Kaggle_House_Prices_Advanced_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "House Prices: Advanced Regression Techniques.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP7qng5OcWl1P7CyJiSYDGx",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArpanSM/MachineLearningHackathons/blob/master/Kaggle_House_Prices_Advanced_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9FgC0hNWKHp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "17a0abfe-9715-4682-e6d8-ac5eadeff6da"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "from scipy.stats import norm, skew \n",
        "\n",
        "import seaborn as sns\n",
        "color = sns.color_palette()\n",
        "sns.set_style('darkgrid')\n",
        "\n",
        "#ignore annoying warning (from sklearn and seaborn)\n",
        "import warnings\n",
        "def ignore_warn(*args, **kwargs):\n",
        "    pass\n",
        "warnings.warn = ignore_warn \n",
        "\n",
        "#Limiting floats output to 3 decimal points\n",
        "pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x)) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vu_rdCLdWKxM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Load data:\n",
        "train = pd.read_csv('train.csv')\n",
        "test = pd.read_csv('test.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-d6cfWqgyjj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "332c3a31-c6f8-4d78-8a33-8af32771d2c4"
      },
      "source": [
        "train.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>65.000</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>196.000</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>2003.000</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>80.000</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>0.000</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>1976.000</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>68.000</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>162.000</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>2001.000</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>60.000</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>0.000</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>1998.000</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>84.000</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>350.000</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>2000.000</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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPpFWF81hxMD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "e94efc13-4441-4ef9-d7c5-e2055de5534c"
      },
      "source": [
        "test.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1461</td>\n",
              "      <td>20</td>\n",
              "      <td>RH</td>\n",
              "      <td>80.000</td>\n",
              "      <td>11622</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NAmes</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1961</td>\n",
              "      <td>1961</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.000</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>Rec</td>\n",
              "      <td>468.000</td>\n",
              "      <td>LwQ</td>\n",
              "      <td>144.000</td>\n",
              "      <td>270.000</td>\n",
              "      <td>882.000</td>\n",
              "      <td>GasA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>896</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>896</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>5</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1961.000</td>\n",
              "      <td>Unf</td>\n",
              "      <td>1.000</td>\n",
              "      <td>730.000</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>140</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1462</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>81.000</td>\n",
              "      <td>14267</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NAmes</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1958</td>\n",
              "      <td>1958</td>\n",
              "      <td>Hip</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>108.000</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>923.000</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.000</td>\n",
              "      <td>406.000</td>\n",
              "      <td>1329.000</td>\n",
              "      <td>GasA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1329</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1329</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1958.000</td>\n",
              "      <td>Unf</td>\n",
              "      <td>1.000</td>\n",
              "      <td>312.000</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>393</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gar2</td>\n",
              "      <td>12500</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1463</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>74.000</td>\n",
              "      <td>13830</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>Gilbert</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1997</td>\n",
              "      <td>1998</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.000</td>\n",
              "      <td>TA</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>791.000</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.000</td>\n",
              "      <td>137.000</td>\n",
              "      <td>928.000</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>928</td>\n",
              "      <td>701</td>\n",
              "      <td>0</td>\n",
              "      <td>1629</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2</td>\n",
              "      <td>1</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>1997.000</td>\n",
              "      <td>Fin</td>\n",
              "      <td>2.000</td>\n",
              "      <td>482.000</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>212</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>78.000</td>\n",
              "      <td>9978</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>Gilbert</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1998</td>\n",
              "      <td>1998</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>20.000</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>602.000</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.000</td>\n",
              "      <td>324.000</td>\n",
              "      <td>926.000</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>926</td>\n",
              "      <td>678</td>\n",
              "      <td>0</td>\n",
              "      <td>1604</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2</td>\n",
              "      <td>1</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>Attchd</td>\n",
              "      <td>1998.000</td>\n",
              "      <td>Fin</td>\n",
              "      <td>2.000</td>\n",
              "      <td>470.000</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>360</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1465</td>\n",
              "      <td>120</td>\n",
              "      <td>RL</td>\n",
              "      <td>43.000</td>\n",
              "      <td>5005</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>HLS</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>StoneBr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>TwnhsE</td>\n",
              "      <td>1Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>1992</td>\n",
              "      <td>1992</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>None</td>\n",
              "      <td>0.000</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>ALQ</td>\n",
              "      <td>263.000</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1017.000</td>\n",
              "      <td>1280.000</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1280</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1280</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>5</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1992.000</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2.000</td>\n",
              "      <td>506.000</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>82</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Id  MSSubClass MSZoning  ...  YrSold  SaleType SaleCondition\n",
              "0  1461          20       RH  ...    2010        WD        Normal\n",
              "1  1462          20       RL  ...    2010        WD        Normal\n",
              "2  1463          60       RL  ...    2010        WD        Normal\n",
              "3  1464          60       RL  ...    2010        WD        Normal\n",
              "4  1465         120       RL  ...    2010        WD        Normal\n",
              "\n",
              "[5 rows x 80 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mo6edYb7hzjf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "acf2e40b-4d74-42c2-d2e5-665c3d48e65e"
      },
      "source": [
        "#check the numbers of samples and features\n",
        "print(\"The train data size before dropping Id feature is : {} \".format(train.shape))\n",
        "print(\"The test data size before dropping Id feature is : {} \".format(test.shape))\n",
        "\n",
        "#Save the 'Id' column\n",
        "train_ID = train['Id']\n",
        "test_ID = test['Id']\n",
        "\n",
        "#Now drop the  'Id' colum since it's unnecessary for  the prediction process.\n",
        "train.drop(\"Id\", axis = 1, inplace = True)\n",
        "test.drop(\"Id\", axis = 1, inplace = True)\n",
        "\n",
        "#check again the data size after dropping the 'Id' variable\n",
        "print(\"\\nThe train data size after dropping Id feature is : {} \".format(train.shape)) \n",
        "print(\"The test data size after dropping Id feature is : {} \".format(test.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The train data size before dropping Id feature is : (1460, 81) \n",
            "The test data size before dropping Id feature is : (1459, 80) \n",
            "\n",
            "The train data size after dropping Id feature is : (1460, 80) \n",
            "The test data size after dropping Id feature is : (1459, 79) \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BX1j7fach9Yg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xuQczVDRiA0b",
        "colab_type": "text"
      },
      "source": [
        "# Data Processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7OOhmckciDpU",
        "colab_type": "text"
      },
      "source": [
        "## Outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SITXzRCiBiP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "830ff68c-5201-47f3-b664-5676256bdafe"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.scatter(x = train['GrLivArea'], y = train['SalePrice'])\n",
        "plt.ylabel('SalePrice', fontsize=13)\n",
        "plt.xlabel('GrLivArea', fontsize=13)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o6TEZTdcibyb",
        "colab_type": "text"
      },
      "source": [
        "We can see at the bottom right two with extremely large GrLivArea that are of a low price. These values are huge oultliers. Therefore, we can safely delete them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXNBYWKeiYEP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "8d3acc9c-7984-4678-a789-e11f5101c479"
      },
      "source": [
        "#Deleting outliers\n",
        "train = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index)\n",
        "#Check the graphic again\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(train['GrLivArea'], train['SalePrice'])\n",
        "plt.ylabel('SalePrice', fontsize=13)\n",
        "plt.xlabel('GrLivArea', fontsize=13)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bukGolzxi1Sb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sL3cxWMbjAat",
        "colab_type": "text"
      },
      "source": [
        "## Target Variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LeteJ6eQjL9i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "1ff118dc-515f-4b5d-f26e-815e3ac39b0a"
      },
      "source": [
        "sns.distplot(train['SalePrice'] , fit=norm);\n",
        "\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(train['SalePrice'])\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "#Now plot the distribution\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('SalePrice distribution')\n",
        "\n",
        "#Get also the QQ-plot\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(train['SalePrice'], plot=plt)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " mu = 180932.92 and sigma = 79467.79\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXcgpKjRkic_",
        "colab_type": "text"
      },
      "source": [
        "The target variable is right skewed. As (linear) models love normally distributed data , we need to transform this variable and make it more normally distributed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8dnzL4SjwgE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "6290ee3b-ac21-465e-bc0e-848236979ee7"
      },
      "source": [
        "#We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
        "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
        "\n",
        "#Check the new distribution \n",
        "sns.distplot(train['SalePrice'] , fit=norm);\n",
        "\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(train['SalePrice'])\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "#Now plot the distribution\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('SalePrice distribution')\n",
        "\n",
        "#Get also the QQ-plot\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(train['SalePrice'], plot=plt)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " mu = 12.02 and sigma = 0.40\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iyJKFbzgk2wf",
        "colab_type": "text"
      },
      "source": [
        "The skew seems now corrected and the data appears more normally distributed.\n",
        "\n",
        "1.   List item\n",
        "2.   List item\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mdn8RXYPkwAg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kS6jl1eUk4v1",
        "colab_type": "text"
      },
      "source": [
        "## Features engineering¶\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MpqUtFRqlC_g",
        "colab_type": "text"
      },
      "source": [
        "let's first concatenate the train and test data in the same dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndK0g7Dok5dK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b5ec7df0-604a-4898-dac1-f84e7a3cfb20"
      },
      "source": [
        "ntrain = train.shape[0]\n",
        "ntest = test.shape[0]\n",
        "y_train = train.SalePrice.values\n",
        "all_data = pd.concat((train, test)).reset_index(drop=True)\n",
        "all_data.drop(['SalePrice'], axis=1, inplace=True)\n",
        "print(\"all_data size is : {}\".format(all_data.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "all_data size is : (2917, 79)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPJjyJcMlScN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_data_na = (all_data.isnull().sum() / len(all_data)) * 100\n",
        "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)[:30]\n",
        "missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\n",
        "missing_data.head(20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upEPpAfwlpA1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "outputId": "fe367cf9-28d1-446d-fa90-bc6baa7e62aa"
      },
      "source": [
        "#Correlation map to see how features are correlated with SalePrice\n",
        "corrmat = train.corr()\n",
        "plt.subplots(figsize=(12,9))\n",
        "sns.heatmap(corrmat, vmax=0.9, square=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0dedd1c0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6IMNd6AmcIf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REzrNo2tm4Qz",
        "colab_type": "text"
      },
      "source": [
        "# Imputing missing values¶"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iM95h9Shm8rX",
        "colab_type": "text"
      },
      "source": [
        "PoolQC : data description says NA means \"No Pool\". That make sense, given the huge ratio of missing value (+99%) and majority of houses have no Pool at all in general."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MhCQcvpm8Np",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_data[\"PoolQC\"] = all_data[\"PoolQC\"].fillna(\"None\")\n",
        "all_data[\"MiscFeature\"] = all_data[\"MiscFeature\"].fillna(\"None\")\n",
        "all_data[\"Alley\"] = all_data[\"Alley\"].fillna(\"None\")\n",
        "all_data[\"Fence\"] = all_data[\"Fence\"].fillna(\"None\")\n",
        "all_data[\"FireplaceQu\"] = all_data[\"FireplaceQu\"].fillna(\"None\")\n",
        "for col in ('GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'):\n",
        "    all_data[col] = all_data[col].fillna('None')\n",
        "\n",
        "for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n",
        "    all_data[col] = all_data[col].fillna(0)\n",
        "\n",
        "for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'):\n",
        "    all_data[col] = all_data[col].fillna(0)\n",
        "\n",
        "for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):\n",
        "    all_data[col] = all_data[col].fillna('None')\n",
        "    \n",
        "#MasVnrArea and MasVnrType : NA most likely means no masonry veneer for these houses. We can fill 0 for the area and None for the type.\n",
        "all_data[\"MasVnrType\"] = all_data[\"MasVnrType\"].fillna(\"None\")\n",
        "all_data[\"MasVnrArea\"] = all_data[\"MasVnrArea\"].fillna(0)\n",
        "\n",
        "#MSZoning (The general zoning classification) : 'RL' is by far the most common value. So we can fill in missing values with 'RL'\n",
        "all_data['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode()[0])\n",
        "\n",
        "#Utilities : For this categorical feature all records are \"AllPub\", except for one \"NoSeWa\" and 2 NA . Since the house with 'NoSewa' is in the training set, this feature won't help in predictive modelling. We can then safely remove it.\n",
        "all_data = all_data.drop(['Utilities'], axis=1)\n",
        "\n",
        "#Functional : data description says NA means typical\n",
        "all_data[\"Functional\"] = all_data[\"Functional\"].fillna(\"Typ\")\n",
        "\n",
        "#Electrical : It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value.\n",
        "all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])\n",
        "\n",
        "#KitchenQual: Only one NA value, and same as Electrical, we set 'TA' (which is the most frequent) for the missing value in KitchenQual.\n",
        "all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])\n",
        "\n",
        "#Exterior1st and Exterior2nd : Again Both Exterior 1 & 2 have only one missing value. We will just substitute in the most common string\n",
        "all_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0])\n",
        "all_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0])\n",
        "\n",
        "#SaleType : Fill in again with most frequent which is \"WD\"\n",
        "all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])\n",
        "\n",
        "#MSSubClass : Na most likely means No building class. We can replace missing values with None\n",
        "all_data['MSSubClass'] = all_data['MSSubClass'].fillna(\"None\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fka-iZmeoETr",
        "colab_type": "text"
      },
      "source": [
        "LotFrontage : Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood , we can fill in missing values by the median LotFrontage of the neighborhood."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DduSXtkvnLFz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_data[\"LotFrontage\"] = all_data.groupby(\"Neighborhood\")[\"LotFrontage\"].transform(lambda x: x.fillna(x.median()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3qfcOmJnb6t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "63bc32cd-ca5a-4192-8177-4ebefd9c6130"
      },
      "source": [
        "#Check remaining missing values if any \n",
        "all_data_na = (all_data.isnull().sum() / len(all_data)) * 100\n",
        "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)\n",
        "missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\n",
        "missing_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Missing Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Missing Ratio]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaIdaAi5qxv8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0KwMgGXrBbI",
        "colab_type": "text"
      },
      "source": [
        "# Transforming some numerical variables that are really categorical\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8CTCYwzrCh5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#MSSubClass=The building class\n",
        "all_data['MSSubClass'] = all_data['MSSubClass'].apply(str)\n",
        "\n",
        "#Changing OverallCond into a categorical variable\n",
        "all_data['OverallCond'] = all_data['OverallCond'].astype(str)\n",
        "\n",
        "#Year and month sold are transformed into categorical features.\n",
        "all_data['YrSold'] = all_data['YrSold'].astype(str)\n",
        "all_data['MoSold'] = all_data['MoSold'].astype(str)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIm6JY-WrfKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "axP71VBLrg0F",
        "colab_type": "text"
      },
      "source": [
        "# Label Encoding some categorical variables that may contain information in their ordering set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWz-Mo-xrhf3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1ead1aca-788e-479f-f0b9-91097f5048e8"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "cols = ('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond', \n",
        "        'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1', \n",
        "        'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',\n",
        "        'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond', \n",
        "        'YrSold', 'MoSold')\n",
        "\n",
        "# process columns, apply LabelEncoder to categorical features\n",
        "for c in cols:\n",
        "    lbl = LabelEncoder() \n",
        "    lbl.fit(list(all_data[c].values)) \n",
        "    all_data[c] = lbl.transform(list(all_data[c].values))\n",
        "\n",
        "# shape        \n",
        "print('Shape all_data: {}'.format(all_data.shape))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape all_data: (2917, 78)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xl0qemw_sIQf",
        "colab_type": "text"
      },
      "source": [
        "Since area related features are very important to determine house prices, we add one more feature which is the total area of basement, first and second floor areas of each house"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUiAZuPrsEsk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Adding total sqfootage feature \n",
        "all_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data['1stFlrSF'] + all_data['2ndFlrSF']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-va68zT9sLTM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aGmueVsXsqj9",
        "colab_type": "text"
      },
      "source": [
        "Skewed features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i93rE3wlsrx-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "dbd722c6-f970-4844-a5fe-e09a6685bd26"
      },
      "source": [
        "numeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\n",
        "# Check the skew of all numerical features\n",
        "skewed_feats = all_data[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
        "print(\"\\nSkew in numerical features: \\n\")\n",
        "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
        "skewness.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Skew in numerical features: \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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>Skew</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MiscVal</th>\n",
              "      <td>21.940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PoolArea</th>\n",
              "      <td>17.689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LotArea</th>\n",
              "      <td>13.109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <td>12.085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3SsnPorch</th>\n",
              "      <td>11.372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LandSlope</th>\n",
              "      <td>4.973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <td>4.301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <td>4.145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <td>4.002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ScreenPorch</th>\n",
              "      <td>3.945</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Skew\n",
              "MiscVal       21.940\n",
              "PoolArea      17.689\n",
              "LotArea       13.109\n",
              "LowQualFinSF  12.085\n",
              "3SsnPorch     11.372\n",
              "LandSlope      4.973\n",
              "KitchenAbvGr   4.301\n",
              "BsmtFinSF2     4.145\n",
              "EnclosedPorch  4.002\n",
              "ScreenPorch    3.945"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CliTb7D6tkIu",
        "colab_type": "text"
      },
      "source": [
        "Box Cox Transformation of (highly) skewed features\n",
        "\n",
        "We use the scipy function boxcox1p which computes the Box-Cox transformation of  1+x .\n",
        "\n",
        "Note that setting  λ=0  is equivalent to log1p used above for the target variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ru9nryb1tRas",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f350bf8b-8d1e-4614-e2c4-d3cb6fecd63d"
      },
      "source": [
        "skewness = skewness[abs(skewness) > 0.75]\n",
        "print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))\n",
        "\n",
        "from scipy.special import boxcox1p\n",
        "skewed_features = skewness.index\n",
        "lam = 0.15\n",
        "for feat in skewed_features:\n",
        "    #all_data[feat] += 1\n",
        "    all_data[feat] = boxcox1p(all_data[feat], lam)\n",
        "    \n",
        "#all_data[skewed_features] = np.log1p(all_data[skewed_features])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 59 skewed numerical features to Box Cox transform\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WU5Q1AQCt4FS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1a09ca27-3bda-47d3-cea4-7496cb2a4b65"
      },
      "source": [
        "#Getting dummy categorical features\n",
        "all_data = pd.get_dummies(all_data)\n",
        "print(all_data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2917, 220)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIldnqgXt9q3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = all_data[:ntrain]\n",
        "test = all_data[ntrain:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CrDDiRChuMy0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LFpD9MHguaw0",
        "colab_type": "text"
      },
      "source": [
        "# **Modelling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIhcWzLludli",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
        "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
        "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlDlLgd2wbhI",
        "colab_type": "text"
      },
      "source": [
        "Define a cross validation strategy\n",
        "\n",
        "We use the cross_val_score function of Sklearn. However this function has not a shuffle attribut, we add then one line of code, in order to shuffle the dataset prior to cross-validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6se5CqZEuebK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Validation function\n",
        "n_folds = 5\n",
        "\n",
        "def rmsle_cv(model):\n",
        "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n",
        "    rmse= np.sqrt(-cross_val_score(model, train.values, y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n",
        "    return(rmse)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IcKJrEpUwoVp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4KzeB6kxFFB",
        "colab_type": "text"
      },
      "source": [
        "Base Models\n",
        "\n",
        "This model may be very sensitive to outliers. So we need to made it more robust on them. For that we use the sklearn's Robustscaler() method on pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgKlSjssxGV0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6SVII5xxJlY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e23pyFuaxOvr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mE_PYdF9xSTR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jeJMSXFMxVM9",
        "colab_type": "text"
      },
      "source": [
        "Gradient Boosting Regression :\n",
        "With huber loss that makes it robust to outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWQAP1z0xVr5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
        "                                   max_depth=4, max_features='sqrt',\n",
        "                                   min_samples_leaf=15, min_samples_split=10, \n",
        "                                   loss='huber', random_state =5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKw_DILTxeEw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
        "                             learning_rate=0.05, max_depth=3, \n",
        "                             min_child_weight=1.7817, n_estimators=2200,\n",
        "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
        "                             subsample=0.5213, silent=1,\n",
        "                             random_state =7, nthread = -1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tD1MG5QgxkC7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
        "                              learning_rate=0.05, n_estimators=720,\n",
        "                              max_bin = 55, bagging_fraction = 0.8,\n",
        "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
        "                              feature_fraction_seed=9, bagging_seed=9,\n",
        "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDeZil1Pxs8Y",
        "colab_type": "text"
      },
      "source": [
        "Base models scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3u_uOzJxrO2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "94cc20db-9ad5-4846-8ed7-d8c5a6f2415c"
      },
      "source": [
        "score = rmsle_cv(lasso)\n",
        "print(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Lasso score: 0.1115 (0.0074)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xtjEhHMxwUM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "595679d9-0595-4f51-b8fd-3b53229710ae"
      },
      "source": [
        "score = rmsle_cv(ENet)\n",
        "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ElasticNet score: 0.1116 (0.0074)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4gICMImnx5V-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c4622a98-25e0-403d-d187-65d935c4d0b4"
      },
      "source": [
        "score = rmsle_cv(KRR)\n",
        "print(\"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Kernel Ridge score: 0.1153 (0.0075)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OG-MYJQx8Gu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "5df65c7f-7588-4ec3-ddaa-cace823b8605"
      },
      "source": [
        "score = rmsle_cv(GBoost)\n",
        "print(\"Gradient Boosting score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gradient Boosting score: 0.1167 (0.0083)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jx34_eWx9oy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "fa750735-c5f6-48c5-d37d-c1310c7ecc45"
      },
      "source": [
        "score = rmsle_cv(model_xgb)\n",
        "print(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Xgboost score: 0.1150 (0.0066)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "70kaNKZpx_Pi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "79c0ef36-bca7-466e-9d6c-69999d7ab8bf"
      },
      "source": [
        "score = rmsle_cv(model_lgb)\n",
        "print(\"LGBM score: {:.4f} ({:.4f})\\n\" .format(score.mean(), score.std()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LGBM score: 0.1176 (0.0062)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVCl5_4zyAlK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LzAFHrV4yBuk",
        "colab_type": "text"
      },
      "source": [
        "# Stacking models¶\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4-1BE0iyC5A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
        "    def __init__(self, models):\n",
        "        self.models = models\n",
        "        \n",
        "    # we define clones of the original models to fit the data in\n",
        "    def fit(self, X, y):\n",
        "        self.models_ = [clone(x) for x in self.models]\n",
        "        \n",
        "        # Train cloned base models\n",
        "        for model in self.models_:\n",
        "            model.fit(X, y)\n",
        "\n",
        "        return self\n",
        "    \n",
        "    #Now we do the predictions for cloned models and average them\n",
        "    def predict(self, X):\n",
        "        predictions = np.column_stack([\n",
        "            model.predict(X) for model in self.models_\n",
        "        ])\n",
        "        return np.mean(predictions, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLlBHmE-yXgX",
        "colab_type": "text"
      },
      "source": [
        "Averaged base models score\n",
        "\n",
        "We just average four models here ENet, GBoost, KRR and lasso. Of course we could easily add more models in the mix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpDwIX4IyVeH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "e12ed428-c416-45c6-ccb5-0cd1a9bf1a36"
      },
      "source": [
        "averaged_models = AveragingModels(models = (ENet, GBoost, KRR, lasso))\n",
        "score = rmsle_cv(averaged_models)\n",
        "print(\" Averaged base models score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Averaged base models score: 0.1087 (0.0077)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2_xxTc0FyrKi",
        "colab_type": "text"
      },
      "source": [
        "Wow ! It seems even the simplest stacking approach really improve the score . This encourages us to go further and explore a less simple stacking approch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0myhzRoylS8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zeaV3FSkzl1J",
        "colab_type": "text"
      },
      "source": [
        "# Ensembling StackedRegressor, XGBoost and LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndp7HlxGznSQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def rmsle(y, y_pred):\n",
        "    return np.sqrt(mean_squared_error(y, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFxTdzyt0TP3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0e5e3011-ce7f-467f-e7e7-509578871461"
      },
      "source": [
        "model_xgb.fit(train, y_train)\n",
        "xgb_train_pred = model_xgb.predict(train)\n",
        "xgb_pred = np.expm1(model_xgb.predict(test))\n",
        "print(rmsle(y_train, xgb_train_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0788580427618937\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Z6FJCYu0k9C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "89001654-4b87-4bf7-cafd-52e56853084b"
      },
      "source": [
        "model_lgb.fit(train, y_train)\n",
        "lgb_train_pred = model_lgb.predict(train)\n",
        "lgb_pred = np.expm1(model_lgb.predict(test.values))\n",
        "print(rmsle(y_train, lgb_train_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.07294132053731643\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Eq-lESG0o_z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}