DataCleaning & Feature Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from time import time\n",
    "from scipy import stats\n",
    "from scipy.stats import norm, skew #for some statistics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the train and test data:\n",
    "train = pd.read_csv('data/train.csv')\n",
    "test = pd.read_csv('data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (1460, 81)\n",
      "Test: (1459, 80)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train:\", train.shape)\n",
    "print(\"Test:\",test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking the target variable from the train dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train[['Id', 'SalePrice']]\n",
    "train.drop('SalePrice', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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>...</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",
       "    </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>...</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",
       "    </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>...</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",
       "    </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>...</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",
       "    </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>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\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      ...       ScreenPorch PoolArea PoolQC Fence  \\\n",
       "0         Lvl    AllPub      ...                 0        0    NaN   NaN   \n",
       "1         Lvl    AllPub      ...                 0        0    NaN   NaN   \n",
       "2         Lvl    AllPub      ...                 0        0    NaN   NaN   \n",
       "3         Lvl    AllPub      ...                 0        0    NaN   NaN   \n",
       "4         Lvl    AllPub      ...                 0        0    NaN   NaN   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0      2    2008        WD         Normal  \n",
       "1         NaN       0      5    2007        WD         Normal  \n",
       "2         NaN       0      9    2008        WD         Normal  \n",
       "3         NaN       0      2    2006        WD        Abnorml  \n",
       "4         NaN       0     12    2008        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers:\n",
    "\n",
    "Checking the data for outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.scatter(x = train['GrLivArea'], y = y_train['SalePrice'])\n",
    "plt.ylabel('SalePrice', fontsize=13)\n",
    "plt.xlabel('GrLivArea', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot, we can see that there are 2 outliers that have a very low \"SalePrice\" despite having a huge \"GrLivArea\". We can safely remove them as they're huge deviation from the trend are just noise in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target Variable Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "a17ad845-6fca-4d47-8e44-7c4c44f0427d",
    "_execution_state": "idle",
    "_uuid": "be3b0157031685ed3dbc31a657ba712312691830",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 180921.20 and sigma = 79415.29\n",
      "\n"
     ]
    },
    {
     "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": [
    "sns.distplot(y_train['SalePrice'] , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(y_train['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('SalePrice distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(y_train['SalePrice'], plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "313a535c-86c4-4db5-96de-6f65bc4adc2f",
    "_execution_state": "idle",
    "_uuid": "1da2d9831ae6c94b3f6304185f02896a9ee40aa5"
   },
   "source": [
    "The target variable is right skewed.  As (linear) models love normally distributed data , we need to transform this variable and make it more normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8df72eef-77de-4a71-aa6a-4b91784a7232",
    "_execution_state": "idle",
    "_uuid": "421775277fdab4e5a05f74aa4ea92e712a743928"
   },
   "source": [
    " **Log-transformation of the target variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "21b3a0ad-bd68-49aa-a3d7-40a30b3c59dc",
    "_execution_state": "idle",
    "_uuid": "719cf6a9dca56cc529e97af21816d291fa8bd8c0",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 12.02 and sigma = 0.40\n",
      "\n"
     ]
    },
    {
     "data": {
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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": [
    "#We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
    "y_train[\"SalePrice\"] = np.log1p(y_train[\"SalePrice\"])\n",
    "\n",
    "#Check the new distribution \n",
    "sns.distplot(y_train['SalePrice'] , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(y_train['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('SalePrice distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(y_train['SalePrice'], plot=plt)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "51620309-727a-4445-a96a-d9851880d31f",
    "_execution_state": "idle",
    "_uuid": "991e699566f4292490fda326703baa33ce09173f"
   },
   "source": [
    "The skew seems now corrected and the data appears more normally distributed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see there are many categorical features and features with missing values, we have to handle them before we start working on the models. In order to handle the train data and test data in a similar way, we can combine them and do the feature engineering part and then seperate them before modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntrain = train.shape[0]\n",
    "ntest = test.shape[0]\n",
    "all_data = pd.concat([train, test], axis = 0, ignore_index= True)\n",
    "all_ID = all_data['Id']\n",
    "all_data.drop('Id', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Engineering inspired from: https://www.kaggle.com/serigne/stacked-regressions-top-4-on-leaderboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC          99.657417\n",
       "MiscFeature     96.402878\n",
       "Alley           93.216855\n",
       "Fence           80.438506\n",
       "FireplaceQu     48.646797\n",
       "LotFrontage     16.649538\n",
       "GarageCond       5.447071\n",
       "GarageQual       5.447071\n",
       "GarageYrBlt      5.447071\n",
       "GarageFinish     5.447071\n",
       "GarageType       5.378554\n",
       "BsmtCond         2.809181\n",
       "BsmtExposure     2.809181\n",
       "BsmtQual         2.774923\n",
       "BsmtFinType2     2.740665\n",
       "BsmtFinType1     2.706406\n",
       "MasVnrType       0.822199\n",
       "MasVnrArea       0.787941\n",
       "MSZoning         0.137033\n",
       "BsmtHalfBath     0.068517\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missingRatio = (all_data.isnull().sum()/len(all_data)*100).sort_values(ascending = False)\n",
    "missingRatio.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see that there are features with NaN values in them. We cannot delete those samples from our data as there are certain features like \"PoolQC\" which only have 7 non-NaN values but can significantly impact the target variable 'SalePrice'. We can either fill the NaN valued fields with a new category for that feature, say \"None\", or follow one of the multiple available options to fill the NaN values by mean, median, highest, mode or zeros based on the feature individually."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the data, we can also see that there are a lot of numerical features that should actually be categorical and not numerical. For example, \"month of sale\" which is currently numerical, but should be categorical as selling in January or December cannot be compared numerically. We'll have to address them as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with the features with Missing values:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first inspect the data to identify the features with missing values and their missing ratios, so that we can decide whether we should remove them from our dataset or to fill them with some value and use them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PoolQC** : data description says NA means \"No  Pool\". That make sense, given the huge ratio of missing value (+99%) and majority of houses have no Pool at all in general. \n",
    "\n",
    "**MiscFeature** : data description says NA means \"no misc feature\"\n",
    "\n",
    "**Alley** : data description says NA means \"no alley access\"\n",
    "\n",
    "**Fence** : data description says NA means \"no fence\"\n",
    "\n",
    "**FireplaceQu** : data description says NA means \"no fireplace\"\n",
    "\n",
    "**LotFrontage** : Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood , we can **fill in missing values by the median LotFrontage of the neighborhood Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood\n",
    "\n",
    "**GarageType, GarageFinish, GarageQual and GarageCond** : Replacing missing data with None\n",
    "\n",
    "**GarageYrBlt, GarageArea and GarageCars** : Replacing missing data with 0 (Since No garage = no cars in such garage.)\n",
    "\n",
    "**BsmtFinSF1, BsmtFinSF2, BsmtUnfSF, TotalBsmtSF, BsmtFullBath and BsmtHalfBath** : missing values are likely zero for having no basement\n",
    "\n",
    "**BsmtQual, BsmtCond, BsmtExposure, BsmtFinType1 and BsmtFinType2** : For all these categorical basement-related features, NaN means that there is no  basement.\n",
    "\n",
    "**MasVnrArea and MasVnrType** : NA most likely means no masonry veneer for these houses. We can fill 0 for the area and None for the type. \n",
    "\n",
    "**MSZoning (The general zoning classification)** :  'RL' is by far  the most common value.  So we can fill in missing values with 'RL'\n",
    "\n",
    "**Utilities** : For this categorical feature all records are \"AllPub\", except for one \"NoSeWa\"  and 2 NA . Since the house with 'NoSewa' is in the training set, `this feature won't help in predictive modelling`. We can then safely  remove it.\n",
    "\n",
    "**Functional** : data description says NA means typical\n",
    "\n",
    "**Electrical** : It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value.\n",
    "\n",
    "**KitchenQual**: Only one NA value, and same as Electrical, we set 'TA' (which is the most frequent)  for the missing value in KitchenQual.\n",
    "\n",
    "**Exterior1st and Exterior2nd** : Again Both Exterior 1 & 2 have only one missing value. We will just substitute in the most common string\n",
    "\n",
    "**SaleType** : Fill in again with most frequent which is \"WD\"\n",
    "\n",
    "**MSSubClass** : Na most likely means No building class. We can replace missing values with None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cols = train_clean[missingRatio[(missingRatio<=15) & (missingRatio >0)].index]\n",
    "cols = all_data[missingRatio[missingRatio >0].index]\n",
    "for col in cols:\n",
    "    if col in ('Alley', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Fence', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'MasVnrType', 'MiscFeature', 'PoolQC'):\n",
    "        all_data[col] = all_data[col].fillna(\"None\")\n",
    "    elif col == 'LotFrontage':\n",
    "        all_data['LotFrontage'] = all_data.groupby('Neighborhood')['LotFrontage'].transform(lambda x: x.fillna(x.median()))\n",
    "    elif col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'GarageYrBlt', 'GarageArea', 'GarageCars', 'MasVnrArea'):\n",
    "        all_data[col] = all_data[col].fillna(0)\n",
    "    elif col in ('MSZoning', 'Electrical', 'KitchenQual', 'Exterior1st', 'Exterior2nd', 'SaleType', 'MSSubClass'):\n",
    "        all_data[col] = all_data[col].fillna(all_data[col].mode()[0])\n",
    "    elif col == 'Utilities':\n",
    "        all_data = all_data.drop(['Utilities'], axis=1)\n",
    "    elif col == 'Functional':\n",
    "        all_data[\"Functional\"] = all_data[\"Functional\"].fillna(\"Typ\")\n",
    "    else:\n",
    "        all_data[col].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for missing values, if we've missed any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missingRatio = (all_data.isnull().sum()/len(all_data)*100).sort_values(ascending = False)\n",
    "missingRatio[missingRatio != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Label Encoding some ordinal categorical variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape all_data: (2919, 78)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "cols = ('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond', \n",
    "        'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1', \n",
    "        'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',\n",
    "        'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond', \n",
    "        'YrSold', 'MoSold')\n",
    "# process columns, apply LabelEncoder to categorical features\n",
    "for c in cols:\n",
    "    lbl = LabelEncoder() \n",
    "    lbl.fit(list(all_data[c].values)) \n",
    "    all_data[c] = lbl.transform(list(all_data[c].values))\n",
    "\n",
    "# shape        \n",
    "print('Shape all_data: {}'.format(all_data.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming some numerical variables that are really categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MSSubClass=The building class\n",
    "all_data['MSSubClass'] = all_data['MSSubClass'].apply(str)\n",
    "\n",
    "#Changing OverallCond into a categorical variable\n",
    "all_data['OverallCond'] = all_data['OverallCond'].astype(str)\n",
    "\n",
    "#Year and month sold are transformed into categorical features.\n",
    "all_data['YrSold'] = all_data['YrSold'].astype(str)\n",
    "all_data['MoSold'] = all_data['MoSold'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding an important feature that wasn't defined in the earlier given data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding total sqfootage feature \n",
    "all_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data['1stFlrSF'] + all_data['2ndFlrSF']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling the skewness in the features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skew in numerical features: \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Skew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>21.947195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>16.898328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>12.822431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <td>12.088761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>11.376065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope</th>\n",
       "      <td>4.975157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>4.302254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>4.146143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>4.003891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>3.946694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Skew\n",
       "MiscVal        21.947195\n",
       "PoolArea       16.898328\n",
       "LotArea        12.822431\n",
       "LowQualFinSF   12.088761\n",
       "3SsnPorch      11.376065\n",
       "LandSlope       4.975157\n",
       "KitchenAbvGr    4.302254\n",
       "BsmtFinSF2      4.146143\n",
       "EnclosedPorch   4.003891\n",
       "ScreenPorch     3.946694"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\n",
    "\n",
    "# Check the skew of all numerical features\n",
    "skewed_feats = all_data[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
    "print(\"\\nSkew in numerical features: \\n\")\n",
    "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
    "skewness.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Box Cox Transformation of (highly) skewed features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 55 skewed numerical features to Box Cox transform\n"
     ]
    }
   ],
   "source": [
    "skewness = skewness[abs(skewness) > 0.75]\n",
    "print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))\n",
    "\n",
    "from scipy.special import boxcox1p\n",
    "skewed_features = skewness.index\n",
    "lam = 0.15\n",
    "for feat in skewed_features:\n",
    "    #all_data[feat] += 1\n",
    "    all_data[feat] = boxcox1p(all_data[feat], lam)\n",
    "    \n",
    "#all_data[skewed_features] = np.log1p(all_data[skewed_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 79)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, the data now has no missing values. Let's proceed to encoding the categorical features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding all categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.get_dummies(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.831328</td>\n",
       "      <td>19.212182</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>1.540963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.440268</td>\n",
       "      <td>14.187527</td>\n",
       "      <td>14.187527</td>\n",
       "      <td>8.059126</td>\n",
       "      <td>...</td>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.221214</td>\n",
       "      <td>19.712205</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>1.540963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.259674</td>\n",
       "      <td>14.145138</td>\n",
       "      <td>14.145138</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.914940</td>\n",
       "      <td>20.347241</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.440268</td>\n",
       "      <td>14.184404</td>\n",
       "      <td>14.185966</td>\n",
       "      <td>7.646538</td>\n",
       "      <td>...</td>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.684507</td>\n",
       "      <td>19.691553</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.440268</td>\n",
       "      <td>14.047529</td>\n",
       "      <td>14.135652</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>4</th>\n",
       "      <td>6.314735</td>\n",
       "      <td>21.325160</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.602594</td>\n",
       "      <td>14.182841</td>\n",
       "      <td>14.182841</td>\n",
       "      <td>9.391827</td>\n",
       "      <td>...</td>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 259 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotFrontage    LotArea    Street     Alley  LotShape  LandSlope  \\\n",
       "0     5.831328  19.212182  0.730463  0.730463  1.540963        0.0   \n",
       "1     6.221214  19.712205  0.730463  0.730463  1.540963        0.0   \n",
       "2     5.914940  20.347241  0.730463  0.730463  0.000000        0.0   \n",
       "3     5.684507  19.691553  0.730463  0.730463  0.000000        0.0   \n",
       "4     6.314735  21.325160  0.730463  0.730463  0.000000        0.0   \n",
       "\n",
       "   OverallQual  YearBuilt  YearRemodAdd  MasVnrArea          ...            \\\n",
       "0     2.440268  14.187527     14.187527    8.059126          ...             \n",
       "1     2.259674  14.145138     14.145138    0.000000          ...             \n",
       "2     2.440268  14.184404     14.185966    7.646538          ...             \n",
       "3     2.440268  14.047529     14.135652    0.000000          ...             \n",
       "4     2.602594  14.182841     14.182841    9.391827          ...             \n",
       "\n",
       "   SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0               0             0             0            1   \n",
       "1               0             0             0            1   \n",
       "2               0             0             0            1   \n",
       "3               0             0             0            1   \n",
       "4               0             0             0            1   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0                      0                      0                     0   \n",
       "1                      0                      0                     0   \n",
       "2                      0                      0                     0   \n",
       "3                      1                      0                     0   \n",
       "4                      0                      0                     0   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                     0                     1                      0  \n",
       "1                     0                     1                      0  \n",
       "2                     0                     1                      0  \n",
       "3                     0                     0                      0  \n",
       "4                     0                     1                      0  \n",
       "\n",
       "[5 rows x 259 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (1460, 259)\n",
      "Test: (1459, 259)\n"
     ]
    }
   ],
   "source": [
    "train = all_data[:ntrain]\n",
    "test = all_data[ntrain:]\n",
    "print(\"Train:\", train.shape)\n",
    "print(\"Test:\",test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (1460, 260)\n",
      "Test: (1459, 260)\n"
     ]
    }
   ],
   "source": [
    "train = pd.concat([all_ID[:ntrain], all_data[:ntrain]], axis = 1)\n",
    "test = pd.concat([all_ID[ntrain:], all_data[ntrain:]], axis = 1)\n",
    "print(\"Train:\", train.shape)\n",
    "print(\"Test:\",test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('data/train_cleaned.csv', index = False)\n",
    "y_train.to_csv('data/y_train.csv', index = False)\n",
    "test.to_csv('data/test_cleaned.csv', index = False)"
   ]
  }
 ],
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