06-feature-engineering-with-open-source.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering with Open-Source\n",
    "\n",
    "In this notebook, we will reproduce the Feature Engineering Pipeline from the notebook 2 (02-Machine-Learning-Pipeline-Feature-Engineering), but we will replace, whenever possible, the manually created functions by open-source classes, and hopefully understand the value they bring forward."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproducibility: Setting the seed\n",
    "\n",
    "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data manipulation and plotting\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for saving the pipeline\n",
    "import joblib\n",
    "\n",
    "# from Scikit-learn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, Binarizer\n",
    "\n",
    "# from 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",
    "from feature_engine.selection import DropFeatures\n",
    "from feature_engine.wrappers import SklearnTransformerWrapper\n",
    "\n",
    "# to visualise al the columns in the dataframe\n",
    "pd.pandas.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dataset\n",
    "data = pd.read_csv('train.csv')\n",
    "\n",
    "# rows and columns of the data\n",
    "print(data.shape)\n",
    "\n",
    "# visualise the dataset\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Separate dataset into train and test\n",
    "\n",
    "It is important to separate our data intro training and testing set. \n",
    "\n",
    "When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.\n",
    "\n",
    "Our feature engineering techniques will learn:\n",
    "\n",
    "- mean\n",
    "- mode\n",
    "- exponents for the yeo-johnson\n",
    "- category frequency\n",
    "- and category to number mappings\n",
    "\n",
    "from the train set.\n",
    "\n",
    "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1314, 79), (146, 79))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's separate into train and test set\n",
    "# Remember to set the seed (random_state for this sklearn function)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(['Id', 'SalePrice'], axis=1), # predictive variables\n",
    "    data['SalePrice'], # target\n",
    "    test_size=0.1, # portion of dataset to allocate to test set\n",
    "    random_state=0, # we are setting the seed here\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "\n",
    "In the following cells, we will engineer the variables of the House Price Dataset so that we tackle:\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": "markdown",
   "metadata": {},
   "source": [
    "## Target\n",
    "\n",
    "We apply the logarithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.log(y_train)\n",
    "y_test = np.log(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing values\n",
    "\n",
    "### Categorical variables\n",
    "\n",
    "We will replace missing values with the string \"missing\" in those variables with a lot of missing data. \n",
    "\n",
    "Alternatively, we will replace missing data with the most frequent category in those variables that contain fewer observations without values. \n",
    "\n",
    "This is common practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's identify the categorical variables\n",
    "# we will capture those of type object\n",
    "\n",
    "cat_vars = [var for var in data.columns if data[var].dtype == 'O']\n",
    "\n",
    "# MSSubClass is also categorical by definition, despite its numeric values\n",
    "# (you can find the definitions of the variables in the data_description.txt\n",
    "# file available on Kaggle, in the same website where you downloaded the data)\n",
    "\n",
    "# lets add MSSubClass to the list of categorical variables\n",
    "cat_vars = cat_vars + ['MSSubClass']\n",
    "\n",
    "# cast all variables as categorical\n",
    "X_train[cat_vars] = X_train[cat_vars].astype('O')\n",
    "X_test[cat_vars] = X_test[cat_vars].astype('O')\n",
    "\n",
    "# number of categorical variables\n",
    "len(cat_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a list of the categorical variables that contain missing values\n",
    "\n",
    "cat_vars_with_na = [\n",
    "    var for var in cat_vars\n",
    "    if X_train[var].isnull().sum() > 0\n",
    "]\n",
    "\n",
    "# print percentage of missing values per variable\n",
    "X_train[cat_vars_with_na ].isnull().mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables to impute with the string missing\n",
    "with_string_missing = [\n",
    "    var for var in cat_vars_with_na if X_train[var].isnull().mean() > 0.1]\n",
    "\n",
    "# variables to impute with the most frequent category\n",
    "with_frequent_category = [\n",
    "    var for var in cat_vars_with_na if X_train[var].isnull().mean() < 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I print the values here, because it makes it easier for\n",
    "# later when we need to add this values to a config file for \n",
    "# deployment\n",
    "\n",
    "with_string_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MasVnrType',\n",
       " 'BsmtQual',\n",
       " 'BsmtCond',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinType2',\n",
       " 'Electrical',\n",
       " 'GarageType',\n",
       " 'GarageFinish',\n",
       " 'GarageQual',\n",
       " 'GarageCond']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_frequent_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alley': 'Missing',\n",
       " 'FireplaceQu': 'Missing',\n",
       " 'PoolQC': 'Missing',\n",
       " 'Fence': 'Missing',\n",
       " 'MiscFeature': 'Missing'}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace missing values with new label: \"Missing\"\n",
    "\n",
    "# set up the class\n",
    "cat_imputer_missing = CategoricalImputer(\n",
    "    imputation_method='missing', variables=with_string_missing)\n",
    "\n",
    "# fit the class to the train set\n",
    "cat_imputer_missing.fit(X_train)\n",
    "\n",
    "# the class learns and stores the parameters\n",
    "cat_imputer_missing.imputer_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace NA by missing\n",
    "\n",
    "# IMPORTANT: note that we could store this class with joblib\n",
    "X_train = cat_imputer_missing.transform(X_train)\n",
    "X_test = cat_imputer_missing.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace missing values with most frequent category\n",
    "\n",
    "# set up the class\n",
    "cat_imputer_frequent = CategoricalImputer(\n",
    "    imputation_method='frequent', variables=with_frequent_category)\n",
    "\n",
    "# fit the class to the train set\n",
    "cat_imputer_frequent.fit(X_train)\n",
    "\n",
    "# the class learns and stores the parameters\n",
    "cat_imputer_frequent.imputer_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace NA by missing\n",
    "\n",
    "# IMPORTANT: note that we could store this class with joblib\n",
    "X_train = cat_imputer_frequent.transform(X_train)\n",
    "X_test = cat_imputer_frequent.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check that we have no missing information in the engineered variables\n",
    "\n",
    "X_train[cat_vars_with_na].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check that test set does not contain null values in the engineered variables\n",
    "\n",
    "[var for var in cat_vars_with_na if X_test[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical variables\n",
    "\n",
    "To engineer missing values in numerical variables, we will:\n",
    "\n",
    "- add a binary missing indicator variable\n",
    "- and then replace the missing values in the original variable with the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now let's identify the numerical variables\n",
    "\n",
    "num_vars = [\n",
    "    var for var in X_train.columns if var not in cat_vars and var != 'SalePrice'\n",
    "]\n",
    "\n",
    "# number of numerical variables\n",
    "len(num_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage    0.177321\n",
       "MasVnrArea     0.004566\n",
       "GarageYrBlt    0.056317\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a list with the numerical variables that contain missing values\n",
    "vars_with_na = [\n",
    "    var for var in num_vars\n",
    "    if X_train[var].isnull().sum() > 0\n",
    "]\n",
    "\n",
    "# print percentage of missing values per variable\n",
    "X_train[vars_with_na].isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print, makes my life easier when I want to create the config\n",
    "vars_with_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .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",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</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\n",
       "55                 0              0               0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add missing indicator\n",
    "\n",
    "missing_ind = AddMissingIndicator(variables=vars_with_na)\n",
    "\n",
    "missing_ind.fit(X_train)\n",
    "\n",
    "X_train = missing_ind.transform(X_train)\n",
    "X_test = missing_ind.transform(X_test)\n",
    "\n",
    "# check the binary missing indicator variables\n",
    "X_train[['LotFrontage_na', 'MasVnrArea_na', 'GarageYrBlt_na']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LotFrontage': 69.87974098057354,\n",
       " 'MasVnrArea': 103.7974006116208,\n",
       " 'GarageYrBlt': 1978.2959677419356}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# then replace missing data with the mean\n",
    "\n",
    "# set the imputer\n",
    "mean_imputer = MeanMedianImputer(\n",
    "    imputation_method='mean', variables=vars_with_na)\n",
    "\n",
    "# learn and store parameters from train set\n",
    "mean_imputer.fit(X_train)\n",
    "\n",
    "# the stored parameters\n",
    "mean_imputer.imputer_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage    0\n",
       "MasVnrArea     0\n",
       "GarageYrBlt    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = mean_imputer.transform(X_train)\n",
    "X_test = mean_imputer.transform(X_test)\n",
    "\n",
    "# IMPORTANT: note that we could save the imputers with joblib\n",
    "\n",
    "# check that we have no more missing values in the engineered variables\n",
    "X_train[vars_with_na].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check that test set does not contain null values in the engineered variables\n",
    "\n",
    "[var for var in vars_with_na if X_test[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal variables\n",
    "\n",
    "### Capture elapsed time\n",
    "\n",
    "There is in Feature-engine 2 classes that allow us to perform the 2 transformations below:\n",
    "\n",
    "- [CombineWithFeatureReference](https://feature-engine.readthedocs.io/en/latest/creation/CombineWithReferenceFeature.html) to capture elapsed time\n",
    "- [DropFeatures](https://feature-engine.readthedocs.io/en/latest/selection/DropFeatures.html) to drop the unwanted features\n",
    "\n",
    "We will do the first one manually, so we take the opportunity to create 1 class ourselves. For the second operation, we will use the DropFeatures class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def elapsed_years(df, var):\n",
    "    # capture difference between the year variable\n",
    "    # and the year in which the house was sold\n",
    "    df[var] = df['YrSold'] - df[var]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:\n",
    "    X_train = elapsed_years(X_train, var)\n",
    "    X_test = elapsed_years(X_test, var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we drop YrSold\n",
    "drop_features = DropFeatures(features_to_drop=['YrSold'])\n",
    "\n",
    "X_train = drop_features.fit_transform(X_train)\n",
    "X_test = drop_features.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical variable transformation\n",
    "\n",
    "### Logarithmic transformation\n",
    "\n",
    "In the previous notebook, we observed that the numerical variables are not normally distributed.\n",
    "\n",
    "We will transform with the logarightm the positive numerical variables in order to get a more Gaussian-like distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_transformer = LogTransformer(\n",
    "    variables=[\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"])\n",
    "\n",
    "X_train = log_transformer.fit_transform(X_train)\n",
    "X_test = log_transformer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check that test set does not contain null values in the engineered variables\n",
    "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] if X_test[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same for train set\n",
    "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] if X_train[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yeo-Johnson transformation\n",
    "\n",
    "We will apply the Yeo-Johnson transformation to LotArea."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\anaconda3\\lib\\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",
      "C:\\Users\\admin\\anaconda3\\lib\\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",
      "C:\\Users\\admin\\anaconda3\\lib\\site-packages\\scipy\\optimize\\optimize.py:2214: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp1 = (x - w) * (fx - fv)\n",
      "C:\\Users\\admin\\anaconda3\\lib\\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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yeo_transformer = YeoJohnsonTransformer(\n",
    "    variables=['LotArea'])\n",
    "\n",
    "X_train = yeo_transformer.fit_transform(X_train)\n",
    "X_test = yeo_transformer.transform(X_test)\n",
    "\n",
    "# the learned parameter\n",
    "yeo_transformer.lambda_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check absence of na in the train set\n",
    "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check absence of na in the test set\n",
    "[var for var in X_train.columns if X_test[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binarize skewed variables\n",
    "\n",
    "There were a few variables very skewed, we would transform those into binary variables.\n",
    "\n",
    "We can perform the below transformation with open source. We can use the [Binarizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.html) from Scikit-learn, in combination with the [SklearnWrapper](https://feature-engine.readthedocs.io/en/latest/wrappers/Wrapper.html) from Feature-engine to be able to apply the transformation only to a subset of features.\n",
    "\n",
    "Instead, we are going to do it manually, to give us another opportunity to code the class as an in-house package later in the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": 71,
     "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.transform(X_test)\n",
    "\n",
    "X_train[skewed].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical variables\n",
    "\n",
    "### Apply mappings\n",
    "\n",
    "These are variables which values have an assigned order, related to quality. For more information, check Kaggle website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-map strings to numbers, which determine quality\n",
    "\n",
    "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": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n",
    "\n",
    "var = 'BsmtExposure'\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": 74,
   "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",
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n",
    "\n",
    "var = 'GarageFinish'\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": 76,
   "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check absence of na in the train set\n",
    "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Rare Labels\n",
    "\n",
    "For the remaining categorical variables, we will group those categories that are present in less than 1% of the observations. That is, all values of categorical variables that are shared by less than 1% of houses, well be replaced by the string \"Rare\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# capture all quality variables\n",
    "\n",
    "qual_vars  = qual_vars + finish_vars + ['BsmtExposure','GarageFinish','Fence']\n",
    "\n",
    "# capture the remaining categorical variables\n",
    "# (those that we did not re-map)\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": 79,
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_encoder = RareLabelEncoder(tol=0.01, n_categories=1, variables=cat_others)\n",
    "\n",
    "# find common labels\n",
    "rare_encoder.fit(X_train)\n",
    "\n",
    "# the common labels are stored, we can save the class\n",
    "# and then use it later :)\n",
    "rare_encoder.encoder_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = rare_encoder.transform(X_train)\n",
    "X_test = rare_encoder.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding of categorical variables\n",
    "\n",
    "Next, we need to transform the strings of the categorical variables into numbers. \n",
    "\n",
    "We will do it so that we capture the monotonic relationship between the label and the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up the encoder\n",
    "cat_encoder = OrdinalEncoder(encoding_method='ordered', variables=cat_others)\n",
    "\n",
    "# create the mappings\n",
    "cat_encoder.fit(X_train, y_train)\n",
    "\n",
    "# mappings are stored and class can be saved\n",
    "cat_encoder.encoder_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = cat_encoder.transform(X_train)\n",
    "X_test = cat_encoder.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check absence of na in the train set\n",
    "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check absence of na in the test set\n",
    "[var for var in X_test.columns if X_test[var].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cp68c/SNgBfAGSf9EdT/E+XtfJCIiDmSdPoupR9JpvPjK0U155WhExOg22CtH21812u/4vHI0ImJ0G2wPovSq0X555WhExCjW5I1yERFxAOv0JDWSzgXeBLyqv832Z5soKiIiRl5Hl7lKWghcQPXuBgHvAY5psK6IiBhhnd4H8e9sXwQ8Zvu/Aafy0qetRkTEKNNpQDxVf39S0tFUrxN9Oa8hjYiIA0Sn5yBulHQY8OfA2rrt641UFBER+4XB7oM4GXjI9ufq+YnAeuBe4IvNlxcRESNlsENMXwOeAZD0W8Cf1W2PU7/FLSIiRqfBDjGNs/2v9fQFwCLbS4GlLS8BioiIUWiwPYhx9es/Ad4OfL+lr+N7KCIi4sAz2C/5fwB+KOkRqiuZfgQg6TjyuO+IiFFtsEdtfF7SbcBRwK22XXcdRHXT3IAkTQe+Abye6kVDi2z/ZduY84DP1f19wPz6VaVIegDYRfVioj7b3fv20SIi4pUY9DCR7bsLbfd1sO4+4LL6UeGTgLWSVtre2DLmNmCFbUs6EbgeOKGl/wzbj3SwrYiIGGKd3ii3z2xvs91TT+8CeoGpbWN2t+yVTKB+Y11ERIy8xgKilaQZwCxgVaHv3ZLuBb4DfLCly8CtktZKmjccdUZExIsaD4j65rqlVOcXdrb3215u+wTgXVTnI/rNsf1m4BzgY/V9GKX1z5O0RtKaHTt2DP0HiIgYoxoNCEnjqcJhyWBvn7N9B9U7ryfX81vr79uB5cDsAZZbZLvbdveUKVOGtP6IiLGssYCQJGAx0Gt7wQBjjqvHIenNwCHAo5Im1Ce2kTQBOAv4SVO1RkTEnpq82W0OMBdY33LX9RVAF4DthcDvAxdJepbqPosL6iuajgSW19lxMHCt7ZsbrDUiIto0FhD1/QwaZMyVwJWF9s3ASQ2VFhERHRiWq5giIuLAk4CIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKLGAkLSdEm3S+qVtEHSpYUx50m6R9I6SWskva2l72xJmyTdL+nypuqMiIiyxt5JDfQBl9nukTQJWCtppe2NLWNuA1bYtqQTgeuBEySNA74CnAlsAVZLWtG2bERENKixPQjb22z31NO7gF5gatuY3bZdz04A+qdnA/fb3mz7GeA64Lymao2IiD0NyzkISTOAWcCqQt+7Jd0LfAf4YN08FXioZdgW2sIlIiKa1XhASJoILAXm297Z3m97ue0TgHcBn+tfrLAqF9qQNK8+f7Fmx44dQ1R1REQ0GhCSxlOFwxLby/Y21vYdwBskTabaY5je0j0N2DrAcotsd9vunjJlyhBVHhERTV7FJGAx0Gt7wQBjjqvHIenNwCHAo8BqYKakYyUdAlwIrGiq1oiI2FOTVzHNAeYC6yWtq9uuALoAbC8Efh+4SNKzwFPABfVJ6z5JFwO3AOOAq21vaLDWiIho01hA2L6T8rmE1jFXAlcO0HcTcFMDpUVERAdyJ3VERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiqLGAkDRd0u2SeiVtkHRpYcz7JN1Tf90l6aSWvgckrZe0TtKapuqMiIiyxt5JDfQBl9nukTQJWCtppe2NLWN+Bpxm+zFJ5wCLgFNa+s+w/UiDNUZExAAaCwjb24Bt9fQuSb3AVGBjy5i7Wha5G5jWVD0REbFvhuUchKQZwCxg1V6GfQj4bsu8gVslrZU0r8HyIiKioMlDTABImggsBebb3jnAmDOoAuJtLc1zbG+VdASwUtK9tu8oLDsPmAfQ1dU15PVHRIxVje5BSBpPFQ5LbC8bYMyJwNeB82w/2t9ue2v9fTuwHJhdWt72ItvdtrunTJky1B8hImLMavIqJgGLgV7bCwYY0wUsA+bavq+lfUJ9YhtJE4CzgJ80VWtEROypyUNMc4C5wHpJ6+q2K4AuANsLgU8BhwNfrfKEPtvdwJHA8rrtYOBa2zc3WGtERLRp8iqmOwENMubDwIcL7ZuBk/ZcIiIihkvupI6IiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqKosYCQNF3S7ZJ6JW2QdGlhzPsk3VN/3SXppJa+syVtknS/pMubqjMiIsoObnDdfcBltnskTQLWSlppe2PLmJ8Bp9l+TNI5wCLgFEnjgK8AZwJbgNWSVrQtGxERDWpsD8L2Nts99fQuoBeY2jbmLtuP1bN3A9Pq6dnA/bY3234GuA44r6laIyJiT03uQbxA0gxgFrBqL8M+BHy3np4KPNTStwU4ZYB1zwPm1bO7JW16RcXuvyYDjwzXxnTlcG1pzMjP78A2bD+/EfjZHTNQR+MBIWkisBSYb3vnAGPOoAqIt/U3FYa5tKztRVSHpkY1SWtsd490HfHy5Od3YBurP79GA0LSeKpwWGJ72QBjTgS+Dpxj+9G6eQswvWXYNGBrk7VGRMRLNXkVk4DFQK/tBQOM6QKWAXNt39fStRqYKelYSYcAFwIrmqo1IiL21OQexBxgLrBe0rq67QqgC8D2QuBTwOHAV6s8oc92t+0+SRcDtwDjgKttb2iw1gPBqD+MNsrl53dgG5M/P9nFQ/sRETHG5U7qiIgoSkBERERRAiIiIoqG5Ua52HeSTqC6e3wq1T0gW4EVtntHtLCIUa7+vzcVWGV7d0v72bZvHrnKhl/2IPZDkj5J9XgRAf+b6rJfAf+QBxce2CR9YKRriIFJ+jjwj8AlwE8ktT7i5wsjU9XIyVVM+yFJ9wFvsv1sW/shwAbbM0emsnilJP3cdtdI1xFlktYDp9reXT8i6Abg72z/paR/sT1rZCscXjnEtH96HjgaeLCt/ai6L/Zjku4ZqAs4cjhriX02rv+wku0HJJ0O3CDpGMqPABrVEhD7p/nAbZJ+yosPLewCjgMuHqmiomNHAr8DPNbWLuCu4S8n9sEvJP2G7XUA9Z7EO4CrgX87opWNgATEfsj2zZKOp3rs+VSqXyxbgNW2nxvR4qITNwIT+3/JtJL0g2GvJvbFRVTvsnmB7T7gIklfG5mSRk7OQURERFGuYoqIiKIEREREFCUgYsyTtHvwUfu8zs9I+kQ9/VZJqyStk9Qr6TN1+/slXTXU244YKjlJHdG8vwX+g+0fSxoHvHGkC4roRPYgIgokvbP+q/9fJH1P0pF1+2ckXS3pB5I213fe9i/zx5I2SfoeLw2BI4BtALafs72xsL1jJN0m6Z76e1fdfo2khZJ+JOm++pJLJI2T9D8kra6X+UiD/xwxRiUgIsruBN5a3zl7HfBfWvpOoLrPYTbwaUnjJb2F6s2Hs4DfA05uGf9FYJOk5ZI+IulVhe1dBXzD9onAEuB/tvTNAE4DzgUW1st/CHjc9sn1tv5Q0rGv9ENHtMohpoiyacA3JR0FHAL8rKXvO7afBp6WtJ3qxrjfBJbbfhJA0guvyLX9WUlLgLOA9wJ/AJzetr1TqYIF4O+AP2/pu97288BPJW2mCqizgBMlnV+PeS0ws63OiFckARFR9mVgge0V9eMWPtPS93TL9HO8+P9owJuKbP8f4K8k/TWwQ9Lhg2zfA0z3zwu4xPYtg6wn4mXLIaaIstcCD9fT/7GD8XcA75Z0qKRJwDv7OySdq/ql61R/5T8H/LJt+buoDlEBvI/qEFe/90g6SNIbgF8FNlG9r/2jksbX2zhe0oROP1xEJ7IHEQGvlrSlZX4B1R7DtyQ9DNwN7PX4vu0eSd8E1lE9ZPFHLd1zgS9KepLqMQ7vs/3ci5kBwMeBqyX9Z2AH0PpY8E3AD6kOZf0n2/9P0tepzk301OGzA3jXPnzmiEHlURsR+zFJ1wA32r5hpGuJsSeHmCIioih7EBERUZQ9iIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFP1/MKMEXFhpaWcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rh/W9ANUpIKD9yqOIiBhG+jPl6B7Ul5pK+iPgqcZ6FRERQ66vU47+V2AB8HpJ/0h1P8SpW98kIiK2Z319FtMySUfx4pSjD2TK0YiI4a23KUfbpxrt9sZMORoRMbz1dgRRmmq0W6YcjYgYxpq8US4iIrZjfR2kRtJ7gbcCr+4us/2lJjoVERFDr0+XuUqaBZxGNXeDgD8D9m+wXxERMcT6eh/EH9v+CPCk7f8BvJ2XPm01IiKGmb4GxLP1z2ck7Us1nejLmYY0IiK2E30dg/iJpN2BvwGW1mWXNNKjiIh4RejtPogjgEdtf7l+PxZYAdwP/F3z3YuIiKHS2ymmbwPPAUh6J3BRXfYUMLvZrkVExFDq7RTTKNu/q5dPA2bbngfMa5kEKCIihqHejiBG1dN/AhwL3Niyrs/3UERExPanty/5HwC3SHqc6kqm2wAkvYE87jsiYljr7VEbX5F0A7APcL1t16t2oLpprkeSxgN/D7yOaqKh2ba/3lbnJODL9fpNwLR6qlIkPQxsoJqYaJPtzv59tIiI2Ba9niayfWeh7ME+7HsTcF79qPBdgaWSFtm+r6XODcAC25Z0MHAFcFDL+mNsP96HtiIiYoD19Ua5frO91vayenkD0AWMa6uzseWoZAz1jHURETH0GguIVpImAocBdxXWnSLpfuCnwMdbVhm4XtJSSVMHo58REfGixgOivrluHtX4wvr29bbn2z4IOJlqPKLbZNuHAycCZ9f3YZT2P1XSEklL1q1bN/AfICJihGo0ICSNpgqHub3NPmf7Vqo5r/es36+pfz4GzAcm9bDdbNudtjs7OjoGtP8RESNZYwEhScAcoMv2zB7qvKGuh6TDgZ2AJySNqQe2kTQGOB64t6m+RkTElpq82W0yMAVY0XLX9XRgAoDtWcAHgY9Iep7qPovT6iua9gbm19mxI3CZ7Wsb7GtERLRpLCDq+xnUS50ZwIxC+SrgkIa6FhERfTAoVzFFRMT2JwERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUWNBYSk8ZJuktQlaaWkcwt1TpJ0j6TlkpZIekfLuhMkPSDpIUkXNNXPiIgoa2xOamATcJ7tZZJ2BZZKWmT7vpY6NwALbFvSwcAVwEGSRgEXA8cBq4HFkha0bRsREQ1q7AjC9lrby+rlDUAXMK6tzkbbrt+OAbqXJwEP2V5l+zngcuCkpvoaERFbGpQxCEkTgcOAuwrrTpF0P/BT4ON18Tjg0ZZqq2kLl4iIaFbjASFpLDAPmGZ7fft62/NtHwScDHy5e7PCrlwoQ9LUevxiybp16wao1xER0WhASBpNFQ5zbV+1tbq2bwVeL2lPqiOG8S2r9wPW9LDdbNudtjs7OjoGqOcREdHkVUwC5gBdtmf2UOcNdT0kHQ7sBDwBLAYOlHSApJ2A04EFTfU1IiK21ORVTJOBKcAKScvrsunABADbs4APAh+R9DzwLHBaPWi9SdI5wHXAKOBS2ysb7GtERLRpLCBs3055LKG1zgxgRg/rFgILG+haRET0Qe6kjoiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQ1FhCSxku6SVKXpJWSzi3U+bCke+rXHZIOaVn3sKQVkpZLWtJUPyMioqyxOamBTcB5tpdJ2hVYKmmR7fta6vwaOMr2k5JOBGYDR7asP8b24w32MSIietBYQNheC6ytlzdI6gLGAfe11LmjZZM7gf2a6k9ERPTPoIxBSJoIHAbctZVqnwCuaXlv4HpJSyVNbbB7ERFR0OQpJgAkjQXmAdNsr++hzjFUAfGOluLJttdI2gtYJOl+27cWtp0KTAWYMGHCgPc/ImKkavQIQtJoqnCYa/uqHuocDFwCnGT7ie5y22vqn48B84FJpe1tz7bdabuzo6NjoD9CRMSI1eRVTALmAF22Z/ZQZwJwFTDF9oMt5WPqgW0kjQGOB+5tqq8REbGlJk8xTQamACskLa/LpgMTAGzPAr4A7AF8s8oTNtnuBPYG5tdlOwKX2b62wb5GRESbJq9iuh1QL3XOAs4qlK8CDtlyi4iIGCy5kzoiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiixgJC0nhJN0nqkrRS0rmFOh+WdE/9ukPSIS3rTpD0gKSHJF3QVD8jIqJsxwb3vQk4z/YySbsCSyUtsn1fS51fA0fZflLSicBs4EhJo4CLgeOA1cBiSQvato2IiAY1dgRhe63tZfXyBqALGNdW5w7bT9Zv7wT2q5cnAQ/ZXmX7OeBy4KSm+hoREVuS7eYbkSYCtwL/wfb6HuqcDxxk+yxJpwIn2D6rXjcFONL2OYXtpgJT67dvAh54md3cE3j8ZW67LYaq3aFsO595+Lc7lG3nM/fP/rY7SiuaPMUEgKSxwDxg2lbC4RjgE8A7uosK1YpJZns21ampbe3nEtud27qf7aXdoWw7n3n4tzuUbeczD5xGA0LSaKpwmGv7qh7qHAxcApxo+4m6eDUwvqXafsCaJvsaEREv1eRVTALmAF22Z/ZQZwJwFTDF9oMtqxYDB0o6QNJOwOnAgqb6GhERW2ryCGIyMAVYIWl5XTYdmABgexbwBWAP4JtVnrDJdqftTZLOAa4DRgGX2l7ZYF9hAE5TbWftDmXb+czDv92hbDufeYAMyiB1RERsf3IndUREFCUgIiKiKAERERFFjd8H8Uok6SCqO7PHUd1fsQZYYLtrSDvWoPozjwPusr2xpfwE29c23PYkwLYXS3oLcAJwv+2FTbZb6Mff2/7IYLZZt/sOqqcD3Gv7+gbbOZLqqsH1knYGLgAOB+4Dvmr7qQbb/gww3/ajTbXRQ7vdVzmusf0zSR8C/pjqyQ2zbT/fYNuvB06huiR/E/BL4AdN/p4H24gbpJb0WeAMqsd3rK6L96P6R3a57YuGqF8fs/3dhvb9GeBsqv9pDgXOtf2jet0y24c30W69/y8CJ1L9MbIIOBK4GXg3cJ3trzTUbvtl0QKOAW4EsP2fmmi3bvtu25Pq5U9S/e7nA8cDP27q35iklcAh9VWAs4FngB8Cx9blH2ii3brtp4CngV8BPwCutL2uqfZa2p1L9W9rF+BfgbFUl84fS/X99tGG2v0M8H7gFuA9wHLgSarA+M+2b26i3UFne0S9gAeB0YXynYBfDmG/Hmlw3yuAsfXyRGAJVUgA/FPDn2sF1aXKuwDrgX9Xl+8M3NNgu8uA7wNHA0fVP9fWy0c1/Jn/qWV5MdBRL48BVjTYblfr529bt7zpz0x1yvp4qvuf1gHXAh8Fdm2w3XvqnzsCvwVG1e/V8L+vFS1t7QLcXC9PGIT/p3YDLgLuB56oX1112e4D2dZIHIN4Adi3UL5Pva4xLY82b3+tAPZusOlRrk8r2X6Y6svyREkzKT/WZCBtsr3Z9jPAr1w/bsX2szT7++4ElgKfA55y9Rfds7ZvsX1Lg+0C7CDpNZL2oPordh2A7aepTkU05V5JH6uXfyGpE0DSG4HGTrXUbPsF29fb/gTV/2PfpDqduKrBdneoTzPtSvVFvVtd/ipgdIPtwoun6F9Vt4/tRwah3SuojlaOtr2H7T2ojo6fBK4cyIZG4hjENOAGSb8Eus+XTgDeAGzxMMABtjfwp1T/IVsJuKPBdn8j6VDbywFsb5T0PuBS4D822C7Ac5J2qQPibd2FknajwYCw/QLwd5KurH/+lsH7974bVTgJsKTX2f5N/VyyJgP5LODrkj5P9eC2n0t6lOrf+VkNtgttn8vVuf8FwIJ6PKQpc6j+kh5F9cfAlZJWAX9EdRq5KZdQTUNwJ/BOYAaApA7gdw22CzDR9ozWAtu/AWZI+vhANjTixiAAJO1ANWg4juof9mpgse3NDbc7B/iu7dsL6y6z/aGG2t2P6i/53xTWTbb9j020W+//VbZ/XyjfE9jH9oqm2m5r773AZNvTB6O9HvqwC7C37V833M6uwL+nCsTVtn/bZHt1m2/0Sx+XM2gk7Qtge42k3anGtx6xfXfD7b4VeDPVxQf3N9lWW7vXAz8D/m/3f1tJewNnAsfZfveAtTUSAyIiYnsl6TVUV6idBOxVF/+W6ojtIr84x862t5WAiIgYHgb6asgERETEMCHpEdsTBmp/I3GQOiJiuyXpnp5WMcBXQyYgIiK2L4N2NWQCIiJi+/ITqhtfl7evkHTzQDaUMYiIiCgaiXdSR0REHyQgIiKiKAERI5KkzZKWt7wu6KX+y7oDW9Il9SPO+7PNXEkPSLpX0qWS+vVsH0kXSjq/fz2N2FLGIGJEkrTR9tim6tfbjOrv41skjaK6QuWauugy4Fbb3+rHPi4ENtr+2/60HdEuRxARNUm71X+5v6l+/wNJn5R0EbBzfaQxt173F5Lursu+XX+xI2mjpC9Jugt4u6SbW56qeoakFfWRwYyWdl+yje2FrgF3U81X0n1kcGm9z1X1nATd+/hc3fefAW8anN9YDHcJiBipur/wu1+nuZoJ7Bzge5JOB15j+zu2L6B6VPihtj8s6c3AaVQP/zsU2Ax8uN7vGKqHtx3Z+lDG+oFyM4B3UU3adISkk3vZZjQwhWpehW4HUR1hTAK+KGm0pLdRTXh1GPAB4IgB+y3FiJb7IGKkerb+cn8J24sk/RlwMXBID9seS/Xo8sWSoJr86LF63WZgXmGbI6gmlVkHf5gJ7Z3A1VvZ5ptUp5duayn7af103N9Leozqpqk/oZru85l63+2z6UW8LAmIiBb1o+DfDDwLvJYXp6V9STWqRy3/dWHdv/Uw7rC1eSC22EbVVK0dwKfa6rY+On0zL/4/nMHEGHA5xRTxUv+FavrGM4DWK4ieb1m+AThV0l4Akl4raf9e9nsXcJSkPevxijOo5jPegqSzqE4jnVFPfNSbW4FTJO1czwXx/j5sE9GrHEHESLWzpOUt76+lmmHvLGCS7Q2SbgU+D3wRmA3cI2lZPQ7xeeD6+ojjeeBs4J97asz2Wkl/DdxEdTSx0PaPeqg+q97Xz+tTWFfZ/tJW9r1M0j8Ay+vtbuupbkR/5DLXiIgoyimmiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUX/H0ui/q9D/baQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gvpbkOcAngNVd22d7qUiStEuYbB7Ey4G7quqj3ft9gHXALcB/6b88SdJsmewU02eARwCS/Bvg413b/XSruEmSdk+TnWKaU1X/r3v9NmBZVS0Hlg8tAiRJ2g1NdgQxp1v+E+C1wDVDfVOeQyFJevqZ7I/8l4BvJbmXwZ1M3wZI8nx83Lck7dYme9TGf07yTeAg4Oqqqq7rGQwmzU0oySLgH4DnMlhoaFlV/deRMScCH+36twJndEuVkuROYAuDhYm2VtXYzn01SdJTMelpoqr6bqPttinseytwZveo8H2B1UlWVtWGoTHfBFZUVSV5CfBl4Iih/tdU1b1T+CxJ0jSb6kS5nVZVd1fVmu71FmAcWDgy5sGho5L5dCvWSZJmX28BMSzJocBRwI2NvrckuQX4OvCeoa4Crk6yOsnSmahTkvRrvQdEN7luOYPrCw+M9lfVpVV1BHASg+sR2yypqqOBE4D3dvMwWvtfmmRVklWbN2+e/i8gSXuoXgMiyTwG4XDRZKvPVdV1DNa83r97v6n7fQ9wKXDMBNstq6qxqhpbsGDBtNYvSXuy3gIiSYALgfGqOm+CMc/vxpHkaGAv4L4k87sL2ySZD7wO+EFftUqSttfnZLclwKnAuqFZ12cDiwGq6gLgrcC7kjzKYJ7F27o7mg4ELu2yYy7wxaq6ssdaJUkjeguIbj5DJhlzLnBuo/0O4MieSpMkTcGM3MUkSXr6MSAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTb0FRJJFSa5NMp5kfZLTG2NOTHJzkrVJViU5dqjv+CS3Jrk9yVl91SlJauttTWpgK3BmVa1Jsi+wOsnKqtowNOabwIqqqiQvAb4MHJFkDnA+cBywEbgpyYqRbSVJPertCKKq7q6qNd3rLcA4sHBkzINVVd3b+cC218cAt1fVHVX1CHAxcGJftUqStjcj1yCSHAocBdzY6HtLkluArwPv6ZoXAncNDdvISLhIkvrVe0Ak2QdYDpxRVQ+M9lfVpVV1BHAS8NFtmzV2VY02kiztrl+s2rx58zRVLUnqNSCSzGMQDhdV1SU7GltV1wG/mWR/BkcMi4a6DwY2TbDdsqoaq6qxBQsWTFPlkqQ+72IKcCEwXlXnTTDm+d04khwN7AXcB9wEHJ7ksCR7AacAK/qqVZK0vT7vYloCnAqsS7K2azsbWAxQVRcAbwXeleRR4GHgbd1F661JTgOuAuYAn6uq9T3WKkka0VtAVNX1tK8lDI85Fzh3gr7Lgct7KE2SNAXOpJYkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpKbeAiLJoiTXJhlPsj7J6Y0x70hyc/dzQ5Ijh/ruTLIuydokq/qqU5LU1tua1MBW4MyqWpNkX2B1kpVVtWFozI+AV1XVz5KcACwDXjHU/5qqurfHGiVJE+gtIKrqbuDu7vWWJOPAQmDD0Jgbhjb5LnBwX/VIknbOjFyDSHIocBRw4w6G/QlwxdD7Aq5OsjrJ0h7LkyQ19HmKCYAk+wDLgTOq6oEJxryGQUAcO9S8pKo2JTkAWJnklqq6rrHtUmApwOLFi6e9fknaU/V6BJFkHoNwuKiqLplgzEuAzwInVtV929qralP3+x7gUuCY1vZVtayqxqpqbMGCBdP9FSRpj9XnXUwBLgTGq+q8CcYsBi4BTq2q24ba53cXtkkyH3gd8IO+apUkba/PU0xLgFOBdUnWdm1nA4sBquoC4BxgP+DTgzxha1WNAQcCl3Ztc4EvVtWVPdYqSRrR511M1wOZZMyfAn/aaL8DOHL7LSRJM8WZ1JKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaeguIJIuSXJtkPMn6JKc3xrwjyc3dzw1JjhzqOz7JrUluT3JWX3VKktrm9rjvrcCZVbUmyb7A6iQrq2rD0JgfAa+qqp8lOQFYBrwiyRzgfOA4YCNwU5IVI9tKknrU2xFEVd1dVWu611uAcWDhyJgbqupn3dvvAgd3r48Bbq+qO6rqEeBi4MS+apUkba/PI4hfSXIocBRw4w6G/QlwRfd6IXDXUN9G4BUT7HspsLR7+2CSW59Ssdpmf+De2S5iMjl3tivQLPG/z+lzyEQdvQdEkn2A5cAZVfXABGNewyAgjt3W1BhWrW2rahmDU1OaRklWVdXYbNchtfjf58zoNSCSzGMQDhdV1SUTjHkJ8FnghKq6r2veCCwaGnYwsKnPWiVJT9TnXUwBLgTGq+q8CcYsBi4BTq2q24a6bgIOT3JYkr2AU4AVfdUqSdpen0cQS4BTgXVJ1nZtZwOLAarqAuAcYD/g04M8YWtVjVXV1iSnAVcBc4DPVdX6HmvV9jxtp12Z/33OgFQ1T+1LkvZwzqSWJDUZEJKkJgNCktQ0IxPltOtLcgSD2eoLGcw52QSsqKrxWS1M0qzxCEIk+SCDx5kE+B6D24wDfMkHJWpXluSPZ7uG3Zl3MYkktwEvqqpHR9r3AtZX1eGzU5m0Y0l+UlWLZ7uO3ZWnmATwOPA84Mcj7Qd1fdKsSXLzRF3AgTNZy57GgBDAGcA3k/yQXz8kcTHwfOC02SpK6hwI/D7ws5H2ADfMfDl7DgNCVNWVSV7A4DHrCxn8w9sI3FRVj81qcRJ8DdinqtaOdiT5xxmvZg/iNQhJUpN3MUmSmgwISVKTASFNIsljSdYm+UGSryR51pPYx7uTfGro/dIkt3Q/q5K8eqhvXpKPJ/lh95nf69Zsl2aUASFN7uGqemlVvRh4BPj3T2VnSd4I/DlwbFUdwWDJ3P+RZNua7R9lcIvxi7vPfBOw71P5TOnJMCCknfNt4PlJfiPJZUluTvLdbmVEJmof8UHgA1V1L0BVrQE+D7y3Ozr5M+B9VfXLrv+fq+rLM/LtpCEGhDRFSeYCJwDrgA8D/7uqXsJgIax/6IZN1D7sRcDqkbZVwG8zmHvyk4nWb5dmkvMgpMntPbQq4rcZLKV7I/BWgKq6Jsl+SZ4NHDtB+2Qy/WVLT40BIU3u4ap66XBDt+b6qKL9h350stEG4GXANUNtRzM4irgdWJxk36ra8qQrlqaBp5ikJ+c64B0A3R1I93anhSZqH/YJ4Nwk+3XjXgq8BfhMVf2CwRHK33YPSyTJQUne2e/XkbbnEYT05Pwn4PPdg+R+AfzRJO2/UlUrkjwP+KfuusZzgSOranM35EPAx4ANSf4FeAg4p8fvIjX5qA1pFnUB8XkGR/PvLP9BahdiQEiSmrwGIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmv4/jbOadaBm1BQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let me show you what I mean by monotonic relationship\n",
    "# between labels and target\n",
    "\n",
    "def analyse_vars(train, y_train, var):\n",
    "    \n",
    "    # function plots median house sale price per encoded\n",
    "    # category\n",
    "    \n",
    "    tmp = pd.concat([X_train, np.log(y_train)], axis=1)\n",
    "    \n",
    "    tmp.groupby(var)['SalePrice'].median().plot.bar()\n",
    "    plt.title(var)\n",
    "    plt.ylim(2.2, 2.6)\n",
    "    plt.ylabel('SalePrice')\n",
    "    plt.show()\n",
    "    \n",
    "for var in cat_others:\n",
    "    analyse_vars(X_train, y_train, var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The monotonic relationship is particularly clear for the variables MSZoning and Neighborhood. Note how, the higher the integer that now represents the category, the higher the mean house sale price.\n",
    "\n",
    "(remember that the target is log-transformed, that is why the differences seem so small)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling\n",
    "\n",
    "For use in linear models, features need to be either scaled. We will scale features to the minimum and maximum values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create scaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "#  fit  the scaler to the train set\n",
    "scaler.fit(X_train) \n",
    "\n",
    "# transform the train and test set\n",
    "\n",
    "# sklearn returns numpy arrays, so we wrap the\n",
    "# array with a pandas dataframe\n",
    "\n",
    "X_train = pd.DataFrame(\n",
    "    scaler.transform(X_train),\n",
    "    columns=X_train.columns\n",
    ")\n",
    "\n",
    "X_test = pd.DataFrame(\n",
    "    scaler.transform(X_test),\n",
    "    columns=X_train.columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>1.00</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>1.00</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>1.00</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.445002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.066176</td>\n",
       "      <td>0.163934</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>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.255670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018114</td>\n",
       "      <td>0.242553</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.566920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.529943</td>\n",
       "      <td>0.333333</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>0.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.084112</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.362482</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.469078</td>\n",
       "      <td>0.045704</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>0.636364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.577658</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>0.555556</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.737705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.333333</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>0.6</td>\n",
       "      <td>0.086818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.434278</td>\n",
       "      <td>0.233224</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.549026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.513216</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>0.333333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.411215</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</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",
       "  </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",
       "3    0.750000      0.75     0.445002      0.0     1.0    1.0  0.666667   \n",
       "4    0.750000      0.75     0.577658      0.0     1.0    1.0  0.333333   \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",
       "3     0.666667        1.0        0.0        0.0      0.454545         0.4   \n",
       "4     0.333333        1.0        0.0        0.0      0.363636         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",
       "3         1.0      0.75         0.6     0.666667         0.50   0.066176   \n",
       "4         1.0      0.75         0.6     0.555556         0.50   0.323529   \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",
       "3      0.163934        0.0       0.0          1.0          1.0    0.333333   \n",
       "4      0.737705        0.0       0.0          0.6          0.7    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",
       "3     0.00000   0.666667        0.5         1.0  0.666667  0.666667   \n",
       "4     0.17000   0.333333        0.5         0.5  0.333333  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",
       "3      1.000000           1.0    0.255670           0.0         0.0   \n",
       "4      0.000000           0.6    0.086818           0.0         0.0   \n",
       "\n",
       "   BsmtUnfSF  TotalBsmtSF  Heating  HeatingQC  CentralAir  Electrical  \\\n",
       "0   0.673479     0.239935      1.0       1.00         1.0         1.0   \n",
       "1   0.114724     0.172340      1.0       1.00         1.0         1.0   \n",
       "2   0.601951     0.286743      1.0       1.00         1.0         1.0   \n",
       "3   0.018114     0.242553      1.0       1.00         1.0         1.0   \n",
       "4   0.434278     0.233224      1.0       0.75         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",
       "3  0.566920       0.0           0.0   0.529943      0.333333           0.0   \n",
       "4  0.549026       0.0           0.0   0.513216      0.000000           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",
       "3  0.666667       0.0         0.375      0.333333     0.666667      0.250000   \n",
       "4  0.666667       0.0         0.375      0.333333     0.333333      0.416667   \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",
       "3         1.0    0.333333          0.4        0.75     0.084112           0.5   \n",
       "4         1.0    0.333333          0.8        0.75     0.411215           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",
       "3        0.50    0.362482         0.5         0.5         1.0    0.469078   \n",
       "4        0.50    0.406206         0.5         0.5         1.0    0.000000   \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",
       "3     0.045704            0.0        0.0          0.0       0.0     0.0   \n",
       "4     0.000000            0.0        1.0          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",
       "3   0.00          1.0      0.0  0.636364  0.666667           0.75   \n",
       "4   0.00          1.0      0.0  0.545455  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  \n",
       "3             1.0            0.0             0.0  \n",
       "4             0.0            0.0             0.0  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
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    "# Conclusion\n",
    "\n",
    "We now have several classes with parameters learned from the training dataset, that we can store and retrieve at a later stage, so that when a colleague comes with new data, we are in a better position to score it faster.\n",
    "\n",
    "Still:\n",
    "\n",
    "- we would need to save each class\n",
    "- then we could load each class\n",
    "- and apply each transformation individually.\n",
    "\n",
    "Which sounds like a lot of work.\n",
    "\n",
    "The good news is, we can reduce the amount of work, if we set up all the transformations within a pipeline.\n",
    "\n",
    "**IMPORTANT**\n",
    "\n",
    "In order to set up the entire feature transformation within a pipeline, we still need to create a class that can be used within a pipeline to map the categorical variables with the arbitrary mappings, and also, to capture elapsed time between the temporal variables.\n",
    "\n",
    "We will take that opportunity to create an in-house package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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