section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb

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    "# Machine Learning Pipeline - Feature Engineering\n",
    "\n",
    "In the following notebooks, we will go through the implementation of each one of the steps in the Machine Learning Pipeline. \n",
    "\n",
    "We will discuss:\n",
    "\n",
    "1. Data Analysis\n",
    "2. **Feature Engineering**\n",
    "3. Feature Selection\n",
    "4. Model Training\n",
    "5. Obtaining Predictions / Scoring\n",
    "\n",
    "\n",
    "We will use the house price dataset available on [Kaggle.com](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data). See below for more details.\n",
    "\n",
    "===================================================================================================\n",
    "\n",
    "## Predicting Sale Price of Houses\n",
    "\n",
    "The aim of the project is to build a machine learning model to predict the sale price of homes based on different explanatory variables describing aspects of residential houses.\n",
    "\n",
    "\n",
    "### Why is this important? \n",
    "\n",
    "Predicting house prices is useful to identify fruitful investments, or to determine whether the price advertised for a house is over or under-estimated.\n",
    "\n",
    "\n",
    "### What is the objective of the machine learning model?\n",
    "\n",
    "We aim to minimise the difference between the real price and the price estimated by our model. We will evaluate model performance with the:\n",
    "\n",
    "1. mean squared error (mse)\n",
    "2. root squared of the mean squared error (rmse)\n",
    "3. r-squared (r2).\n",
    "\n",
    "\n",
    "### How do I download the dataset?\n",
    "\n",
    "- Visit the [Kaggle Website](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data).\n",
    "\n",
    "- Remember to **log in**\n",
    "\n",
    "- Scroll down to the bottom of the page, and click on the link **'train.csv'**, and then click the 'download' blue button towards the right of the screen, to download the dataset.\n",
    "\n",
    "- The download the file called **'test.csv'** and save it in the directory with the notebooks.\n",
    "\n",
    "\n",
    "**Note the following:**\n",
    "\n",
    "-  You need to be logged in to Kaggle in order to download the datasets.\n",
    "-  You need to accept the terms and conditions of the competition to download the dataset\n",
    "-  If you save the file to the directory with the jupyter notebook, then you can run the code as it is written here."
   ]
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   "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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to handle datasets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for the yeo-johnson transformation\n",
    "import scipy.stats as stats\n",
    "\n",
    "# to divide train and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# feature scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# to save the trained scaler class\n",
    "import joblib\n",
    "\n",
    "# to visualise al the columns in the dataframe\n",
    "pd.pandas.set_option('display.max_columns', None)"
   ]
  },
  {
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      "(1460, 81)\n"
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       "      <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": 2,
     "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1314, 79), (146, 79))"
      ]
     },
     "execution_count": 3,
     "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. Put the variables in a similar scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target\n",
    "\n",
    "We apply the logarithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "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": 6,
     "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": 7,
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_string_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace missing values with new label: \"Missing\"\n",
    "\n",
    "X_train[with_string_missing] = X_train[with_string_missing].fillna('Missing')\n",
    "X_test[with_string_missing] = X_test[with_string_missing].fillna('Missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "for var in with_frequent_category:\n",
    "    \n",
    "    # there can be more than 1 mode in a variable\n",
    "    # we take the first one with [0]    \n",
    "    mode = X_train[var].mode()[0]\n",
    "    \n",
    "    print(var, mode)\n",
    "    \n",
    "    X_train[var].fillna(mode, inplace=True)\n",
    "    X_test[var].fillna(mode, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage    0.177321\n",
       "MasVnrArea     0.004566\n",
       "GarageYrBlt    0.056317\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage 69.87974098057354\n",
      "MasVnrArea 103.7974006116208\n",
      "GarageYrBlt 1978.2959677419356\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LotFrontage    0\n",
       "MasVnrArea     0\n",
       "GarageYrBlt    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace missing values as we described above\n",
    "\n",
    "for var in vars_with_na:\n",
    "\n",
    "    # calculate the mean using the train set\n",
    "    mean_val = X_train[var].mean()\n",
    "    \n",
    "    print(var, mean_val)\n",
    "\n",
    "    # add binary missing indicator (in train and test)\n",
    "    X_train[var + '_na'] = np.where(X_train[var].isnull(), 1, 0)\n",
    "    X_test[var + '_na'] = np.where(X_test[var].isnull(), 1, 0)\n",
    "\n",
    "    # replace missing values by the mean\n",
    "    # (in train and test)\n",
    "    X_train[var].fillna(mean_val, inplace=True)\n",
    "    X_test[var].fillna(mean_val, inplace=True)\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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 16,
     "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": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "    }\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the binary missing indicator variables\n",
    "\n",
    "X_train[['LotFrontage_na', 'MasVnrArea_na', 'GarageYrBlt_na']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal variables\n",
    "\n",
    "### Capture elapsed time\n",
    "\n",
    "We learned in the previous notebook, that there are 4 variables that refer to the years in which the house or the garage were built or remodeled. \n",
    "\n",
    "We will capture the time elapsed between those variables and the year in which the house was sold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we drop YrSold\n",
    "X_train.drop(['YrSold'], axis=1, inplace=True)\n",
    "X_test.drop(['YrSold'], axis=1, inplace=True)"
   ]
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]:\n",
    "    X_train[var] = np.log(X_train[var])\n",
    "    X_test[var] = np.log(X_test[var])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-12.55283001172003\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\stats\\morestats.py:1476: RuntimeWarning: divide by zero encountered in log\n",
      "  loglike = -n_samples / 2 * np.log(trans.var(axis=0))\n",
      "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2555: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  w = xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom\n",
      "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2148: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp1 = (x - w) * (fx - fv)\n",
      "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2149: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n"
     ]
    }
   ],
   "source": [
    "# the yeo-johnson transformation learns the best exponent to transform the variable\n",
    "# it needs to learn it from the train set: \n",
    "X_train['LotArea'], param = stats.yeojohnson(X_train['LotArea'])\n",
    "\n",
    "# and then apply the transformation to the test set with the same\n",
    "# parameter: see who this time we pass param as argument to the \n",
    "# yeo-johnson\n",
    "X_test['LotArea'] = stats.yeojohnson(X_test['LotArea'], lmbda=param)\n",
    "\n",
    "print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 26,
     "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "skewed = [\n",
    "    'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n",
    "    '3SsnPorch', 'ScreenPorch', 'MiscVal'\n",
    "]\n",
    "\n",
    "for var in skewed:\n",
    "    \n",
    "    # map the variable values into 0 and 1\n",
    "    X_train[var] = np.where(X_train[var]==0, 0, 1)\n",
    "    X_test[var] = np.where(X_test[var]==0, 0, 1)"
   ]
  },
  {
   "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": 28,
   "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": 29,
   "metadata": {},
   "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": 30,
   "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": 31,
   "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": 32,
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 33,
     "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\".\n",
    "\n",
    "To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 34,
     "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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning Index(['FV', 'RH', 'RL', 'RM'], dtype='object', name='MSZoning')\n",
      "\n",
      "Street Index(['Pave'], dtype='object', name='Street')\n",
      "\n",
      "Alley Index(['Grvl', 'Missing', 'Pave'], dtype='object', name='Alley')\n",
      "\n",
      "LotShape Index(['IR1', 'IR2', 'Reg'], dtype='object', name='LotShape')\n",
      "\n",
      "LandContour Index(['Bnk', 'HLS', 'Low', 'Lvl'], dtype='object', name='LandContour')\n",
      "\n",
      "Utilities Index(['AllPub'], dtype='object', name='Utilities')\n",
      "\n",
      "LotConfig Index(['Corner', 'CulDSac', 'FR2', 'Inside'], dtype='object', name='LotConfig')\n",
      "\n",
      "LandSlope Index(['Gtl', 'Mod'], dtype='object', name='LandSlope')\n",
      "\n",
      "Neighborhood Index(['Blmngtn', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor',\n",
      "       'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NWAmes',\n",
      "       'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW',\n",
      "       'Somerst', 'StoneBr', 'Timber'],\n",
      "      dtype='object', name='Neighborhood')\n",
      "\n",
      "Condition1 Index(['Artery', 'Feedr', 'Norm', 'PosN', 'RRAn'], dtype='object', name='Condition1')\n",
      "\n",
      "Condition2 Index(['Norm'], dtype='object', name='Condition2')\n",
      "\n",
      "BldgType Index(['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE'], dtype='object', name='BldgType')\n",
      "\n",
      "HouseStyle Index(['1.5Fin', '1Story', '2Story', 'SFoyer', 'SLvl'], dtype='object', name='HouseStyle')\n",
      "\n",
      "RoofStyle Index(['Gable', 'Hip'], dtype='object', name='RoofStyle')\n",
      "\n",
      "RoofMatl Index(['CompShg'], dtype='object', name='RoofMatl')\n",
      "\n",
      "Exterior1st Index(['AsbShng', 'BrkFace', 'CemntBd', 'HdBoard', 'MetalSd', 'Plywood',\n",
      "       'Stucco', 'VinylSd', 'Wd Sdng', 'WdShing'],\n",
      "      dtype='object', name='Exterior1st')\n",
      "\n",
      "Exterior2nd Index(['AsbShng', 'BrkFace', 'CmentBd', 'HdBoard', 'MetalSd', 'Plywood',\n",
      "       'Stucco', 'VinylSd', 'Wd Sdng', 'Wd Shng'],\n",
      "      dtype='object', name='Exterior2nd')\n",
      "\n",
      "MasVnrType Index(['BrkFace', 'None', 'Stone'], dtype='object', name='MasVnrType')\n",
      "\n",
      "Foundation Index(['BrkTil', 'CBlock', 'PConc', 'Slab'], dtype='object', name='Foundation')\n",
      "\n",
      "Heating Index(['GasA', 'GasW'], dtype='object', name='Heating')\n",
      "\n",
      "CentralAir Index(['N', 'Y'], dtype='object', name='CentralAir')\n",
      "\n",
      "Electrical Index(['FuseA', 'FuseF', 'SBrkr'], dtype='object', name='Electrical')\n",
      "\n",
      "Functional Index(['Min1', 'Min2', 'Mod', 'Typ'], dtype='object', name='Functional')\n",
      "\n",
      "GarageType Index(['Attchd', 'Basment', 'BuiltIn', 'Detchd'], dtype='object', name='GarageType')\n",
      "\n",
      "PavedDrive Index(['N', 'P', 'Y'], dtype='object', name='PavedDrive')\n",
      "\n",
      "PoolQC Index(['Missing'], dtype='object', name='PoolQC')\n",
      "\n",
      "MiscFeature Index(['Missing', 'Shed'], dtype='object', name='MiscFeature')\n",
      "\n",
      "SaleType Index(['COD', 'New', 'WD'], dtype='object', name='SaleType')\n",
      "\n",
      "SaleCondition Index(['Abnorml', 'Family', 'Normal', 'Partial'], dtype='object', name='SaleCondition')\n",
      "\n",
      "MSSubClass Int64Index([20, 30, 50, 60, 70, 75, 80, 85, 90, 120, 160, 190], dtype='int64', name='MSSubClass')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def find_frequent_labels(df, var, rare_perc):\n",
    "    \n",
    "    # function finds the labels that are shared by more than\n",
    "    # a certain % of the houses in the dataset\n",
    "\n",
    "    df = df.copy()\n",
    "\n",
    "    tmp = df.groupby(var)[var].count() / len(df)\n",
    "\n",
    "    return tmp[tmp > rare_perc].index\n",
    "\n",
    "\n",
    "for var in cat_others:\n",
    "    \n",
    "    # find the frequent categories\n",
    "    frequent_ls = find_frequent_labels(X_train, var, 0.01)\n",
    "    \n",
    "    print(var, frequent_ls)\n",
    "    print()\n",
    "    \n",
    "    # replace rare categories by the string \"Rare\"\n",
    "    X_train[var] = np.where(X_train[var].isin(\n",
    "        frequent_ls), X_train[var], 'Rare')\n",
    "    \n",
    "    X_test[var] = np.where(X_test[var].isin(\n",
    "        frequent_ls), X_test[var], 'Rare')"
   ]
  },
  {
   "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.\n",
    "\n",
    "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this function will assign discrete values to the strings of the variables,\n",
    "# so that the smaller value corresponds to the category that shows the smaller\n",
    "# mean house sale price\n",
    "\n",
    "def replace_categories(train, test, y_train, var, target):\n",
    "    \n",
    "    tmp = pd.concat([X_train, y_train], axis=1)\n",
    "    \n",
    "    # order the categories in a variable from that with the lowest\n",
    "    # house sale price, to that with the highest\n",
    "    ordered_labels = tmp.groupby([var])[target].mean().sort_values().index\n",
    "\n",
    "    # create a dictionary of ordered categories to integer values\n",
    "    ordinal_label = {k: i for i, k in enumerate(ordered_labels, 0)}\n",
    "    \n",
    "    print(var, ordinal_label)\n",
    "    print()\n",
    "\n",
    "    # use the dictionary to replace the categorical strings by integers\n",
    "    train[var] = train[var].map(ordinal_label)\n",
    "    test[var] = test[var].map(ordinal_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning {'Rare': 0, 'RM': 1, 'RH': 2, 'RL': 3, 'FV': 4}\n",
      "\n",
      "Street {'Rare': 0, 'Pave': 1}\n",
      "\n",
      "Alley {'Grvl': 0, 'Pave': 1, 'Missing': 2}\n",
      "\n",
      "LotShape {'Reg': 0, 'IR1': 1, 'Rare': 2, 'IR2': 3}\n",
      "\n",
      "LandContour {'Bnk': 0, 'Lvl': 1, 'Low': 2, 'HLS': 3}\n",
      "\n",
      "Utilities {'Rare': 0, 'AllPub': 1}\n",
      "\n",
      "LotConfig {'Inside': 0, 'FR2': 1, 'Corner': 2, 'Rare': 3, 'CulDSac': 4}\n",
      "\n",
      "LandSlope {'Gtl': 0, 'Mod': 1, 'Rare': 2}\n",
      "\n",
      "Neighborhood {'IDOTRR': 0, 'MeadowV': 1, 'BrDale': 2, 'Edwards': 3, 'BrkSide': 4, 'OldTown': 5, 'Sawyer': 6, 'SWISU': 7, 'NAmes': 8, 'Mitchel': 9, 'SawyerW': 10, 'Rare': 11, 'NWAmes': 12, 'Gilbert': 13, 'Blmngtn': 14, 'CollgCr': 15, 'Crawfor': 16, 'ClearCr': 17, 'Somerst': 18, 'Timber': 19, 'StoneBr': 20, 'NridgHt': 21, 'NoRidge': 22}\n",
      "\n",
      "Condition1 {'Artery': 0, 'Feedr': 1, 'Norm': 2, 'RRAn': 3, 'Rare': 4, 'PosN': 5}\n",
      "\n",
      "Condition2 {'Rare': 0, 'Norm': 1}\n",
      "\n",
      "BldgType {'2fmCon': 0, 'Duplex': 1, 'Twnhs': 2, '1Fam': 3, 'TwnhsE': 4}\n",
      "\n",
      "HouseStyle {'SFoyer': 0, '1.5Fin': 1, 'Rare': 2, '1Story': 3, 'SLvl': 4, '2Story': 5}\n",
      "\n",
      "RoofStyle {'Gable': 0, 'Rare': 1, 'Hip': 2}\n",
      "\n",
      "RoofMatl {'CompShg': 0, 'Rare': 1}\n",
      "\n",
      "Exterior1st {'AsbShng': 0, 'Wd Sdng': 1, 'WdShing': 2, 'MetalSd': 3, 'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7, 'BrkFace': 8, 'CemntBd': 9, 'VinylSd': 10}\n",
      "\n",
      "Exterior2nd {'AsbShng': 0, 'Wd Sdng': 1, 'MetalSd': 2, 'Wd Shng': 3, 'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7, 'BrkFace': 8, 'CmentBd': 9, 'VinylSd': 10}\n",
      "\n",
      "MasVnrType {'Rare': 0, 'None': 1, 'BrkFace': 2, 'Stone': 3}\n",
      "\n",
      "Foundation {'Slab': 0, 'BrkTil': 1, 'CBlock': 2, 'Rare': 3, 'PConc': 4}\n",
      "\n",
      "Heating {'Rare': 0, 'GasW': 1, 'GasA': 2}\n",
      "\n",
      "CentralAir {'N': 0, 'Y': 1}\n",
      "\n",
      "Electrical {'Rare': 0, 'FuseF': 1, 'FuseA': 2, 'SBrkr': 3}\n",
      "\n",
      "Functional {'Rare': 0, 'Min2': 1, 'Mod': 2, 'Min1': 3, 'Typ': 4}\n",
      "\n",
      "GarageType {'Rare': 0, 'Detchd': 1, 'Basment': 2, 'Attchd': 3, 'BuiltIn': 4}\n",
      "\n",
      "PavedDrive {'N': 0, 'P': 1, 'Y': 2}\n",
      "\n",
      "PoolQC {'Missing': 0, 'Rare': 1}\n",
      "\n",
      "MiscFeature {'Rare': 0, 'Shed': 1, 'Missing': 2}\n",
      "\n",
      "SaleType {'COD': 0, 'Rare': 1, 'WD': 2, 'New': 3}\n",
      "\n",
      "SaleCondition {'Rare': 0, 'Abnorml': 1, 'Family': 2, 'Normal': 3, 'Partial': 4}\n",
      "\n",
      "MSSubClass {30: 0, 'Rare': 1, 190: 2, 90: 3, 160: 4, 50: 5, 85: 6, 70: 7, 80: 8, 20: 9, 75: 10, 120: 11, 60: 12}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for var in cat_others:\n",
    "    replace_categories(X_train, X_test, y_train, var, 'SalePrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 38,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o0QF55GhExE6syYVyERHxAtbpSWokHQ8cBOw60Gb7c00UFRER3dfRZa6S5lI9Ve5MqpPUHwD2b7CuiIjosk7XQbzZ9mnAY7b/BngTcGBzZUVERLd1GhBb6n+fkLQf1eNE922mpIiIGAs6PQdxtaSXAn8HLK/bvtpIRRERMSYMtw7icOBB2+fX7ycBq4A7gH9svryIiOiW4Q4xXQI8BSDpLcAFddvj1E9xi4iIndNwh5h2aXlQz8nAPNvzgfktDwGKiIid0HAziF0kDYTIHwI/bOnreA1FRES88Az3S/7bwA2SHqG6kunHAJJeQ273HRGxUxtyBmH7b4GzqZ4od7Rtt2x35lDbSpouaYmkPkmrJc0pjDlR0m2SVkpaJunolr6n6/aVkhZu7w8WERE7ZtjDRLZvKbTd1cG++4Gz61uF7wksl7TYdl/LmB8AC21b0sHAd4CZdd8W24d28H0iIqIBnS6U226219leUb/eBKwBpraN2dwyK9mD+ol1ERHRfY0FRCtJM4DDgFsLfe+TdAdwDfCRlq5d68NOt0h672jUGRERWzUeEPXiuvnAWbY3tvfbvtL2TOC9wPktXfvb7gU+BHxB0qsH2f/sOkiWrV+/fuR/gIiIcarRgJA0kSocLh/u6XO2bwReJWly/X5t/e+9wI+oZiCl7ebZ7rXdO2XKlJEsPyJiXGssICQJuBRYY/uiQca8ph6HpDcALwE2SNpL0kvq9snAUUBfaR8REdGMJhe7HQXMAla1rLo+F+gBsD0XeD9wmqTfUa2zOLm+oum1wCWSnqEKsQvarn6KiIiGNRYQtm+ierjQUGMuBC4stN8MvL6h0iIiogOjchVTRES88CQgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqKosYCQNF3SEkl9klZLmlMYc6Kk2yStlLRM0tEtfadLurv+Or2pOiMioqyxZ1ID/cDZtldI2hNYLmmx7b6WMT8AFtq2pIOB7wAzJb0M+CzQC7jedqHtxxqsNyIiWjQ2g7C9zvaK+vUmYA0wtW3MZtuu3+5BFQYA7wIW2360DoXFwLFN1RoREdsalXMQkmYAhwG3FvreJ+kO4BrgI3XzVODBlmEP0RYuERHRrMYDQtIkYD5wlu2N7f22r7Q9E3gvcP7z2P/s+vzFsvXr1+9wvRERUWk0ICRNpAqHy20vGGqs7RuBV0maDKwFprd0T6vbStvNs91ru3fKlCkjVHlERDR5FZOAS4E1ti8aZMxr6nFIegPwEmADcB3wTkl7SdoLeGfdFhERo6TJq5iOAmYBqyStrNvOBXoAbM8F3g+cJul3wBbg5Pqk9aOSzgeW1tt9zvajDdYaERFtGgsI2zcBGmbMhcCFg/RdBlzWQGkREdGBrKSOiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFDUWEJKmS1oiqU/SaklzCmNOlXSbpFWSbpZ0SEvf/XX7SknLmqozIiLKGnsmNdAPnG17haQ9geWSFtvuaxlzH/BW249JOg6YBxzZ0n+M7UcarDEiIgbRWEDYXgesq19vkrQGmAr0tYy5uWWTW4BpTdUTERHbZ1TOQUiaARwG3DrEsI8C3295b+B6ScslzW6wvIiIKGjyEBMAkiYB84GzbG8cZMwxVAFxdEvz0bbXSno5sFjSHbZvLGw7G5gN0NPTM+L1R0SMV43OICRNpAqHy20vGGTMwcBXgRNtbxhot722/vdh4ErgiNL2tufZ7rXdO2XKlJH+ESIixq0mr2IScCmwxvZFg4zpARYAs2zf1dK+R31iG0l7AO8Ebm+q1oiI2FaTh5iOAmYBqyStrNvOBXoAbM8FzgP2Br5U5Qn9tnuBfYAr67YJwLdsX9tgrRER0abJq5huAjTMmI8BHyu03wscsu0WERExWrKSOiIiihIQERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKLGAkLSdElLJPVJWi1pTmHMqZJuk7RK0s2SDmnpO1bSnZLukXROU3VGRETZhAb33Q+cbXuFpD2B5ZIW2+5rGXMf8Fbbj0k6DpgHHClpF+Bi4B3AQ8BSSQvbto2IiAY1NoOwvc72ivr1JmANMLVtzM22H6vf3gJMq18fAdxj+17bTwFXACc2VWtERGxLtpv/JtIM4EbgD2xvHGTMp4CZtj8m6STgWNsfq/tmAUfa/mRhu9nA7Prt7wN3NvAjbI/JwCNdrmGsyGexVT6LrfJZbDUWPov9bU8pdTR5iAkASZOA+cBZQ4TDMcBHgaO3d/+251EdmhoTJC2z3dvtOsaCfBZb5bPYKp/FVmP9s2g0ICRNpAqHy20vGGTMwcBXgeNsb6ib1wLTW4ZNq9siImKUNHkVk4BLgTW2LxpkTA+wAJhl+66WrqXAAZJeKenFwCnAwqZqjYiIbTU5gzgKmAWskrSybjsX6AGwPRc4D9gb+FKVJ/Tb7rXdL+mTwHXALsBltlc3WOtIGjOHu8aAfBZb5bPYKp/FVmP6sxiVk9QREfHCk5XUERFRlICIiIiiBERERBQ1vg5iZydpJtUq74FV4muBhbbXdK+q6Lb6v4upwK22N7e0H2v72u5VNvokHQHY9lJJrwOOBe6wvajLpXWVpH+xfVq36xhKTlLvAEmfAT5IdSuQh+rmaVSX5V5h+4Ju1TaWSPqw7a91u47RIum/A5+gur3MocAc2/9a962w/YYuljeqJH0WOI7qj9HFwJHAEqr7rF1n+2+7WN6okdR+mb6AY4AfAtj+41EvqgMJiB0g6S7gINu/a2t/MbDa9gHdqWxskfRL2z3drmO0SFoFvMn25vo2M98DvmH7nyT9zPZh3a1w9NSfxaHAS4D/AKbZ3ihpN6rZ1cHdrG+0SFoB9FEtCjZVQHyb6o9JbN/QveoGl0NMO+YZYD/ggbb2feu+cUPSbYN1AfuMZi1jwIsGDivZvl/S24DvSdqf6vMYT/ptPw08IekXA7fbsb1F0nj6f6QXmAP8JfA/ba+UtGWsBsOABMSOOQv4gaS7gQfrth7gNcA2Nxbcye0DvAt4rK1dwM2jX05X/VrSobZXAtQziROAy4DXd7Wy0feUpN1tPwG8caBR0u8xjv6Isv0M8I+Svlv/+2teAL9/x3yBY5ntayUdSHV78taT1Evrv5rGk6uBSQO/FFtJ+tGoV9Ndp1E9D+VZtvuB0yRd0p2SuuYttp+EZ39JDpgInN6dkrrH9kPAByQdDxRvXjqW5BxEREQUZR1EREQUJSAiIqIoAREBSLKkb7a8nyBpvaSr6/f7SLpa0s8l9UlaVLd/QtLKlq/b63299nnWsUjSS0fkh4rYQTkHEQFI2gzcQ7V+YYuk44D/DTxk+4T65HKf7X+qxx9se5tLeyV9Huix/aejWX9EEzKDiNhqEXB8/fqDVAuZBuzL1tXyDBIObwH+BPjz+v2ukr4maZWkn9WP1kXSGZIWSLpW0t2S/q5lH/dLmixphqQ1kr4iabWk6+vFZUg6XNJt9Yzl7yXdPsKfQwSQgIhodQVwiqRdgYOBW1v6LgYulbRE0l9K2q91w/qw0NeB01uevf4JqnsQvZ4qcP5vvW+oVhefTLUu4mRJrY/YHXAAcLHtg4DfAO+v278G/JntQ4Hxdjl1jKIEREStnhXMoPplvqit7zrgVcBXgJnAzyRNaRkyl+p2Gj9paTsa+Ga9/R1UK+4PrPt+YPtx2/+P6hYM+xdKuq9lXclyYEYdRHva/mnd/q3t/0kjOpOAiHiuhcA/8NzDSwDYftT2t2zPonpu+lsAJJ1O9Qv+/O34Pk+2vH6a8qLVTsZENCYBEfFclwF/Y3tVa6Okt0vavX69J/Bq4JeSXgV8Hji1Xi3d6sfAqfU2B1LdhuXOHSnO9m+ATZKOrJtO2ZH9RQwlf5FEtKhvhfDPha43Al+U1E/1h9VX6+cbXALsDiyQnnMfvjOBLwFfru9o2g+cYfvJtnHPx0eBr9Q3u7sBeHxHdxhRkstcI15gJE0auFuspHOAfW3P6XJZsRPKDCLihed4SX9B9f/vA8AZ3S0ndlaZQURERFFOUkdERFECIiIiihIQERFRlICIiIiiBERERBQlICIiouj/AzQQxsJigEKcAAAAAElFTkSuQmCC\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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\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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\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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\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": 41,
   "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": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's now save the train and test sets for the next notebook!\n",
    "\n",
    "X_train.to_csv('xtrain.csv', index=False)\n",
    "X_test.to_csv('xtest.csv', index=False)\n",
    "\n",
    "y_train.to_csv('ytrain.csv', index=False)\n",
    "y_test.to_csv('ytest.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['minmax_scaler.joblib']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now let's save the scaler\n",
    "\n",
    "joblib.dump(scaler, 'minmax_scaler.joblib') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "That concludes the feature engineering section.\n",
    "\n",
    "# Additional Resources\n",
    "\n",
    "- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n",
    "- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n",
    "- [Feature Engineering for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-engineering-for-machine-learning/) - Article\n",
    "- [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd) - Article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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