
            "Real variables:\n",
            "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xVxNt6uuQiAI",
        "outputId": "3c82be19-3ff3-4ff3-e512-7c952717ac65"
      },
      "source": [
        "f_desc = open('/content/drive/MyDrive/Data Science/Data_Sets/Housing/data_description.txt')\n",
        "print(f_desc.read())\n",
        "f_desc.close()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSSubClass: Identifies the type of dwelling involved in the sale.\t\n",
            "\n",
            "        20\t1-STORY 1946 & NEWER ALL STYLES\n",
            "        30\t1-STORY 1945 & OLDER\n",
            "        40\t1-STORY W/FINISHED ATTIC ALL AGES\n",
            "        45\t1-1/2 STORY - UNFINISHED ALL AGES\n",
            "        50\t1-1/2 STORY FINISHED ALL AGES\n",
            "        60\t2-STORY 1946 & NEWER\n",
            "        70\t2-STORY 1945 & OLDER\n",
            "        75\t2-1/2 STORY ALL AGES\n",
            "        80\tSPLIT OR MULTI-LEVEL\n",
            "        85\tSPLIT FOYER\n",
            "        90\tDUPLEX - ALL STYLES AND AGES\n",
            "       120\t1-STORY PUD (Planned Unit Development) - 1946 & NEWER\n",
            "       150\t1-1/2 STORY PUD - ALL AGES\n",
            "       160\t2-STORY PUD - 1946 & NEWER\n",
            "       180\tPUD - MULTILEVEL - INCL SPLIT LEV/FOYER\n",
            "       190\t2 FAMILY CONVERSION - ALL STYLES AND AGES\n",
            "\n",
            "MSZoning: Identifies the general zoning classification of the sale.\n",
            "\t\t\n",
            "       A\tAgriculture\n",
            "       C\tCommercial\n",
            "       FV\tFloating Village Residential\n",
            "       I\tIndustrial\n",
            "       RH\tResidential High Density\n",
            "       RL\tResidential Low Density\n",
            "       RP\tResidential Low Density Park \n",
            "       RM\tResidential Medium Density\n",
            "\t\n",
            "LotFrontage: Linear feet of street connected to property\n",
            "\n",
            "LotArea: Lot size in square feet\n",
            "\n",
            "Street: Type of road access to property\n",
            "\n",
            "       Grvl\tGravel\t\n",
            "       Pave\tPaved\n",
            "       \t\n",
            "Alley: Type of alley access to property\n",
            "\n",
            "       Grvl\tGravel\n",
            "       Pave\tPaved\n",
            "       NA \tNo alley access\n",
            "\t\t\n",
            "LotShape: General shape of property\n",
            "\n",
            "       Reg\tRegular\t\n",
            "       IR1\tSlightly irregular\n",
            "       IR2\tModerately Irregular\n",
            "       IR3\tIrregular\n",
            "       \n",
            "LandContour: Flatness of the property\n",
            "\n",
            "       Lvl\tNear Flat/Level\t\n",
            "       Bnk\tBanked - Quick and significant rise from street grade to building\n",
            "       HLS\tHillside - Significant slope from side to side\n",
            "       Low\tDepression\n",
            "\t\t\n",
            "Utilities: Type of utilities available\n",
            "\t\t\n",
            "       AllPub\tAll public Utilities (E,G,W,& S)\t\n",
            "       NoSewr\tElectricity, Gas, and Water (Septic Tank)\n",
            "       NoSeWa\tElectricity and Gas Only\n",
            "       ELO\tElectricity only\t\n",
            "\t\n",
            "LotConfig: Lot configuration\n",
            "\n",
            "       Inside\tInside lot\n",
            "       Corner\tCorner lot\n",
            "       CulDSac\tCul-de-sac\n",
            "       FR2\tFrontage on 2 sides of property\n",
            "       FR3\tFrontage on 3 sides of property\n",
            "\t\n",
            "LandSlope: Slope of property\n",
            "\t\t\n",
            "       Gtl\tGentle slope\n",
            "       Mod\tModerate Slope\t\n",
            "       Sev\tSevere Slope\n",
            "\t\n",
            "Neighborhood: Physical locations within Ames city limits\n",
            "\n",
            "       Blmngtn\tBloomington Heights\n",
            "       Blueste\tBluestem\n",
            "       BrDale\tBriardale\n",
            "       BrkSide\tBrookside\n",
            "       ClearCr\tClear Creek\n",
            "       CollgCr\tCollege Creek\n",
            "       Crawfor\tCrawford\n",
            "       Edwards\tEdwards\n",
            "       Gilbert\tGilbert\n",
            "       IDOTRR\tIowa DOT and Rail Road\n",
            "       MeadowV\tMeadow Village\n",
            "       Mitchel\tMitchell\n",
            "       Names\tNorth Ames\n",
            "       NoRidge\tNorthridge\n",
            "       NPkVill\tNorthpark Villa\n",
            "       NridgHt\tNorthridge Heights\n",
            "       NWAmes\tNorthwest Ames\n",
            "       OldTown\tOld Town\n",
            "       SWISU\tSouth & West of Iowa State University\n",
            "       Sawyer\tSawyer\n",
            "       SawyerW\tSawyer West\n",
            "       Somerst\tSomerset\n",
            "       StoneBr\tStone Brook\n",
            "       Timber\tTimberland\n",
            "       Veenker\tVeenker\n",
            "\t\t\t\n",
            "Condition1: Proximity to various conditions\n",
            "\t\n",
            "       Artery\tAdjacent to arterial street\n",
            "       Feedr\tAdjacent to feeder street\t\n",
            "       Norm\tNormal\t\n",
            "       RRNn\tWithin 200' of North-South Railroad\n",
            "       RRAn\tAdjacent to North-South Railroad\n",
            "       PosN\tNear positive off-site feature--park, greenbelt, etc.\n",
            "       PosA\tAdjacent to postive off-site feature\n",
            "       RRNe\tWithin 200' of East-West Railroad\n",
            "       RRAe\tAdjacent to East-West Railroad\n",
            "\t\n",
            "Condition2: Proximity to various conditions (if more than one is present)\n",
            "\t\t\n",
            "       Artery\tAdjacent to arterial street\n",
            "       Feedr\tAdjacent to feeder street\t\n",
            "       Norm\tNormal\t\n",
            "       RRNn\tWithin 200' of North-South Railroad\n",
            "       RRAn\tAdjacent to North-South Railroad\n",
            "       PosN\tNear positive off-site feature--park, greenbelt, etc.\n",
            "       PosA\tAdjacent to postive off-site feature\n",
            "       RRNe\tWithin 200' of East-West Railroad\n",
            "       RRAe\tAdjacent to East-West Railroad\n",
            "\t\n",
            "BldgType: Type of dwelling\n",
            "\t\t\n",
            "       1Fam\tSingle-family Detached\t\n",
            "       2FmCon\tTwo-family Conversion; originally built as one-family dwelling\n",
            "       Duplx\tDuplex\n",
            "       TwnhsE\tTownhouse End Unit\n",
            "       TwnhsI\tTownhouse Inside Unit\n",
            "\t\n",
            "HouseStyle: Style of dwelling\n",
            "\t\n",
            "       1Story\tOne story\n",
            "       1.5Fin\tOne and one-half story: 2nd level finished\n",
            "       1.5Unf\tOne and one-half story: 2nd level unfinished\n",
            "       2Story\tTwo story\n",
            "       2.5Fin\tTwo and one-half story: 2nd level finished\n",
            "       2.5Unf\tTwo and one-half story: 2nd level unfinished\n",
            "       SFoyer\tSplit Foyer\n",
            "       SLvl\tSplit Level\n",
            "\t\n",
            "OverallQual: Rates the overall material and finish of the house\n",
            "\n",
            "       10\tVery Excellent\n",
            "       9\tExcellent\n",
            "       8\tVery Good\n",
            "       7\tGood\n",
            "       6\tAbove Average\n",
            "       5\tAverage\n",
            "       4\tBelow Average\n",
            "       3\tFair\n",
            "       2\tPoor\n",
            "       1\tVery Poor\n",
            "\t\n",
            "OverallCond: Rates the overall condition of the house\n",
            "\n",
            "       10\tVery Excellent\n",
            "       9\tExcellent\n",
            "       8\tVery Good\n",
            "       7\tGood\n",
            "       6\tAbove Average\t\n",
            "       5\tAverage\n",
            "       4\tBelow Average\t\n",
            "       3\tFair\n",
            "       2\tPoor\n",
            "       1\tVery Poor\n",
            "\t\t\n",
            "YearBuilt: Original construction date\n",
            "\n",
            "YearRemodAdd: Remodel date (same as construction date if no remodeling or additions)\n",
            "\n",
            "RoofStyle: Type of roof\n",
            "\n",
            "       Flat\tFlat\n",
            "       Gable\tGable\n",
            "       Gambrel\tGabrel (Barn)\n",
            "       Hip\tHip\n",
            "       Mansard\tMansard\n",
            "       Shed\tShed\n",
            "\t\t\n",
            "RoofMatl: Roof material\n",
            "\n",
            "       ClyTile\tClay or Tile\n",
            "       CompShg\tStandard (Composite) Shingle\n",
            "       Membran\tMembrane\n",
            "       Metal\tMetal\n",
            "       Roll\tRoll\n",
            "       Tar&Grv\tGravel & Tar\n",
            "       WdShake\tWood Shakes\n",
            "       WdShngl\tWood Shingles\n",
            "\t\t\n",
            "Exterior1st: Exterior covering on house\n",
            "\n",
            "       AsbShng\tAsbestos Shingles\n",
            "       AsphShn\tAsphalt Shingles\n",
            "       BrkComm\tBrick Common\n",
            "       BrkFace\tBrick Face\n",
            "       CBlock\tCinder Block\n",
            "       CemntBd\tCement Board\n",
            "       HdBoard\tHard Board\n",
            "       ImStucc\tImitation Stucco\n",
            "       MetalSd\tMetal Siding\n",
            "       Other\tOther\n",
            "       Plywood\tPlywood\n",
            "       PreCast\tPreCast\t\n",
            "       Stone\tStone\n",
            "       Stucco\tStucco\n",
            "       VinylSd\tVinyl Siding\n",
            "       Wd Sdng\tWood Siding\n",
            "       WdShing\tWood Shingles\n",
            "\t\n",
            "Exterior2nd: Exterior covering on house (if more than one material)\n",
            "\n",
            "       AsbShng\tAsbestos Shingles\n",
            "       AsphShn\tAsphalt Shingles\n",
            "       BrkComm\tBrick Common\n",
            "       BrkFace\tBrick Face\n",
            "       CBlock\tCinder Block\n",
            "       CemntBd\tCement Board\n",
            "       HdBoard\tHard Board\n",
            "       ImStucc\tImitation Stucco\n",
            "       MetalSd\tMetal Siding\n",
            "       Other\tOther\n",
            "       Plywood\tPlywood\n",
            "       PreCast\tPreCast\n",
            "       Stone\tStone\n",
            "       Stucco\tStucco\n",
            "       VinylSd\tVinyl Siding\n",
            "       Wd Sdng\tWood Siding\n",
            "       WdShing\tWood Shingles\n",
            "\t\n",
            "MasVnrType: Masonry veneer type\n",
            "\n",
            "       BrkCmn\tBrick Common\n",
            "       BrkFace\tBrick Face\n",
            "       CBlock\tCinder Block\n",
            "       None\tNone\n",
            "       Stone\tStone\n",
            "\t\n",
            "MasVnrArea: Masonry veneer area in square feet\n",
            "\n",
            "ExterQual: Evaluates the quality of the material on the exterior \n",
            "\t\t\n",
            "       Ex\tExcellent\n",
            "       Gd\tGood\n",
            "       TA\tAverage/Typical\n",
            "       Fa\tFair\n",
            "       Po\tPoor\n",
            "\t\t\n",
            "ExterCond: Evaluates the present condition of the material on the exterior\n",
            "\t\t\n",
            "       Ex\tExcellent\n",
            "       Gd\tGood\n",
            "       TA\tAverage/Typical\n",
            "       Fa\tFair\n",
            "       Po\tPoor\n",
            "\t\t\n",
            "Foundation: Type of foundation\n",
            "\t\t\n",
            "       BrkTil\tBrick & Tile\n",
            "       CBlock\tCinder Block\n",
            "       PConc\tPoured Contrete\t\n",
            "       Slab\tSlab\n",
            "       Stone\tStone\n",
            "       Wood\tWood\n",
            "\t\t\n",
            "BsmtQual: Evaluates the height of the basement\n",
            "\n",
            "       Ex\tExcellent (100+ inches)\t\n",
            "       Gd\tGood (90-99 inches)\n",
            "       TA\tTypical (80-89 inches)\n",
            "       Fa\tFair (70-79 inches)\n",
            "       Po\tPoor (<70 inches\n",
            "       NA\tNo Basement\n",
            "\t\t\n",
            "BsmtCond: Evaluates the general condition of the basement\n",
            "\n",
            "       Ex\tExcellent\n",
            "       Gd\tGood\n",
            "       TA\tTypical - slight dampness allowed\n",
            "       Fa\tFair - dampness or some cracking or settling\n",
            "       Po\tPoor - Severe cracking, settling, or wetness\n",
            "       NA\tNo Basement\n",
            "\t\n",
            "BsmtExposure: Refers to walkout or garden level walls\n",
            "\n",
            "       Gd\tGood Exposure\n",
            "       Av\tAverage Exposure (split levels or foyers typically score average or above)\t\n",
            "       Mn\tMimimum Exposure\n",
            "       No\tNo Exposure\n",
            "       NA\tNo Basement\n",
            "\t\n",
            "BsmtFinType1: Rating of basement finished area\n",
            "\n",
            "       GLQ\tGood Living Quarters\n",
            "       ALQ\tAverage Living Quarters\n",
            "       BLQ\tBelow Average Living Quarters\t\n",
            "       Rec\tAverage Rec Room\n",
            "       LwQ\tLow Quality\n",
            "       Unf\tUnfinshed\n",
            "       NA\tNo Basement\n",
            "\t\t\n",
            "BsmtFinSF1: Type 1 finished square feet\n",
            "\n",
            "BsmtFinType2: Rating of basement finished area (if multiple types)\n",
            "\n",
            "       GLQ\tGood Living Quarters\n",
            "       ALQ\tAverage Living Quarters\n",
            "       BLQ\tBelow Average Living Quarters\t\n",
            "       Rec\tAverage Rec Room\n",
            "       LwQ\tLow Quality\n",
            "       Unf\tUnfinshed\n",
            "       NA\tNo Basement\n",
            "\n",
            "BsmtFinSF2: Type 2 finished square feet\n",
            "\n",
            "BsmtUnfSF: Unfinished square feet of basement area\n",
            "\n",
            "TotalBsmtSF: Total square feet of basement area\n",
            "\n",
            "Heating: Type of heating\n",
            "\t\t\n",
            "       Floor\tFloor Furnace\n",
            "       GasA\tGas forced warm air furnace\n",
            "       GasW\tGas hot water or steam heat\n",
            "       Grav\tGravity furnace\t\n",
            "       OthW\tHot water or steam heat other than gas\n",
            "       Wall\tWall furnace\n",
            "\t\t\n",
            "HeatingQC: Heating quality and condition\n",
            "\n",
            "       Ex\tExcellent\n",
            "       Gd\tGood\n",
            "       TA\tAverage/Typical\n",
            "       Fa\tFair\n",
            "       Po\tPoor\n",
            "\t\t\n",
            "CentralAir: Central air conditioning\n",
            "\n",
            "       N\tNo\n",
            "       Y\tYes\n",
            "\t\t\n",
            "Electrical: Electrical system\n",
            "\n",
            "       SBrkr\tStandard Circuit Breakers & Romex\n",
            "       FuseA\tFuse Box over 60 AMP and all Romex wiring (Average)\t\n",
            "       FuseF\t60 AMP Fuse Box and mostly Romex wiring (Fair)\n",
            "       FuseP\t60 AMP Fuse Box and mostly knob & tube wiring (poor)\n",
            "       Mix\tMixed\n",
            "\t\t\n",
            "1stFlrSF: First Floor square feet\n",
            " \n",
            "2ndFlrSF: Second floor square feet\n",
            "\n",
            "LowQualFinSF: Low quality finished square feet (all floors)\n",
            "\n",
            "GrLivArea: Above grade (ground) living area square feet\n",
            "\n",
            "BsmtFullBath: Basement full bathrooms\n",
            "\n",
            "BsmtHalfBath: Basement half bathrooms\n",
            "\n",
            "FullBath: Full bathrooms above grade\n",
            "\n",
            "HalfBath: Half baths above grade\n",
            "\n",
            "Bedroom: Bedrooms above grade (does NOT include basement bedrooms)\n",
            "\n",
            "Kitchen: Kitchens above grade\n",
            "\n",
            "KitchenQual: Kitchen quality\n",
            "\n",
            "       Ex\tExcellent\n",
            "       Gd\tGood\n",
            "       TA\tTypical/Average\n",
            "       Fa\tFair\n",
            "       Po\tPoor\n",
            "       \t\n",
            "TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)\n",
            "\n",
            "Functional: Home functionality (Assume typical unless deductions are warranted)\n",
            "\n",
            "       Typ\tTypical Functionality\n",
            "       Min1\tMinor Deductions 1\n",
            "       Min2\tMinor Deductions 2\n",
            "       Mod\tModerate Deductions\n",
            "       Maj1\tMajor Deductions 1\n",
            "       Maj2\tMajor Deductions 2\n",
            "       Sev\tSeverely Damaged\n",
            "       Sal\tSalvage only\n",
            "\t\t\n",
            "Fireplaces: Number of fireplaces\n",
            "\n",
            "FireplaceQu: Fireplace quality\n",
            "\n",
            "       Ex\tExcellent - Exceptional Masonry Fireplace\n",
            "       Gd\tGood - Masonry Fireplace in main level\n",
            "       TA\tAverage - Prefabricated Fireplace in main living area or Masonry Fireplace in basement\n",
            "       Fa\tFair - Prefabricated Fireplace in basement\n",
            "       Po\tPoor - Ben Franklin Stove\n",
            "       NA\tNo Fireplace\n",
            "\t\t\n",
            "GarageType: Garage location\n",
            "\t\t\n",
            "       2Types\tMore than one type of garage\n",
            "       Attchd\tAttached to home\n",
            "       Basment\tBasement Garage\n",
            "       BuiltIn\tBuilt-In (Garage part of house - typically has room above garage)\n",
            "       CarPort\tCar Port\n",
            "       Detchd\tDetached from home\n",
            "       NA\tNo Garage\n",
            "\t\t\n",
            "GarageYrBlt: Year garage was built\n",
            "\t\t\n",
            "GarageFinish: Interior finish of the garage\n",
            "\n",
            "       Fin\tFinished\n",
            "       RFn\tRough Finished\t\n",
            "       Unf\tUnfinished\n",
            "       NA\tNo Garage\n",
            "\t\t\n",
            "GarageCars: Size of garage in car capacity\n",
            "\n",
            "GarageArea: Size of garage in square feet\n",
            "\n",
            "GarageQual: Garage quality\n",
            "\n",
            "       Ex\tExcellent\n",
            "       Gd\tGood\n",
            "       TA\tTypical/Average\n",
            "       Fa\tFair\n",
            "       Po\tPoor\n",
            "       NA\tNo Garage\n",
            "\t\t\n",
            "GarageCond: Garage condition\n",
            "\n",
            "       Ex\tExcellent\n",
            "       Gd\tGood\n",
            "       TA\tTypical/Average\n",
            "       Fa\tFair\n",
            "       Po\tPoor\n",
            "       NA\tNo Garage\n",
            "\t\t\n",
            "PavedDrive: Paved driveway\n",
            "\n",
            "       Y\tPaved \n",
            "       P\tPartial Pavement\n",
            "       N\tDirt/Gravel\n",
            "\t\t\n",
            "WoodDeckSF: Wood deck area in square feet\n",
            "\n",
            "OpenPorchSF: Open porch area in square feet\n",
            "\n",
            "EnclosedPorch: Enclosed porch area in square feet\n",
            "\n",
            "3SsnPorch: Three season porch area in square feet\n",
            "\n",
            "ScreenPorch: Screen porch area in square feet\n",
            "\n",
            "PoolArea: Pool area in square feet\n",
            "\n",
            "PoolQC: Pool quality\n",
            "\t\t\n",
            "       Ex\tExcellent\n",
            "       Gd\tGood\n",
            "       TA\tAverage/Typical\n",
            "       Fa\tFair\n",
            "       NA\tNo Pool\n",
            "\t\t\n",
            "Fence: Fence quality\n",
            "\t\t\n",
            "       GdPrv\tGood Privacy\n",
            "       MnPrv\tMinimum Privacy\n",
            "       GdWo\tGood Wood\n",
            "       MnWw\tMinimum Wood/Wire\n",
            "       NA\tNo Fence\n",
            "\t\n",
            "MiscFeature: Miscellaneous feature not covered in other categories\n",
            "\t\t\n",
            "       Elev\tElevator\n",
            "       Gar2\t2nd Garage (if not described in garage section)\n",
            "       Othr\tOther\n",
            "       Shed\tShed (over 100 SF)\n",
            "       TenC\tTennis Court\n",
            "       NA\tNone\n",
            "\t\t\n",
            "MiscVal: $Value of miscellaneous feature\n",
            "\n",
            "MoSold: Month Sold (MM)\n",
            "\n",
            "YrSold: Year Sold (YYYY)\n",
            "\n",
            "SaleType: Type of sale\n",
            "\t\t\n",
            "       WD \tWarranty Deed - Conventional\n",
            "       CWD\tWarranty Deed - Cash\n",
            "       VWD\tWarranty Deed - VA Loan\n",
            "       New\tHome just constructed and sold\n",
            "       COD\tCourt Officer Deed/Estate\n",
            "       Con\tContract 15% Down payment regular terms\n",
            "       ConLw\tContract Low Down payment and low interest\n",
            "       ConLI\tContract Low Interest\n",
            "       ConLD\tContract Low Down\n",
            "       Oth\tOther\n",
            "\t\t\n",
            "SaleCondition: Condition of sale\n",
            "\n",
            "       Normal\tNormal Sale\n",
            "       Abnorml\tAbnormal Sale -  trade, foreclosure, short sale\n",
            "       AdjLand\tAdjoining Land Purchase\n",
            "       Alloca\tAllocation - two linked properties with separate deeds, typically condo with a garage unit\t\n",
            "       Family\tSale between family members\n",
            "       Partial\tHome was not completed when last assessed (associated with New Homes)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCkOg_77JZGB"
      },
      "source": [
        "## Missing Values:\n",
        "\n",
        "### Combine training and test data into a single dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQp8uDMKYskl"
      },
      "source": [
        "df = [df_Train,df_Test]\n",
        "df_combined = pd.concat(df).reset_index(drop=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AMA7TE_VaVBQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "wA7hIk75ZHCs",
        "outputId": "e201a588-aada-414d-aab9-f4c9c410d4a4"
      },
      "source": [
        "df_combined.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>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>...</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>706.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</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>1.0</td>\n",
              "      <td>0.0</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>2.0</td>\n",
              "      <td>548.0</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>208500.0</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>None</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>978.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>1.0</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>2.0</td>\n",
              "      <td>460.0</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>181500.0</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>486.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</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>1.0</td>\n",
              "      <td>0.0</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>2.0</td>\n",
              "      <td>608.0</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>223500.0</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>None</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>216.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</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>1.0</td>\n",
              "      <td>0.0</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>3.0</td>\n",
              "      <td>642.0</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>140000.0</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>655.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</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>1.0</td>\n",
              "      <td>0.0</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>3.0</td>\n",
              "      <td>836.0</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>250000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
              "0   1          60       RL  ...        WD         Normal  208500.0\n",
              "1   2          20       RL  ...        WD         Normal  181500.0\n",
              "2   3          60       RL  ...        WD         Normal  223500.0\n",
              "3   4          70       RL  ...        WD        Abnorml  140000.0\n",
              "4   5          60       RL  ...        WD         Normal  250000.0\n",
              "\n",
              "[5 rows x 81 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03hEztX7WG1N",
        "outputId": "da683fe3-4ec8-4732-d0e6-f779b63f59f6"
      },
      "source": [
        "print(df_combined.isnull().sum())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Id                  0\n",
            "MSSubClass          0\n",
            "MSZoning            4\n",
            "LotFrontage       486\n",
            "LotArea             0\n",
            "Street              0\n",
            "Alley            2721\n",
            "LotShape            0\n",
            "LandContour         0\n",
            "Utilities           2\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         1\n",
            "Exterior2nd         1\n",
            "MasVnrType         24\n",
            "MasVnrArea         23\n",
            "ExterQual           0\n",
            "ExterCond           0\n",
            "Foundation          0\n",
            "BsmtQual           81\n",
            "BsmtCond           82\n",
            "BsmtExposure       82\n",
            "BsmtFinType1       79\n",
            "BsmtFinSF1          1\n",
            "BsmtFinType2       80\n",
            "BsmtFinSF2          1\n",
            "BsmtUnfSF           1\n",
            "TotalBsmtSF         1\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        2\n",
            "BsmtHalfBath        2\n",
            "FullBath            0\n",
            "HalfBath            0\n",
            "BedroomAbvGr        0\n",
            "KitchenAbvGr        0\n",
            "KitchenQual         1\n",
            "TotRmsAbvGrd        0\n",
            "Functional          2\n",
            "Fireplaces          0\n",
            "FireplaceQu      1420\n",
            "GarageType        157\n",
            "GarageYrBlt       159\n",
            "GarageFinish      159\n",
            "GarageCars          1\n",
            "GarageArea          1\n",
            "GarageQual        159\n",
            "GarageCond        159\n",
            "PavedDrive          0\n",
            "WoodDeckSF          0\n",
            "OpenPorchSF         0\n",
            "EnclosedPorch       0\n",
            "3SsnPorch           0\n",
            "ScreenPorch         0\n",
            "PoolArea            0\n",
            "PoolQC           2909\n",
            "Fence            2348\n",
            "MiscFeature      2814\n",
            "MiscVal             0\n",
            "MoSold              0\n",
            "YrSold              0\n",
            "SaleType            1\n",
            "SaleCondition       0\n",
            "SalePrice        1459\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "gGo-iK6SxFhi",
        "outputId": "1c5ea031-b9b4-4f2a-9a85-8a25a97363ee"
      },
      "source": [
        "plt.figure(figsize=(18,6))\n",
        "plt.title('Heatmap of missing values')\n",
        "sns.heatmap(df_combined.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f78991d8b90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "ZHlIRcjc0yEi",
        "outputId": "17ff5dea-4af9-4b02-99db-fba1d13f4837"
      },
      "source": [
        "unique_values = []\n",
        "for col in object_cols:\n",
        "  unique_values.append(df_combined[col].unique().size)\n",
        "plt.figure(figsize=(18,6))\n",
        "plt.title('No. Unique values of Categorical Features')\n",
        "plt.xticks(rotation=90)\n",
        "sns.barplot(x=object_cols,y=unique_values)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f78907964d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "m6WrEiBe8TmX",
        "outputId": "9c76eed9-0478-4dd2-e485-c9d13c4ca956"
      },
      "source": [
        "plt.figure(figsize=(18,36))\n",
        "plt.title('Categorical Features: Distribution')\n",
        "plt.xticks(rotation=90)\n",
        "index = 1\n",
        "for col in object_cols:\n",
        "  y = df_combined[col].value_counts()\n",
        "  plt.subplot(11,4,index)\n",
        "  plt.xticks(rotation=90)\n",
        "  sns.barplot(x=list(y.index), y=y)\n",
        "  index +=1"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x2592 with 43 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JqW-2CYQKTOs"
      },
      "source": [
        "### Fill up missing values:\n",
        "\n",
        "* Drop the features 'Alley', 'Fence', and 'MiscFeature'.\n",
        "\n",
        "* Drop 'Utilities' feature, as all but one have the value 'AllPub'\n",
        "\n",
        "* All entries with missing 'FirePlaceQu' have 'Fireplaces' = 0. Hence fill missing values with 'NA'.\n",
        "\n",
        "* All but one entries with missing 'PoolQC' value have 'PoolArea' = 0. Use mode for missing value with non-zero PoolArea. Use 'NA' for the rest of the entries. \n",
        "\n",
        "* Basement features: Fill missing values with 'NA' or '0'.\n",
        "\n",
        "* Garage features: Fill missing values with 'NA' or '0'. \n",
        "\n",
        "* Remaining Integer and Real features: fill up missing values with mean of the corresponding feature.\n",
        "\n",
        "* Remaining Categorical features: fill up missing values with mode of the corresponding feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIelEeGHKei_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "848d73b9-fba7-41b3-9960-cc63261aecd9"
      },
      "source": [
        "df_combined.drop(columns='Id',inplace=True); print('Drop Id \\n')\n",
        "df_combined['MSZoning'] = df_combined['MSZoning'].fillna(df_combined['MSZoning'].mode()[0])\n",
        "df_combined['LotFrontage'] = df_combined['LotFrontage'].fillna(df_combined['LotFrontage'].mean())\n",
        "df_combined.drop(columns='Alley',inplace=True); print('Drop Alley \\n')\n",
        "\n",
        "#df_combined['Utilities'] = df_combined['Utilities'].fillna(df_combined['Utilities'].mode()[0])\n",
        "print(df_combined['Utilities'].value_counts())\n",
        "df_combined.drop(columns='Utilities',inplace=True); print('Drop Utilities \\n')\n",
        "\n",
        "df_combined['Exterior1st'] = df_combined['Exterior1st'].fillna(df_combined['Exterior1st'].mode()[0])\n",
        "df_combined['Exterior2nd'] = df_combined['Exterior2nd'].fillna(df_combined['Exterior2nd'].mode()[0])\n",
        "df_combined['MasVnrType'] = df_combined['MasVnrType'].fillna(df_combined['MasVnrType'].mode()[0])\n",
        "df_combined['MasVnrArea'] = df_combined['MasVnrArea'].fillna(df_combined['MasVnrArea'].mean())\n",
        "df_combined['Electrical'] = df_combined['Electrical'].fillna(df_combined['Electrical'].mode()[0])\n",
        "df_combined['KitchenQual'] = df_combined['KitchenQual'].fillna(df_combined['KitchenQual'].mode()[0])\n",
        "df_combined['Functional'] = df_combined['Functional'].fillna(df_combined['Functional'].mode()[0])\n",
        "\n",
        "#df_combined.loc[(df_combined['Fireplaces'] != 0) & (df_combined['FireplaceQu'].isnull()) ][['FireplaceQu','Fireplaces']]\n",
        "df_combined['FireplaceQu'] = df_combined['FireplaceQu'].fillna('NA'); print('FirePlaceQu: Fill NA values for missing values \\n')\n",
        "\n",
        "df_combined.loc[(df_combined['PoolQC'].isnull()) & df_combined['PoolArea']>0][['PoolQC','PoolArea']]\n",
        "df_combined.at[2599,'PoolQC'] = df_combined['PoolQC'].mode()[0]; print('PoolQC: Use mode for missing value with non-zero PoolAre \\n')\n",
        "df_combined['PoolQC'] = df_combined['PoolQC'].fillna('NA'); print('PoolQC: Use NA for remaining missing values \\n')\n",
        "\n",
        "df_combined['SaleType'].fillna(df_combined['SaleType'].mode()[0],inplace=True)\n",
        "df_combined.drop(columns=['Fence','MiscFeature','SalePrice'],inplace=True); print('Drop Fence, MiscFeature and SalePrice\\n')\n",
        "\n",
        "# Basement Features\n",
        "#df_combined.loc[df_combined['BsmtQual'].isnull()][['BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinSF1','BsmtFinType2','BsmtUnfSF','TotalBsmtSF','BsmtFullBath','BsmtHalfBath']].head()\n",
        "#df_combined.loc[df_combined['TotalBsmtSF'].isnull()][['BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinSF1','BsmtFinType2','BsmtFinSF2','BsmtUnfSF','TotalBsmtSF','BsmtFullBath','BsmtHalfBath']]\n",
        "print('Fill missing values of Basement features with NA or 0 \\n')\n",
        "df_combined['BsmtQual'] = df_combined['BsmtQual'].fillna('NA')\n",
        "df_combined['BsmtCond'] = df_combined['BsmtCond'].fillna('NA')\n",
        "df_combined['BsmtExposure'] = df_combined['BsmtExposure'].fillna('NA')\n",
        "df_combined['BsmtFinType1'] = df_combined['BsmtFinType1'].fillna('NA')\n",
        "df_combined['BsmtFinType2'] = df_combined['BsmtFinType2'].fillna('NA')\n",
        "\n",
        "df_combined['BsmtFinSF1'] = df_combined['BsmtFinSF1'].fillna(int(0))\n",
        "df_combined['BsmtFinSF2'] = df_combined['BsmtFinSF2'].fillna(int(0))\n",
        "df_combined['BsmtUnfSF'] = df_combined['BsmtUnfSF'].fillna(int(0))\n",
        "df_combined['TotalBsmtSF'] = df_combined['TotalBsmtSF'].fillna(int(0))\n",
        "df_combined['BsmtFullBath'] = df_combined['BsmtFullBath'].fillna(int(0))\n",
        "df_combined['BsmtHalfBath'] = df_combined['BsmtHalfBath'].fillna(int(0))\n",
        "\n",
        "# Garage Features\n",
        "# df_combined.loc[df_combined['GarageCond'].isnull()][['GarageType','GarageYrBlt','GarageFinish','GarageCars','GarageArea','GarageQual','GarageCond']].head()\n",
        "print('Fill missing values of Garage features with NA or 0 \\n')\n",
        "df_combined['GarageType'] = df_combined['GarageType'].fillna('NA')\n",
        "df_combined['GarageFinish'] = df_combined['GarageFinish'].fillna('NA')\n",
        "df_combined['GarageCond'] = df_combined['GarageCond'].fillna('NA')\n",
        "df_combined['GarageQual'] = df_combined['GarageQual'].fillna('NA')\n",
        "df_combined['GarageCars'] = df_combined['GarageCars'].fillna(int(0))\n",
        "df_combined['GarageArea'] = df_combined['GarageArea'].fillna(int(0))\n",
        "df_combined['GarageYrBlt'] = df_combined['GarageYrBlt'].fillna(int(0))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drop Id \n",
            "\n",
            "Drop Alley \n",
            "\n",
            "AllPub    2916\n",
            "NoSeWa       1\n",
            "Name: Utilities, dtype: int64\n",
            "Drop Utilities \n",
            "\n",
            "FirePlaceQu: Fill NA values for missing values \n",
            "\n",
            "PoolQC: Use mode for missing value with non-zero PoolAre \n",
            "\n",
            "PoolQC: Use NA for remaining missing values \n",
            "\n",
            "Drop Fence, MiscFeature and SalePrice\n",
            "\n",
            "Fill missing values of Basement features with NA or 0 \n",
            "\n",
            "Fill missing values of Garage features with NA or 0 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "id": "UNx2KxMW7pFx",
        "outputId": "218ba361-5b27-4a88-eb5c-9b9e4378ee31"
      },
      "source": [
        "df_combined.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</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>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>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</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>706.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</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>1.0</td>\n",
              "      <td>0.0</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>NA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</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>NA</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</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>None</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>978.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</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>0.0</td>\n",
              "      <td>1.0</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>2.0</td>\n",
              "      <td>460.0</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>NA</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</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>486.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</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>1.0</td>\n",
              "      <td>0.0</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>2.0</td>\n",
              "      <td>608.0</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>NA</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</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>None</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>216.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</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>1.0</td>\n",
              "      <td>0.0</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>3.0</td>\n",
              "      <td>642.0</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>NA</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</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>655.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</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>1.0</td>\n",
              "      <td>0.0</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>3.0</td>\n",
              "      <td>836.0</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>NA</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass MSZoning  LotFrontage  ...  YrSold SaleType SaleCondition\n",
              "0          60       RL         65.0  ...    2008       WD        Normal\n",
              "1          20       RL         80.0  ...    2007       WD        Normal\n",
              "2          60       RL         68.0  ...    2008       WD        Normal\n",
              "3          70       RL         60.0  ...    2006       WD       Abnorml\n",
              "4          60       RL         84.0  ...    2008       WD        Normal\n",
              "\n",
              "[5 rows x 75 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-m7HSZm_KsBp"
      },
      "source": [
        "Check that all missing values have been taken care of."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5L2LkbTtQ8xd",
        "outputId": "7dbcbe3b-d50f-4d48-b545-fff45043caae"
      },
      "source": [
        "print(df_combined.isnull().sum().sum())"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RaV5mg7yKiN9"
      },
      "source": [
        "## Data Preprocessing\n",
        "\n",
        "### Import Onehot encoder to encode categorical features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WXr4CqKUNCV"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sycSwssztEHI",
        "outputId": "f153081c-c08d-4b14-d7ea-915ed20adb07"
      },
      "source": [
        "s = (df_combined.dtypes == 'object')\n",
        "object_cols = list(s[s].index)\n",
        "print(\"Categorical variables:\")\n",
        "print(object_cols)\n",
        "print('No. of. categorical features: ',len(object_cols))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Categorical variables:\n",
            "['MSZoning', 'Street', 'LotShape', 'LandContour', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'SaleType', 'SaleCondition']\n",
            "No. of. categorical features:  39\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eW5pfSVDtOFl"
      },
      "source": [
        "OH_encoder = OneHotEncoder(sparse=False)\n",
        "OH_cols = pd.DataFrame(OH_encoder.fit_transform(df_combined[object_cols]))\n",
        "OH_cols.index = df_combined.index\n",
        "OH_cols.columns = OH_encoder.get_feature_names()\n",
        "df_final = df_combined.drop(object_cols, axis=1)\n",
        "df_final = pd.concat([df_final, OH_cols], axis=1)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "BQ079ui0uE-g",
        "outputId": "ca46fecd-ecff-449f-fdef-1d3de0d561c2"
      },
      "source": [
        "df_final.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>x0_C (all)</th>\n",
              "      <th>x0_FV</th>\n",
              "      <th>x0_RH</th>\n",
              "      <th>x0_RL</th>\n",
              "      <th>...</th>\n",
              "      <th>x31_Detchd</th>\n",
              "      <th>x31_NA</th>\n",
              "      <th>x32_Fin</th>\n",
              "      <th>x32_NA</th>\n",
              "      <th>x32_RFn</th>\n",
              "      <th>x32_Unf</th>\n",
              "      <th>x33_Ex</th>\n",
              "      <th>x33_Fa</th>\n",
              "      <th>x33_Gd</th>\n",
              "      <th>x33_NA</th>\n",
              "      <th>x33_Po</th>\n",
              "      <th>x33_TA</th>\n",
              "      <th>x34_Ex</th>\n",
              "      <th>x34_Fa</th>\n",
              "      <th>x34_Gd</th>\n",
              "      <th>x34_NA</th>\n",
              "      <th>x34_Po</th>\n",
              "      <th>x34_TA</th>\n",
              "      <th>x35_N</th>\n",
              "      <th>x35_P</th>\n",
              "      <th>x35_Y</th>\n",
              "      <th>x36_Ex</th>\n",
              "      <th>x36_Fa</th>\n",
              "      <th>x36_Gd</th>\n",
              "      <th>x36_NA</th>\n",
              "      <th>x37_COD</th>\n",
              "      <th>x37_CWD</th>\n",
              "      <th>x37_Con</th>\n",
              "      <th>x37_ConLD</th>\n",
              "      <th>x37_ConLI</th>\n",
              "      <th>x37_ConLw</th>\n",
              "      <th>x37_New</th>\n",
              "      <th>x37_Oth</th>\n",
              "      <th>x37_WD</th>\n",
              "      <th>x38_Abnorml</th>\n",
              "      <th>x38_AdjLand</th>\n",
              "      <th>x38_Alloca</th>\n",
              "      <th>x38_Family</th>\n",
              "      <th>x38_Normal</th>\n",
              "      <th>x38_Partial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>196.0</td>\n",
              "      <td>706.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</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>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>0.0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</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>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>162.0</td>\n",
              "      <td>486.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</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>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</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>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>350.0</td>\n",
              "      <td>655.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</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>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 287 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass  LotFrontage  LotArea  ...  x38_Family  x38_Normal  x38_Partial\n",
              "0          60         65.0     8450  ...         0.0         1.0          0.0\n",
              "1          20         80.0     9600  ...         0.0         1.0          0.0\n",
              "2          60         68.0    11250  ...         0.0         1.0          0.0\n",
              "3          70         60.0     9550  ...         0.0         0.0          0.0\n",
              "4          60         84.0    14260  ...         0.0         1.0          0.0\n",
              "\n",
              "[5 rows x 287 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RbNcDSCruO67",
        "outputId": "4328b8dc-afa4-41c6-e236-7af76abb211a"
      },
      "source": [
        "# Check that the shapes are consistent\n",
        "\n",
        "print('df_final shape:', df_final.shape)\n",
        "print('df_train shape:', df_Train.shape)\n",
        "print('df_test shape:',  df_Test.shape)\n",
        "\n",
        "X_Train = pd.DataFrame(df_final[:1460])\n",
        "X_Test  = pd.DataFrame(df_final[1460:])\n",
        "Y_Train = df_Train['SalePrice']\n",
        "\n",
        "print('\\nCheck that the datasets are consistent:\\n')\n",
        "print('X_train shape', X_Train.shape)\n",
        "print('Y_train shape:', Y_Train.shape)\n",
        "print('X_test shape:',  X_Test.shape)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "df_final shape: (2919, 287)\n",
            "df_train shape: (1460, 81)\n",
            "df_test shape: (1459, 80)\n",
            "\n",
            "Check that the datasets are consistent:\n",
            "\n",
            "X_train shape (1460, 287)\n",
            "Y_train shape: (1460,)\n",
            "X_test shape: (1459, 287)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbYPZZdwLkqU"
      },
      "source": [
        "## Model selection and prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itoVF6Vb8chB"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the training set into training and validation set\n",
        "\n",
        "X_train, X_valid, Y_train, Y_valid = train_test_split(X_Train, Y_Train, train_size=0.8, test_size=0.2,random_state=0)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pd5_heADBfFp",
        "outputId": "29c5c731-47ac-44e3-b6b6-c43d7c31c68b"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "model_RFR = RandomForestRegressor()\n",
        "model_RFR.fit(X_train, Y_train)\n",
        "Y_pred = model_RFR.predict(X_valid)\n",
        "print(mean_absolute_error(Y_valid, Y_pred))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "17698.124315068493\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvGLCJllOdIQ",
        "outputId": "f0944bb3-9865-4077-f4e1-f9d29db41160"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "model_GBR = GradientBoostingRegressor()\n",
        "model_GBR.fit(X_train, Y_train)\n",
        "Y_pred = model_GBR.predict(X_valid)\n",
        "print(mean_absolute_error(Y_valid, Y_pred))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "17099.051310281626\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0ER2AjMPIK9",
        "outputId": "3680262f-d95c-435a-842e-1eecf7f7b697"
      },
      "source": [
        "from sklearn.linear_model import SGDRegressor\n",
        "\n",
        "model_SGD = SGDRegressor()\n",
        "model_SGD.fit(X_train, Y_train)\n",
        "Y_pred = model_SGD.predict(X_valid)\n",
        "print(mean_absolute_error(Y_valid, Y_pred))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.815523337673585e+16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qzZ6rXmAc5Q",
        "outputId": "396a102b-6e5a-4cec-8875-5a4fda23a1a3"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from xgboost import XGBRegressor\n",
        "\n",
        "model_XGBR = XGBRegressor(learning_rate=0.03,n_estimators=200,objective='reg:squarederror')\n",
        "model_XGBR.fit(X_train,Y_train)\n",
        "Y_pred = model_XGBR.predict(X_valid)\n",
        "print(mean_absolute_error(Y_valid, Y_pred))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "17399.26773865582\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "R-6wwb9ISMHM",
        "outputId": "aeee54b3-1c03-4a3a-d703-d25f39642446"
      },
      "source": [
        "plt.figure()\n",
        "plt.title('Comparison of Sale Price of Predicted and Actual values')\n",
        "plt.scatter(Y_Train,model_RFR.predict(X_Train),label='Random Forest')\n",
        "plt.scatter(Y_Train,model_XGBR.predict(X_Train),label='XGB')\n",
        "plt.legend()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f788c706110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8ERKn8W-Qwa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81062b82-87e0-4e6d-8880-a1462f92c3c0"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "model = XGBRegressor()\n",
        "\n",
        "n_estimators   = [100, 200, 500]\n",
        "learning_rates = [0.03,0.1,0.3]\n",
        "objectives     = ['reg:squarederror']\n",
        "\n",
        "# Define the grid of hyperparameters to search\n",
        "hyperparameter_grid = {\n",
        "    'n_estimators' : n_estimators,\n",
        "    'learning_rate':learning_rates,\n",
        "    'objective' : objectives\n",
        "    }\n",
        "\n",
        "grid_cv = GridSearchCV(estimator = model,\n",
        "            param_grid = hyperparameter_grid,\n",
        "            scoring = 'neg_mean_absolute_error',\n",
        "            return_train_score = True)\n",
        "\n",
        "grid_cv.fit(X_Train,Y_Train)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=XGBRegressor(base_score=0.5, booster='gbtree',\n",
              "                                    colsample_bylevel=1, colsample_bynode=1,\n",
              "                                    colsample_bytree=1, gamma=0,\n",
              "                                    importance_type='gain', learning_rate=0.1,\n",
              "                                    max_delta_step=0, max_depth=3,\n",
              "                                    min_child_weight=1, missing=None,\n",
              "                                    n_estimators=100, n_jobs=1, nthread=None,\n",
              "                                    objective='reg:linear', random_state=0,\n",
              "                                    reg_alpha=0, reg_lambda=1,\n",
              "                                    scale_pos_weight=1, seed=None, silent=None,\n",
              "                                    subsample=1, verbosity=1),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'learning_rate': [0.03, 0.1, 0.3],\n",
              "                         'n_estimators': [100, 200, 500],\n",
              "                         'objective': ['reg:squarederror']},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
              "             scoring='neg_mean_absolute_error', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cyNAAInrUEo1",
        "outputId": "8e45fdc1-011c-4af6-9922-693232a14765"
      },
      "source": [
        "grid_cv.best_score_"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-15410.454291523973"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a33ytix6UHLm",
        "outputId": "66ca026c-e197-42bb-ef9c-c846af8453bf"
      },
      "source": [
        "grid_cv.best_estimator_"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
              "             max_depth=3, min_child_weight=1, missing=None, n_estimators=500,\n",
              "             n_jobs=1, nthread=None, objective='reg:squarederror',\n",
              "             random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
              "             seed=None, silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCZssZbMFvuo",
        "outputId": "c05882de-2503-4573-b0d2-da5c8fb60ee5"
      },
      "source": [
        "Y_pred = random_cv.predict(X_valid)\n",
        "print(mean_absolute_error(Y_valid, Y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16171.361154216609\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRKc1LWuUO8K",
        "outputId": "66afc105-f824-4fe1-8fa4-2446bab19990"
      },
      "source": [
        "regressor = grid_cv.best_estimator_\n",
        "Y_pred = regressor.predict(X_valid)\n",
        "print(mean_absolute_error(Y_valid, Y_pred))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5902.788273223459\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "9BBGLXdMUjjw",
        "outputId": "2ea6cf17-cbe8-4359-a06f-3f398198c9cb"
      },
      "source": [
        "plt.figure()\n",
        "plt.title('Comparison of Sale Price of Predicted and Actual values')\n",
        "plt.scatter(Y_Train,model_RFR.predict(X_Train),label='Random Forest')\n",
        "plt.scatter(Y_Train,model_XGBR.predict(X_Train),label='XGB')\n",
        "plt.scatter(Y_Train,regressor.predict(X_Train),label='Best model')\n",
        "plt.legend()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f788cb50750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9vKYpTBU6VS"
      },
      "source": [
        "## Preparation of Submission Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZK0ugZUYJzIN"
      },
      "source": [
        "Y_Pred = regressor.predict(X_Test)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQVnMr1zJ9T9",
        "outputId": "f6b896b2-f23f-480e-cf51-e343360ddaca"
      },
      "source": [
        "Y_Pred"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([126633.34, 168998.73, 181575.25, ..., 142767.14, 122064.92,\n",
              "       228735.19], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCaUOy8ZKAQX",
        "outputId": "b659ca22-1443-406f-cd95-be5620c8850c"
      },
      "source": [
        "Y_Pred.shape"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1459,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7r7rvYFKCoU"
      },
      "source": [
        "sub = pd.DataFrame()\n",
        "sub['Id'] = df_Test['Id']\n",
        "sub['SalePrice'] = Y_Pred"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "6yy__v8HKd-b",
        "outputId": "5679e2a4-4e48-43e3-e7b9-3158e0e358db"
      },
      "source": [
        "sub.head()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1461</td>\n",
              "      <td>126633.343750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1462</td>\n",
              "      <td>168998.734375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1463</td>\n",
              "      <td>181575.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>189381.921875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1465</td>\n",
              "      <td>172626.390625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Id      SalePrice\n",
              "0  1461  126633.343750\n",
              "1  1462  168998.734375\n",
              "2  1463  181575.250000\n",
              "3  1464  189381.921875\n",
              "4  1465  172626.390625"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "oZOYPHMDVOsU",
        "outputId": "371194f8-b423-4e90-f13e-08aa605409ff"
      },
      "source": [
        "sub.tail()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1454</th>\n",
              "      <td>2915</td>\n",
              "      <td>80406.585938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1455</th>\n",
              "      <td>2916</td>\n",
              "      <td>74899.859375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>2917</td>\n",
              "      <td>142767.140625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>2918</td>\n",
              "      <td>122064.921875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>2919</td>\n",
              "      <td>228735.187500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Id      SalePrice\n",
              "1454  2915   80406.585938\n",
              "1455  2916   74899.859375\n",
              "1456  2917  142767.140625\n",
              "1457  2918  122064.921875\n",
              "1458  2919  228735.187500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvYYlVc0KmCE"
      },
      "source": [
        "sub.to_csv('Submission.csv')"
      ],
      "execution_count": 61,
      "outputs": []
    }
  ]
}