Research and Development/ml-pipeline-feature-engineering-opensource.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering with open source\n",
    "\n",
    "In this notebook we reproduce feature engineering pipeline from notebook 2 using [scikit learn](https://scikit-learn.org/) and [feature engine](https://feature-engine.readthedocs.io/en/1.2.x/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# saving pipeline\n",
    "import joblib\n",
    "\n",
    "# scikit-learn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, Binarizer\n",
    "\n",
    "# feature-engine\n",
    "from feature_engine.imputation import(\n",
    "    AddMissingIndicator,\n",
    "    MeanMedianImputer,\n",
    "    CategoricalImputer\n",
    ")\n",
    "\n",
    "from feature_engine.encoding import (\n",
    "    RareLabelEncoder,\n",
    "    OrdinalEncoder\n",
    ")\n",
    "\n",
    "from feature_engine.transformation import (\n",
    "    LogTransformer,\n",
    "    YeoJohnsonTransformer\n",
    ")\n",
    "\n",
    "\n",
    "from feature_engine.selection import DropFeatures\n",
    "from feature_engine.wrappers import SklearnTransformerWrapper\n",
    "\n",
    "\n",
    "pd.pandas.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3         Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4         Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "\n",
       "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0       Norm     1Fam     2Story            7            5       2003   \n",
       "1       Norm     1Fam     1Story            6            8       1976   \n",
       "2       Norm     1Fam     2Story            7            5       2001   \n",
       "3       Norm     1Fam     2Story            7            5       1915   \n",
       "4       Norm     1Fam     2Story            8            5       2000   \n",
       "\n",
       "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1          1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2          2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3          1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4          2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "\n",
       "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
       "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
       "2       162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
       "3         0.0        TA        TA     BrkTil       TA       Gd           No   \n",
       "4       350.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "\n",
       "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0          GLQ         706          Unf           0        150          856   \n",
       "1          ALQ         978          Unf           0        284         1262   \n",
       "2          GLQ         486          Unf           0        434          920   \n",
       "3          ALQ         216          Unf           0        540          756   \n",
       "4          GLQ         655          Unf           0        490         1145   \n",
       "\n",
       "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
       "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
       "2    GasA        Ex          Y      SBrkr       920       866             0   \n",
       "3    GasA        Gd          Y      SBrkr       961       756             0   \n",
       "4    GasA        Ex          Y      SBrkr      1145      1053             0   \n",
       "\n",
       "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0       1710             1             0         2         1             3   \n",
       "1       1262             0             1         2         0             3   \n",
       "2       1786             1             0         2         1             3   \n",
       "3       1717             1             0         1         0             3   \n",
       "4       2198             1             0         2         1             4   \n",
       "\n",
       "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0             1          Gd             8        Typ           0         NaN   \n",
       "1             1          TA             6        Typ           1          TA   \n",
       "2             1          Gd             6        Typ           1          TA   \n",
       "3             1          Gd             7        Typ           1          Gd   \n",
       "4             1          Gd             9        Typ           1          TA   \n",
       "\n",
       "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0     Attchd       2003.0          RFn           2         548         TA   \n",
       "1     Attchd       1976.0          RFn           2         460         TA   \n",
       "2     Attchd       2001.0          RFn           2         608         TA   \n",
       "3     Detchd       1998.0          Unf           3         642         TA   \n",
       "4     Attchd       2000.0          RFn           3         836         TA   \n",
       "\n",
       "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0         TA          Y           0           61              0          0   \n",
       "1         TA          Y         298            0              0          0   \n",
       "2         TA          Y           0           42              0          0   \n",
       "3         TA          Y           0           35            272          0   \n",
       "4         TA          Y         192           84              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
       "0            0         0    NaN   NaN         NaN        0       2    2008   \n",
       "1            0         0    NaN   NaN         NaN        0       5    2007   \n",
       "2            0         0    NaN   NaN         NaN        0       9    2008   \n",
       "3            0         0    NaN   NaN         NaN        0       2    2006   \n",
       "4            0         0    NaN   NaN         NaN        0      12    2008   \n",
       "\n",
       "  SaleType SaleCondition  SalePrice  \n",
       "0       WD        Normal     208500  \n",
       "1       WD        Normal     181500  \n",
       "2       WD        Normal     223500  \n",
       "3       WD       Abnorml     140000  \n",
       "4       WD        Normal     250000  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('train.csv')\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Separate dataset into train and test set\n",
    "\n",
    "Some feature engineering technique will require learning from our data. For this purpose it is important we learn from our train data (not both) to avoid overfitting\n",
    "\n",
    "- mean\n",
    "- mode\n",
    "- exponent for yeo-johnson\n",
    "- category frequency\n",
    "- and category to number mappings\n",
    "\n",
    "\n",
    "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1314, 79), (146, 79))"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(['Id', 'SalePrice'], axis=1), #predictor\n",
    "    data['SalePrice'], #target\n",
    "    test_size=0.1, # 10% for testing\n",
    "    random_state=0 # seed -> for reproduceability\n",
    ")\n",
    "\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "\n",
    "We will carry out feature engineering in to take care of the following\n",
    "\n",
    "1. Missing values\n",
    "2. Temporal variables\n",
    "3. Non-Gaussian distributed variables\n",
    "4. Categorical variables: remove rare labels\n",
    "5. Categorical variables: convert strings to numbers\n",
    "5. Standardize the values of the variables to the same range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.log(y_train)\n",
    "y_test = np.log(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing values\n",
    "### Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_vars = [var for var in data.columns if data[var].dtype == 'O']\n",
    "\n",
    "cat_vars = cat_vars + ['MSSubClass']\n",
    "\n",
    "\n",
    "X_train[cat_vars] = X_train[cat_vars].astype('O')\n",
    "X_test[cat_vars] = X_test[cat_vars].astype('O')\n",
    "\n",
    "\n",
    "len(cat_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC          0.995434\n",
       "MiscFeature     0.961187\n",
       "Alley           0.938356\n",
       "Fence           0.814307\n",
       "FireplaceQu     0.472603\n",
       "GarageType      0.056317\n",
       "GarageFinish    0.056317\n",
       "GarageQual      0.056317\n",
       "GarageCond      0.056317\n",
       "BsmtExposure    0.025114\n",
       "BsmtFinType2    0.025114\n",
       "BsmtQual        0.024353\n",
       "BsmtCond        0.024353\n",
       "BsmtFinType1    0.024353\n",
       "MasVnrType      0.004566\n",
       "Electrical      0.000761\n",
       "dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_vars_with_na = [\n",
    "    var for var in cat_vars\n",
    "    if X_train[var].isnull().sum() > 0\n",
    "]\n",
    "\n",
    "\n",
    "X_train[cat_vars_with_na].isnull().mean().sort_values(\n",
    "    ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for columns with missing value greater than 10%\n",
    "# replace with 'missing'\n",
    "# While for those less than 10% replace with the first mode\n",
    "\n",
    "\n",
    "with_string_missing = [\n",
    "    var for var in cat_vars_with_na if X_train[var].isnull().mean()\n",
    "    > 0.1 \n",
    "]\n",
    "\n",
    "\n",
    "with_mode_missing = [\n",
    "    var for var in cat_vars_with_na if X_train[var].isnull().mean()\n",
    "    < 0.1\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_string_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alley': 'Missing',\n",
       " 'FireplaceQu': 'Missing',\n",
       " 'PoolQC': 'Missing',\n",
       " 'Fence': 'Missing',\n",
       " 'MiscFeature': 'Missing'}"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace missing value with label: \"missing\"\n",
    "\n",
    "cat_imputer_missing = CategoricalImputer(\n",
    "    imputation_method='missing', variables=with_string_missing\n",
    ")\n",
    "\n",
    "cat_imputer_missing.fit(X_train)\n",
    "\n",
    "cat_imputer_missing.imputer_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace NA with missing\n",
    "\n",
    "X_train = cat_imputer_missing.transform(X_train)\n",
    "X_test = cat_imputer_missing.transform(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MasVnrType': 'None',\n",
       " 'BsmtQual': 'TA',\n",
       " 'BsmtCond': 'TA',\n",
       " 'BsmtExposure': 'No',\n",
       " 'BsmtFinType1': 'Unf',\n",
       " 'BsmtFinType2': 'Unf',\n",
       " 'Electrical': 'SBrkr',\n",
       " 'GarageType': 'Attchd',\n",
       " 'GarageFinish': 'Unf',\n",
       " 'GarageQual': 'TA',\n",
       " 'GarageCond': 'TA'}"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_imputer_mode = CategoricalImputer(\n",
    "    imputation_method='frequent', variables=with_mode_missing\n",
    ")\n",
    "\n",
    "cat_imputer_mode.fit(X_train)\n",
    "\n",
    "cat_imputer_mode.imputer_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = cat_imputer_mode.transform(X_train)\n",
    "X_test = cat_imputer_mode.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alley           0\n",
       "MasVnrType      0\n",
       "BsmtQual        0\n",
       "BsmtCond        0\n",
       "BsmtExposure    0\n",
       "BsmtFinType1    0\n",
       "BsmtFinType2    0\n",
       "Electrical      0\n",
       "FireplaceQu     0\n",
       "GarageType      0\n",
       "GarageFinish    0\n",
       "GarageQual      0\n",
       "GarageCond      0\n",
       "PoolQC          0\n",
       "Fence           0\n",
       "MiscFeature     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[cat_vars_with_na].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\n",
    "    var for var in cat_vars_with_na if X_test[var].isnull().sum()\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical variables\n",
    "\n",
    "For numerical variable we will\n",
    "\n",
    "- add a binary indicator column\n",
    "- replace the missing va;ue in the original variable with the meaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_vars = [\n",
    "    var for var in X_train.columns if var not in cat_vars\n",
    "]\n",
    "\n",
    "len(num_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage    0.177321\n",
       "MasVnrArea     0.004566\n",
       "GarageYrBlt    0.056317\n",
       "dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_with_na = [\n",
    "    var for var in num_vars if X_train[var].isnull().sum() > 0\n",
    "]\n",
    "\n",
    "\n",
    "X_train[var_with_na].isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_with_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_ind = AddMissingIndicator(variables=var_with_na)\n",
    "\n",
    "missing_ind_var = missing_ind.fit(X_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_ind_var.variables_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "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>LotFrontage_na</th>\n",
       "      <th>MasVnrArea_na</th>\n",
       "      <th>GarageYrBlt_na</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      LotFrontage_na  MasVnrArea_na  GarageYrBlt_na\n",
       "930                0              0               0\n",
       "656                0              0               0\n",
       "45                 0              0               0\n",
       "1348               1              0               0"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = missing_ind.transform(X_train)\n",
    "X_test = missing_ind.transform(X_test)\n",
    "\n",
    "X_train[['LotFrontage_na', 'MasVnrArea_na', 'GarageYrBlt_na']].head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LotFrontage': 69.87974098057354,\n",
       " 'MasVnrArea': 103.7974006116208,\n",
       " 'GarageYrBlt': 1978.2959677419356}"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_imputer = MeanMedianImputer(\n",
    "    imputation_method='mean', variables=var_with_na\n",
    ")\n",
    "\n",
    "mean_imputer.fit(X_train)\n",
    "\n",
    "mean_imputer.imputer_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage    0\n",
       "MasVnrArea     0\n",
       "GarageYrBlt    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = mean_imputer.transform(X_train)\n",
    "X_test = mean_imputer.transform(X_test)\n",
    "\n",
    "\n",
    "X_train[var_with_na].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def elapsed_years(df, var):\n",
    "\n",
    "    df[var] = df['YrSold'] - df[var]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:\n",
    "\n",
    "    X_train = elapsed_years(X_train, var)\n",
    "    X_test = elapsed_years(X_test, var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_features = DropFeatures(features_to_drop=['YrSold'])\n",
    "\n",
    "\n",
    "X_train = drop_features.fit_transform(X_train)\n",
    "X_test = drop_features.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical variable transformation\n",
    "\n",
    "### Logarithmic transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_transformer = LogTransformer(\n",
    "    variables=[\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]\n",
    ")\n",
    "\n",
    "X_train = log_transformer.fit_transform(X_train)\n",
    "X_test = log_transformer.fit_transform(X_test)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] \n",
    "\n",
    "    if X_train[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] \n",
    "\n",
    "    if X_test[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yeo-Johnson transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arnold/Desktop/MlOp/env/lib/python3.8/site-packages/scipy/stats/morestats.py:1557: RuntimeWarning: divide by zero encountered in log\n",
      "  loglike = -n_samples / 2 * np.log(trans.var(axis=0))\n",
      "/home/arnold/Desktop/MlOp/env/lib/python3.8/site-packages/scipy/optimize/optimize.py:2621: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  w = xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom\n",
      "/home/arnold/Desktop/MlOp/env/lib/python3.8/site-packages/scipy/optimize/optimize.py:2214: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp1 = (x - w) * (fx - fv)\n",
      "/home/arnold/Desktop/MlOp/env/lib/python3.8/site-packages/scipy/optimize/optimize.py:2215: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LotArea': -12.55283001172003}"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yeo_transformer = YeoJohnsonTransformer(\n",
    "    variables=['LotArea']\n",
    ")\n",
    "\n",
    "X_train = yeo_transformer.fit_transform(X_train)\n",
    "X_test = yeo_transformer.transform(X_test)\n",
    "\n",
    "yeo_transformer.lambda_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[var for var in X_train.columns if X_test[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binarize skewed variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "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>BsmtFinSF2</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>MiscVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      BsmtFinSF2  LowQualFinSF  EnclosedPorch  3SsnPorch  ScreenPorch  MiscVal\n",
       "930            0             0              0          0            0        0\n",
       "656            0             0              0          0            0        0\n",
       "45             0             0              0          0            0        0\n",
       "1348           0             0              0          0            0        0\n",
       "55             0             0              0          1            0        0"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skewed = [\n",
    "    'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n",
    "    '3SsnPorch', 'ScreenPorch', 'MiscVal'\n",
    "]\n",
    "\n",
    "binarizer = SklearnTransformerWrapper(\n",
    "    transformer=Binarizer(threshold=0), variables=skewed\n",
    ")\n",
    "\n",
    "\n",
    "X_train = binarizer.fit_transform(X_train)\n",
    "X_test = binarizer.fit_transform(X_test)\n",
    "\n",
    "X_train[skewed].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical variables\n",
    "\n",
    "### Applied mapplings\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n",
    "\n",
    "qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n",
    "             'HeatingQC', 'KitchenQual', 'FireplaceQu',\n",
    "             'GarageQual', 'GarageCond',\n",
    "            ]\n",
    "            \n",
    "for var in qual_vars:\n",
    "    X_train[var] = X_train[var].map(qual_mappings)\n",
    "    X_test[var] = X_test[var].map(qual_mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n",
    "\n",
    "var = 'BsmtExposure'\n",
    "\n",
    "\n",
    "X_train[var] = X_train[var].map(exposure_mappings)\n",
    "X_test[var] = X_test[var].map(exposure_mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "finish_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n",
    "\n",
    "finish_vars = ['BsmtFinType1', 'BsmtFinType2']\n",
    "\n",
    "\n",
    "for var in finish_vars:\n",
    "    X_train[var] = X_train[var].map(finish_mappings)\n",
    "    X_test[var] = X_test[var].map(finish_mappings)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n",
    "\n",
    "var = 'GarageFinish'\n",
    "\n",
    "\n",
    "X_train[var] = X_train[var].map(garage_mappings)\n",
    "X_test[var] = X_test[var].map(garage_mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "fence_mappings = {'Missing': 0, 'NA': 0, 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}\n",
    "\n",
    "var = 'Fence'\n",
    "\n",
    "X_train[var] = X_train[var].map(fence_mappings)\n",
    "X_test[var] = X_test[var].map(fence_mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\n",
    "    var for var in X_train.columns if X_train[var].isnull().sum() > 0\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "[\n",
    "    var for var in X_test.columns if X_test[var].isnull().sum() > 0\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing rare labels\n",
    "\n",
    "For this project, rare labels will represent those columns with less than 1% representation in the house"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qual_vars = qual_vars + finish_vars + ['BsmtExposure','GarageFinish','Fence']\n",
    "\n",
    "cat_others = [\n",
    "    var for var in cat_vars if var not in qual_vars\n",
    "]\n",
    "\n",
    "len(cat_others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSZoning',\n",
       " 'Street',\n",
       " 'Alley',\n",
       " 'LotShape',\n",
       " 'LandContour',\n",
       " 'Utilities',\n",
       " 'LotConfig',\n",
       " 'LandSlope',\n",
       " 'Neighborhood',\n",
       " 'Condition1',\n",
       " 'Condition2',\n",
       " 'BldgType',\n",
       " 'HouseStyle',\n",
       " 'RoofStyle',\n",
       " 'RoofMatl',\n",
       " 'Exterior1st',\n",
       " 'Exterior2nd',\n",
       " 'MasVnrType',\n",
       " 'Foundation',\n",
       " 'Heating',\n",
       " 'CentralAir',\n",
       " 'Electrical',\n",
       " 'Functional',\n",
       " 'GarageType',\n",
       " 'PavedDrive',\n",
       " 'PoolQC',\n",
       " 'MiscFeature',\n",
       " 'SaleType',\n",
       " 'SaleCondition',\n",
       " 'MSSubClass']"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MSZoning': Index(['RL', 'RM', 'FV', 'RH'], dtype='object'),\n",
       " 'Street': Index(['Pave'], dtype='object'),\n",
       " 'Alley': Index(['Missing', 'Grvl', 'Pave'], dtype='object'),\n",
       " 'LotShape': Index(['Reg', 'IR1', 'IR2'], dtype='object'),\n",
       " 'LandContour': Index(['Lvl', 'Bnk', 'HLS', 'Low'], dtype='object'),\n",
       " 'Utilities': Index(['AllPub'], dtype='object'),\n",
       " 'LotConfig': Index(['Inside', 'Corner', 'CulDSac', 'FR2'], dtype='object'),\n",
       " 'LandSlope': Index(['Gtl', 'Mod'], dtype='object'),\n",
       " 'Neighborhood': Index(['NAmes', 'CollgCr', 'OldTown', 'Edwards', 'Somerst', 'NridgHt',\n",
       "        'Gilbert', 'Sawyer', 'NWAmes', 'BrkSide', 'SawyerW', 'Crawfor',\n",
       "        'Mitchel', 'Timber', 'NoRidge', 'IDOTRR', 'ClearCr', 'SWISU', 'StoneBr',\n",
       "        'Blmngtn', 'MeadowV', 'BrDale'],\n",
       "       dtype='object'),\n",
       " 'Condition1': Index(['Norm', 'Feedr', 'Artery', 'RRAn', 'PosN'], dtype='object'),\n",
       " 'Condition2': Index(['Norm'], dtype='object'),\n",
       " 'BldgType': Index(['1Fam', 'TwnhsE', 'Duplex', 'Twnhs', '2fmCon'], dtype='object'),\n",
       " 'HouseStyle': Index(['1Story', '2Story', '1.5Fin', 'SLvl', 'SFoyer'], dtype='object'),\n",
       " 'RoofStyle': Index(['Gable', 'Hip'], dtype='object'),\n",
       " 'RoofMatl': Index(['CompShg'], dtype='object'),\n",
       " 'Exterior1st': Index(['VinylSd', 'HdBoard', 'Wd Sdng', 'MetalSd', 'Plywood', 'CemntBd',\n",
       "        'BrkFace', 'Stucco', 'WdShing', 'AsbShng'],\n",
       "       dtype='object'),\n",
       " 'Exterior2nd': Index(['VinylSd', 'HdBoard', 'Wd Sdng', 'MetalSd', 'Plywood', 'CmentBd',\n",
       "        'Wd Shng', 'BrkFace', 'Stucco', 'AsbShng'],\n",
       "       dtype='object'),\n",
       " 'MasVnrType': Index(['None', 'BrkFace', 'Stone'], dtype='object'),\n",
       " 'Foundation': Index(['PConc', 'CBlock', 'BrkTil', 'Slab'], dtype='object'),\n",
       " 'Heating': Index(['GasA', 'GasW'], dtype='object'),\n",
       " 'CentralAir': Index(['Y', 'N'], dtype='object'),\n",
       " 'Electrical': Index(['SBrkr', 'FuseA', 'FuseF'], dtype='object'),\n",
       " 'Functional': Index(['Typ', 'Min2', 'Min1', 'Mod'], dtype='object'),\n",
       " 'GarageType': Index(['Attchd', 'Detchd', 'BuiltIn', 'Basment'], dtype='object'),\n",
       " 'PavedDrive': Index(['Y', 'N', 'P'], dtype='object'),\n",
       " 'PoolQC': Index(['Missing'], dtype='object'),\n",
       " 'MiscFeature': Index(['Missing', 'Shed'], dtype='object'),\n",
       " 'SaleType': Index(['WD', 'New', 'COD'], dtype='object'),\n",
       " 'SaleCondition': Index(['Normal', 'Partial', 'Abnorml', 'Family'], dtype='object'),\n",
       " 'MSSubClass': Int64Index([20, 60, 50, 120, 30, 160, 70, 80, 90, 190, 75, 85], dtype='int64')}"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_encoder = RareLabelEncoder(tol=0.01, n_categories=1, variables=cat_others)\n",
    "\n",
    "rare_encoder.fit(X_train)\n",
    "\n",
    "rare_encoder.encoder_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = rare_encoder.transform(X_train)\n",
    "X_test = rare_encoder.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MSZoning': {'Rare': 0, 'RM': 1, 'RH': 2, 'RL': 3, 'FV': 4},\n",
       " 'Street': {'Rare': 0, 'Pave': 1},\n",
       " 'Alley': {'Grvl': 0, 'Pave': 1, 'Missing': 2},\n",
       " 'LotShape': {'Reg': 0, 'IR1': 1, 'Rare': 2, 'IR2': 3},\n",
       " 'LandContour': {'Bnk': 0, 'Lvl': 1, 'Low': 2, 'HLS': 3},\n",
       " 'Utilities': {'Rare': 0, 'AllPub': 1},\n",
       " 'LotConfig': {'Inside': 0, 'FR2': 1, 'Corner': 2, 'Rare': 3, 'CulDSac': 4},\n",
       " 'LandSlope': {'Gtl': 0, 'Mod': 1, 'Rare': 2},\n",
       " 'Neighborhood': {'IDOTRR': 0,\n",
       "  'MeadowV': 1,\n",
       "  'BrDale': 2,\n",
       "  'Edwards': 3,\n",
       "  'BrkSide': 4,\n",
       "  'OldTown': 5,\n",
       "  'Sawyer': 6,\n",
       "  'SWISU': 7,\n",
       "  'NAmes': 8,\n",
       "  'Mitchel': 9,\n",
       "  'SawyerW': 10,\n",
       "  'Rare': 11,\n",
       "  'NWAmes': 12,\n",
       "  'Gilbert': 13,\n",
       "  'Blmngtn': 14,\n",
       "  'CollgCr': 15,\n",
       "  'Crawfor': 16,\n",
       "  'ClearCr': 17,\n",
       "  'Somerst': 18,\n",
       "  'Timber': 19,\n",
       "  'StoneBr': 20,\n",
       "  'NridgHt': 21,\n",
       "  'NoRidge': 22},\n",
       " 'Condition1': {'Artery': 0,\n",
       "  'Feedr': 1,\n",
       "  'Norm': 2,\n",
       "  'RRAn': 3,\n",
       "  'Rare': 4,\n",
       "  'PosN': 5},\n",
       " 'Condition2': {'Rare': 0, 'Norm': 1},\n",
       " 'BldgType': {'2fmCon': 0, 'Duplex': 1, 'Twnhs': 2, '1Fam': 3, 'TwnhsE': 4},\n",
       " 'HouseStyle': {'SFoyer': 0,\n",
       "  '1.5Fin': 1,\n",
       "  'Rare': 2,\n",
       "  '1Story': 3,\n",
       "  'SLvl': 4,\n",
       "  '2Story': 5},\n",
       " 'RoofStyle': {'Gable': 0, 'Rare': 1, 'Hip': 2},\n",
       " 'RoofMatl': {'CompShg': 0, 'Rare': 1},\n",
       " 'Exterior1st': {'AsbShng': 0,\n",
       "  'Wd Sdng': 1,\n",
       "  'WdShing': 2,\n",
       "  'MetalSd': 3,\n",
       "  'Stucco': 4,\n",
       "  'Rare': 5,\n",
       "  'HdBoard': 6,\n",
       "  'Plywood': 7,\n",
       "  'BrkFace': 8,\n",
       "  'CemntBd': 9,\n",
       "  'VinylSd': 10},\n",
       " 'Exterior2nd': {'AsbShng': 0,\n",
       "  'Wd Sdng': 1,\n",
       "  'MetalSd': 2,\n",
       "  'Wd Shng': 3,\n",
       "  'Stucco': 4,\n",
       "  'Rare': 5,\n",
       "  'HdBoard': 6,\n",
       "  'Plywood': 7,\n",
       "  'BrkFace': 8,\n",
       "  'CmentBd': 9,\n",
       "  'VinylSd': 10},\n",
       " 'MasVnrType': {'Rare': 0, 'None': 1, 'BrkFace': 2, 'Stone': 3},\n",
       " 'Foundation': {'Slab': 0, 'BrkTil': 1, 'CBlock': 2, 'Rare': 3, 'PConc': 4},\n",
       " 'Heating': {'Rare': 0, 'GasW': 1, 'GasA': 2},\n",
       " 'CentralAir': {'N': 0, 'Y': 1},\n",
       " 'Electrical': {'Rare': 0, 'FuseF': 1, 'FuseA': 2, 'SBrkr': 3},\n",
       " 'Functional': {'Rare': 0, 'Min2': 1, 'Mod': 2, 'Min1': 3, 'Typ': 4},\n",
       " 'GarageType': {'Rare': 0,\n",
       "  'Detchd': 1,\n",
       "  'Basment': 2,\n",
       "  'Attchd': 3,\n",
       "  'BuiltIn': 4},\n",
       " 'PavedDrive': {'N': 0, 'P': 1, 'Y': 2},\n",
       " 'PoolQC': {'Missing': 0, 'Rare': 1},\n",
       " 'MiscFeature': {'Rare': 0, 'Shed': 1, 'Missing': 2},\n",
       " 'SaleType': {'COD': 0, 'Rare': 1, 'WD': 2, 'New': 3},\n",
       " 'SaleCondition': {'Rare': 0,\n",
       "  'Abnorml': 1,\n",
       "  'Family': 2,\n",
       "  'Normal': 3,\n",
       "  'Partial': 4},\n",
       " 'MSSubClass': {30: 0,\n",
       "  'Rare': 1,\n",
       "  190: 2,\n",
       "  90: 3,\n",
       "  160: 4,\n",
       "  50: 5,\n",
       "  85: 6,\n",
       "  70: 7,\n",
       "  80: 8,\n",
       "  20: 9,\n",
       "  75: 10,\n",
       "  120: 11,\n",
       "  60: 12}}"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder = OrdinalEncoder(encoding_method='ordered', variables=cat_others)\n",
    "\n",
    "cat_encoder.fit(X_train, y_train)\n",
    "\n",
    "cat_encoder.encoder_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = cat_encoder.transform(X_train)\n",
    "X_test = cat_encoder.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[var for var in X_test.columns if X_train[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[var for var in X_test.columns if X_test[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eO98bIEma2jAPB98DXFZV40kOBzYkWTvhId/fANZ0DwQ/GfgL4KQkPwf8ATAGVDd3TVU9Mc/bIUmaxLR79FW1o6rGu+XdwFZg0YQxT1ZVdS8PoxfqAO8A1lbV4124rwXOnq/iJUnTm9Ex+iRLgNOAuwb0nZfkPuDLwAe65kXAD/qGbWPCh4Qkad8aOuiTLARuBC6tql0T+6vqpqo6CTgX+OhMC0myvDu+v37nzp0znS5JmsRQQZ9kAb2QX1VVq6caW1W3AyckOQrYDizu6z62axs0b2VVjVXV2MjIyFDFS5KmN8xZNwGuA7ZW1TWTjHllN44krwMOBR4Dvga8PckRSY4A3t61SZL2k2HOulkKLAM2JdnYtV0BjAJU1QrgXcB7kjwNPAVc0H05+3iSjwJ3d/OuqqrH57F+SdI0pg36qroDyDRjrgaunqTveuD6WVUnSZozr4yVpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4YR4OvjjJrUm2JNmc5JIBYy5Kck+STUnWJTmlr+/hrn1jkvXzvQGSpKkN83DwPcBlVTWe5HBgQ5K1VbWlb8xDwJur6okk7wRWAm/s6z+rqh6dv7IlScMa5uHgO4Ad3fLuJFuBRcCWvjHr+qbcCRw7z3VKkmZpRsfokywBTgPummLYB4Fb+l4X8PUkG5Isn3GFkqQ5GebQDQBJFgI3ApdW1a5JxpxFL+jP6Gs+o6q2J3k5sDbJfVV1+4C5y4HlAKOjozPYBEnSVIbao0+ygF7Ir6qq1ZOMORn4LHBOVT22t72qtne/HwFuAk4fNL+qVlbVWFWNjYyMzGwrJEmTGuasmwDXAVur6ppJxowCq4FlVfXtvvbDui9wSXIY8Hbg3vkoXJI0nGEO3SwFlgGbkmzs2q4ARgGqagVwJXAk8One5wJ7qmoMOBq4qWt7CfCFqvrqfG6AJGlqw5x1cweQacb8NvDbA9ofBE55/gxJ0v7ilbGS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS46YN+iSLk9yaZEuSzUkuGTDmoiT3JNmUZF2SU/r6zk5yf5IHklw+3xsgSZratA8HB/YAl1XVeJLDgQ1J1lbVlr4xDwFvrqonkrwTWAm8MckhwKeAtwHbgLuTrJkwV5K0D027R19VO6pqvFveDWwFFk0Ys66qnuhe3gkc2y2fDjxQVQ9W1U+AG4Bz5qt4SdL0htmjf06SJcBpwF1TDPsgcEu3vAj4QV/fNuCNk6x7ObC8e/lkkvtnUpsmdRTw6IEuYjq5+kBXoAPEf5/z57jJOoYO+iQLgRuBS6tq1yRjzqIX9GfMtMKqWknvkI/mUZL1VTV2oOuQBvHf5/4xVNAnWUAv5FdV1epJxpwMfBZ4Z1U91jVvBxb3DTu2a5Mk7SfDnHUT4Dpga1VdM8mYUWA1sKyqvt3XdTfwqiTHJ3kpcCGwZu5lS5KGNcwe/VJgGbApycau7QpgFKCqVgBXAkcCn+59LrCnqsaqak+Si4GvAYcA11fV5vndBE3Dw2F6IfPf536QqjrQNUiS9iGvjJWkxhn0ktQ4g16SGjejC6b0wpfkJHpXH++9enk7sKaqth64qiQdSO7RNyTJR+jdZiLAN7ufAF/0hnJ6IUvy/gNdQ8s866YhSb4NvKaqnp7Q/lJgc1W96sBUJk0tyferavRA19EqD9205Vng54HvTWg/puuTDpgk90zWBRy9P2t5sTHo23Ip8I0k3+FfbyY3CrwSuPhAFSV1jgbeATwxoT3Auv1fzouHQd+Qqvpqkl+gd3vo/i9j766qZw5cZRIAXwIWVtXGiR1Jbtvv1byIeIxekhrnWTeS1DiDXpIaZ9CrCUlekeSGJN9NsiHJV7rvK+ayzjOTfKlb/o291yIkOTfJq/vGXZXkrbN8j4uS3JNkU5J1SU6ZS83SIH4Zq4Ne98yEm4DPV9WFXdsp9M7y+PZUc4dVVWv412cpnEvvi8UtXd+Vc1j1Q8Cbq+qJJO+kd9vegY/blGbLPXq14Czg6e7ZCABU1beAO5L8SZJ7uz3mC+C5PfXbkvxlkvuSrOo+LEhydtc2Dvzm3vUleV+STyb5j8BvAH+SZGOSE5N8Lsn53bi3JPm/3ftdn+TQrv3hJH+UZLzrO6mrc11V7T3d8E56T2GT5pVBrxb8e2DDgPbfBE4FTgHeSi+cj+n6TqN33cGrgROApUleBvxv4NeB1wOvmLjCqlpHb8/+w1V1alV9d29fN/9zwAVV9Vp6fzF/qG/6o1X1OuAzwH8dUO8HgVuG2mJpBgx6tewM4ItV9UxV/RPwt8Abur5vVtW2qnoW2AgsAU4CHqqq71TvvOM/n+H7/WI3f+/hos8D/6mvf+/zljd07/ecJGfRC/qPzPA9pWkZ9GrBZnp74DPx477lZ9g/31ftfc+fer8kJwOfBc6pqsf2Qx16kTHo1YK/AQ5NsnxvQxee/wxckOSQJCP09q6/OcV67gOWJDmxe/1bk4zbDRw+oP3+bv4ru9fL6P0VMakko/T29Jf1/SUgzSuDXge97jDLecBbu9MrNwP/A/gCcA/wLXofBr9XVf84xXr+BVgOfLn7MvaRSYbeAHy4+9L1xAnz3w/8nySb6N1IbsUk69jrSuBI4NPdl7vrp99iaWa8BYIkNc49eklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1Lj/j/fcPu70I6QgQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Aq2wPSJoD9EvaOOom398ANlQ3BD8T+BKwUNILgQ8DfYCrZTfYfqLL2xEREWNou0dve7ftgerxfmAYmDdqzJO2XT2dRSPUAd4IbLT9eBXuG4El3So+IiLa62iOXtIC4BxgS4u+t0m6D7gFeHfVPA94pGnYTkZ9SERExOSqHfSSZgPrgBW2943ut32T7YXAJcA1nRYiaVk1v79tz549nS4eERFjqBX0kmbSCPm1ttePN9b2ncArJM0FdgHzm7pPqtpaLbfGdp/tvp6enlrFR0REe3WOuhFwPTBse9UYY06rxiHpNcDxwF7gNuBCSSdKOhG4sGqLiIgpUueom8XAUmC7pMGqbSXQC2B7NfB24HJJvwQOAO+ovpx9XNI1wNZquY/ZfryL9UdERBttg972XYDajLkOuG6MvhuAGyZUXUREHLGcGRsRUbgEfURE4RL0ERGFS9BHRBQuQR8RUbgEfURE4RL0ERGFS9BHRBQuQR8RUbgEfURE4RL0ERGFS9BHRBQuQR8RUbgEfURE4RL0ERGFS9BHRBQuQR8RUbgEfURE4ercHHy+pE2ShiTtkLS8xZg/knSvpO2SNks6q6nv4ap9UNK2bm9ARESMr87NwUeAq2wPSJoD9EvaaHuoacwPgN+2/YSki4A1wLlN/RfYfqx7ZUdERF11bg6+G9hdPd4vaRiYBww1jdnctMjdwEldrjMiIiaoozl6SQuAc4At4wz7E+DrTc8N3C6pX9KyjiuMiIgjUmfqBgBJs4F1wArb+8YYcwGNoD+vqfk827skvRjYKOk+23e2WHYZsAygt7e3g02IiIjx1NqjlzSTRsivtb1+jDFnAp8DLra991C77V3V70eBm4BFrZa3vcZ2n+2+np6ezrYiIiLGVOeoGwHXA8O2V40xphdYDyy1/b2m9lnVF7hImgVcCHy3G4VHREQ9daZuFgNLge2SBqu2lUAvgO3VwNXAi4BPNz4XGLHdB7wEuKlqOw74ou1bu7kBERExvjpH3dwFqM2YK4ErW7Q/BJz1zCUiImKq5MzYiIjCJegjIgqXoI+IKFyCPiKicAn6iIjCJegjIgqXoI+IKFyCPiKicAn6iIjCJegjIgqXoI+IKFyCPiKicAn6iIjCJegjIgqXoI+IKFyCPiKicAn6iIjCJegjIgqXoI+IKFzboJc0X9ImSUOSdkha3mLMH0m6V9J2SZslndXUt0TS/ZIekPShbm9ARESMr+3NwYER4CrbA5LmAP2SNtoeahrzA+C3bT8h6SJgDXCupBnAp4A3ADuBrZI2jFo2IiImUds9etu7bQ9Uj/cDw8C8UWM2236ieno3cFL1eBHwgO2HbD8F3Ahc3K3iIyKiPdmuP1haANwJ/IbtfWOMeT+w0PaVki4Flti+supbCpxr+70tllsGLKuengHc38mGTIK5wGPTXMPRIu/FYXkvDst7cdjR8F6cbLunVUedqRsAJM0G1gErxgn5C4A/Ac7rtELba2hM+RwVJG2z3TfddRwN8l4clvfisLwXhx3t70WtoJc0k0bIr7W9fowxZwKfAy6yvbdq3gXMbxp2UtUWERFTpM5RNwKuB4ZtrxpjTC+wHlhq+3tNXVuB0yWdIuk5wGXAhiMvOyIi6qqzR78YWApslzRYta0EegFsrwauBl4EfLrxucCI7T7bI5LeC9wGzABusL2ju5swaY6aaaSjQN6Lw/JeHJb34rCj+r3o6MvYiIh49smZsRERhUvQR0QULkEfEVG42sfRl07SQhpn7R4663cXsMH28PRVFdOt+ncxD9hi+8mm9iW2b52+yqaepEWAbW+V9C+BJcB9tr82zaVNK0n/w/bl013HePJlLCDpg8A7aVyiYWfVfBKNw0FvtH3tdNV2NJH0x7b/ZrrrmCqS/gPw72lc9uNsYLntv6/6Bmy/ZhrLm1KSPgxcRGPncCNwLrCJxnWsbrP98Wksb8pIGn14uIALgG8C2H7rlBdVQ4IekPQ94FW2fzmq/TnADtunT09lRxdJP7LdO911TBVJ24HX2X6yuvzHV4C/tf1JSd+xfc70Vjh1qvfibOB44B+Bk2zvk/RcGn/tnDmd9U0VSQPAEI2TQ00j6P8njZ1CbH9r+qobW6ZuGg4CLwd+OKr9ZVXfMUPSvWN1AS+ZylqOAr92aLrG9sOSzge+IulkGu/HsWTE9tPAzyU9eOgyKLYPSDqW/o/0AcuBPwM+YHtQ0oGjNeAPSdA3rAC+Ien7wCNVWy9wGvCMC7AV7iXAG4EnRrUL2Dz15UyrH0s62/YgQLVn/2bgBuDV01rZ1HtK0vNs/xx47aFGSc/nGNoZsn0Q+O+Svlz9/jHPghw96gucCrZvlfTrNC6r3Pxl7NZqL+ZY8lVg9qFwaybpjimvZnpdTuN+DL9iewS4XNJnp6ekafNbtn8Bvwq7Q2YCV0xPSdPH9k7g30r6faDlRR6PJpmjj4goXI6jj4goXII+IqJwCfoohqSnJQ02/Szo4rovqU4SOvT8Y5J+r1vrr9Z5vqSvdnOdEZAvY6MsB2yfPUnrvoTGF9VDALavnqTXiei67NFH0SQ9LGlu9bjv0JFDkj4i6QZJd0h6qDoL9tAyl0u6V9I9kv5W0r8G3gr81+ovhVMlfb66JzKSflfSdyRtr9Z5fNNrf1TSQNW3sGpfJOkfqmU2Szpjit+WOMYk6KMkz22atrmpxviFNM4ZWAR8WNJMSa8C/hz4Hdtn0bjswWYad0b7gO2zbT94aAWSTgA+D7zD9qtp/JX8nqbXeKy6VMJngPdXbfcBr6/OrL0a+Isj2OaItjJ1EyXpdOrmlurY8F9IepTGyWK/A3zZ9mMAth9vs44zgB803ULzCzSuj/OJ6vmheyz3A/+mevx84AuSTqdxGv3MDmqO6Fj26KN0Ixz+d37CqL5fND1+msnZ8Tn0Gs3rvwbYZPs3gLe0qCuiqxL0UbqHOXzK/ttrjP8mjTMeXwQg6YVV+35gTovx9wMLJJ1WPV8KtLvuyfNpnHkN8K4aNUUckQR9lO6jwCclbaOxVz2u6ub1Hwe+JekeYFXVdSPwgeoL1FObxv8T8MfAl6srPB4EVrd5mf8C/KWk75Dp05gCuQRCREThskcfEVG4BH1EROES9BERhUvQR0QULkEfEVG4BH1EROES9BERhUvQR0QU7v8DtFZchG79GbwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let explore the monotonic relationship between the target variable \n",
    "# and label\n",
    "\n",
    "def analyse_vars(train, y_train, var):\n",
    "\n",
    "    tmp = pd.concat([X_train, np.log(y_train)], axis = 1)\n",
    "\n",
    "    tmp.groupby(var)['SalePrice'].median().plot.bar()\n",
    "\n",
    "    plt.title(var)\n",
    "    plt.ylim(2.2, 2.6)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "for var in cat_others:\n",
    "    analyse_vars(X_train, y_train, var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create scaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# fit the scaler to the train set\n",
    "scaler.fit(X_train)\n",
    "\n",
    "\n",
    "# sklearn returns numpy array, so we wrap the array\n",
    "# with a pandas dataframe\n",
    "X_train = pd.DataFrame(\n",
    "    scaler.transform(X_train),\n",
    "    columns=X_train.columns\n",
    ")\n",
    "\n",
    "\n",
    "X_test = pd.DataFrame(\n",
    "    scaler.transform(X_test),\n",
    "    columns=X_train.columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>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>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>LotFrontage_na</th>\n",
       "      <th>MasVnrArea_na</th>\n",
       "      <th>GarageYrBlt_na</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.461171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.014706</td>\n",
       "      <td>0.049180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.673479</td>\n",
       "      <td>0.239935</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.559760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.523250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.018692</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.430183</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116686</td>\n",
       "      <td>0.032907</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.456066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.360294</td>\n",
       "      <td>0.049180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.03375</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.142807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.114724</td>\n",
       "      <td>0.172340</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.434539</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.406196</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.457944</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.220028</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.394699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.954545</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.036765</td>\n",
       "      <td>0.098361</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.25750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.080794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.601951</td>\n",
       "      <td>0.286743</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.627205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.586296</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.046729</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.406206</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.228705</td>\n",
       "      <td>0.149909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  LotShape  \\\n",
       "0    0.750000      0.75     0.461171      0.0     1.0    1.0  0.333333   \n",
       "1    0.750000      0.75     0.456066      0.0     1.0    1.0  0.333333   \n",
       "2    0.916667      0.75     0.394699      0.0     1.0    1.0  0.000000   \n",
       "\n",
       "   LandContour  Utilities  LotConfig  LandSlope  Neighborhood  Condition1  \\\n",
       "0     1.000000        1.0        0.0        0.0      0.863636         0.4   \n",
       "1     0.333333        1.0        0.0        0.0      0.363636         0.4   \n",
       "2     0.333333        1.0        0.0        0.0      0.954545         0.4   \n",
       "\n",
       "   Condition2  BldgType  HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0         1.0      0.75         0.6     0.777778         0.50   0.014706   \n",
       "1         1.0      0.75         0.6     0.444444         0.75   0.360294   \n",
       "2         1.0      1.00         0.6     0.888889         0.50   0.036765   \n",
       "\n",
       "   YearRemodAdd  RoofStyle  RoofMatl  Exterior1st  Exterior2nd  MasVnrType  \\\n",
       "0      0.049180        0.0       0.0          1.0          1.0    0.333333   \n",
       "1      0.049180        0.0       0.0          0.6          0.6    0.666667   \n",
       "2      0.098361        1.0       0.0          0.3          0.2    0.666667   \n",
       "\n",
       "   MasVnrArea  ExterQual  ExterCond  Foundation  BsmtQual  BsmtCond  \\\n",
       "0     0.00000   0.666667        0.5         1.0  0.666667  0.666667   \n",
       "1     0.03375   0.666667        0.5         0.5  0.333333  0.666667   \n",
       "2     0.25750   1.000000        0.5         1.0  1.000000  0.666667   \n",
       "\n",
       "   BsmtExposure  BsmtFinType1  BsmtFinSF1  BsmtFinType2  BsmtFinSF2  \\\n",
       "0      0.666667           1.0    0.002835           0.0         0.0   \n",
       "1      0.000000           0.8    0.142807           0.0         0.0   \n",
       "2      0.000000           1.0    0.080794           0.0         0.0   \n",
       "\n",
       "   BsmtUnfSF  TotalBsmtSF  Heating  HeatingQC  CentralAir  Electrical  \\\n",
       "0   0.673479     0.239935      1.0        1.0         1.0         1.0   \n",
       "1   0.114724     0.172340      1.0        1.0         1.0         1.0   \n",
       "2   0.601951     0.286743      1.0        1.0         1.0         1.0   \n",
       "\n",
       "   1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  \\\n",
       "0  0.559760       0.0           0.0   0.523250      0.000000           0.0   \n",
       "1  0.434539       0.0           0.0   0.406196      0.333333           0.0   \n",
       "2  0.627205       0.0           0.0   0.586296      0.333333           0.0   \n",
       "\n",
       "   FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  KitchenQual  TotRmsAbvGrd  \\\n",
       "0  0.666667       0.0         0.375      0.333333     0.666667      0.416667   \n",
       "1  0.333333       0.5         0.375      0.333333     0.666667      0.250000   \n",
       "2  0.666667       0.0         0.250      0.333333     1.000000      0.333333   \n",
       "\n",
       "   Functional  Fireplaces  FireplaceQu  GarageType  GarageYrBlt  GarageFinish  \\\n",
       "0         1.0    0.000000          0.0        0.75     0.018692           1.0   \n",
       "1         1.0    0.000000          0.0        0.75     0.457944           0.5   \n",
       "2         1.0    0.333333          0.8        0.75     0.046729           0.5   \n",
       "\n",
       "   GarageCars  GarageArea  GarageQual  GarageCond  PavedDrive  WoodDeckSF  \\\n",
       "0        0.75    0.430183         0.5         0.5         1.0    0.116686   \n",
       "1        0.25    0.220028         0.5         0.5         1.0    0.000000   \n",
       "2        0.50    0.406206         0.5         0.5         1.0    0.228705   \n",
       "\n",
       "   OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  PoolQC  \\\n",
       "0     0.032907            0.0        0.0          0.0       0.0     0.0   \n",
       "1     0.000000            0.0        0.0          0.0       0.0     0.0   \n",
       "2     0.149909            0.0        0.0          0.0       0.0     0.0   \n",
       "\n",
       "   Fence  MiscFeature  MiscVal    MoSold  SaleType  SaleCondition  \\\n",
       "0   0.00          1.0      0.0  0.545455  0.666667           0.75   \n",
       "1   0.75          1.0      0.0  0.636364  0.666667           0.75   \n",
       "2   0.00          1.0      0.0  0.090909  0.666667           0.75   \n",
       "\n",
       "   LotFrontage_na  MasVnrArea_na  GarageYrBlt_na  \n",
       "0             0.0            0.0             0.0  \n",
       "1             0.0            0.0             0.0  \n",
       "2             0.0            0.0             0.0  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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