housing-feature-engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pylab import *\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['total_SF'] = train[['TotalBsmtSF','1stFlrSF','2ndFlrSF']].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ed3f759588>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "train.loc[train['MoSold'].apply(lambda x: x in [12,1,2]),'Season'] = \"winter\"\n",
    "train.loc[train['MoSold'].apply(lambda x: x in [3,4,5]),'Season'] = \"spring\" \n",
    "train.loc[train['MoSold'].apply(lambda x: x in [6,7,8]),'Season'] = \"summer\"\n",
    "train.loc[train['MoSold'].apply(lambda x: x in [9,10,11]),'Season'] = \"fall\"\n",
    "\n",
    "sns.boxplot(x=train['OverallQual'], y=train['SalePrice'])\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(x=train['Season'], y=train['SalePrice'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}