Case Study - Feature Selection on Store Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "store = pd.read_csv('https://raw.githubusercontent.com/zekelabs/data-science-complete-tutorial/master/Data/store.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1115 entries, 0 to 1114\n",
      "Data columns (total 10 columns):\n",
      "Store                        1115 non-null int64\n",
      "StoreType                    1115 non-null object\n",
      "Assortment                   1115 non-null object\n",
      "CompetitionDistance          1112 non-null float64\n",
      "CompetitionOpenSinceMonth    761 non-null float64\n",
      "CompetitionOpenSinceYear     761 non-null float64\n",
      "Promo2                       1115 non-null int64\n",
      "Promo2SinceWeek              571 non-null float64\n",
      "Promo2SinceYear              571 non-null float64\n",
      "PromoInterval                571 non-null object\n",
      "dtypes: float64(5), int64(2), object(3)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "store.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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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>Store</th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1115.00000</td>\n",
       "      <td>1112.000000</td>\n",
       "      <td>761.000000</td>\n",
       "      <td>761.000000</td>\n",
       "      <td>1115.000000</td>\n",
       "      <td>571.000000</td>\n",
       "      <td>571.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>558.00000</td>\n",
       "      <td>5404.901079</td>\n",
       "      <td>7.224704</td>\n",
       "      <td>2008.668857</td>\n",
       "      <td>0.512108</td>\n",
       "      <td>23.595447</td>\n",
       "      <td>2011.763573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>322.01708</td>\n",
       "      <td>7663.174720</td>\n",
       "      <td>3.212348</td>\n",
       "      <td>6.195983</td>\n",
       "      <td>0.500078</td>\n",
       "      <td>14.141984</td>\n",
       "      <td>1.674935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>279.50000</td>\n",
       "      <td>717.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>558.00000</td>\n",
       "      <td>2325.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>836.50000</td>\n",
       "      <td>6882.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1115.00000</td>\n",
       "      <td>75860.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Store  CompetitionDistance  CompetitionOpenSinceMonth  \\\n",
       "count  1115.00000          1112.000000                 761.000000   \n",
       "mean    558.00000          5404.901079                   7.224704   \n",
       "std     322.01708          7663.174720                   3.212348   \n",
       "min       1.00000            20.000000                   1.000000   \n",
       "25%     279.50000           717.500000                   4.000000   \n",
       "50%     558.00000          2325.000000                   8.000000   \n",
       "75%     836.50000          6882.500000                  10.000000   \n",
       "max    1115.00000         75860.000000                  12.000000   \n",
       "\n",
       "       CompetitionOpenSinceYear       Promo2  Promo2SinceWeek  Promo2SinceYear  \n",
       "count                761.000000  1115.000000       571.000000       571.000000  \n",
       "mean                2008.668857     0.512108        23.595447      2011.763573  \n",
       "std                    6.195983     0.500078        14.141984         1.674935  \n",
       "min                 1900.000000     0.000000         1.000000      2009.000000  \n",
       "25%                 2006.000000     0.000000        13.000000      2011.000000  \n",
       "50%                 2010.000000     1.000000        22.000000      2012.000000  \n",
       "75%                 2013.000000     1.000000        37.000000      2013.000000  \n",
       "max                 2015.000000     1.000000        50.000000      2015.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = list(store.select_dtypes('object').columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = list(store.select_dtypes(include=['int64', 'float64']).columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['StoreType', 'Assortment', 'PromoInterval']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1115 entries, 0 to 1114\n",
      "Data columns (total 10 columns):\n",
      "Store                        1115 non-null int64\n",
      "StoreType                    1115 non-null object\n",
      "Assortment                   1115 non-null object\n",
      "CompetitionDistance          1112 non-null float64\n",
      "CompetitionOpenSinceMonth    761 non-null float64\n",
      "CompetitionOpenSinceYear     761 non-null float64\n",
      "Promo2                       1115 non-null int64\n",
      "Promo2SinceWeek              571 non-null float64\n",
      "Promo2SinceYear              571 non-null float64\n",
      "PromoInterval                571 non-null object\n",
      "dtypes: float64(5), int64(2), object(3)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "store.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "store.PromoInterval.fillna('Unknown',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1115 entries, 0 to 1114\n",
      "Data columns (total 10 columns):\n",
      "Store                        1115 non-null int64\n",
      "StoreType                    1115 non-null object\n",
      "Assortment                   1115 non-null object\n",
      "CompetitionDistance          1112 non-null float64\n",
      "CompetitionOpenSinceMonth    761 non-null float64\n",
      "CompetitionOpenSinceYear     761 non-null float64\n",
      "Promo2                       1115 non-null int64\n",
      "Promo2SinceWeek              571 non-null float64\n",
      "Promo2SinceYear              571 non-null float64\n",
      "PromoInterval                1115 non-null object\n",
      "dtypes: float64(5), int64(2), object(3)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "store.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Store</th>\n",
       "      <th>StoreType</th>\n",
       "      <th>Assortment</th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <th>PromoInterval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>a</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>570.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>620.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>29910.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store StoreType Assortment  CompetitionDistance  CompetitionOpenSinceMonth  \\\n",
       "0      1         c          a               1270.0                        9.0   \n",
       "1      2         a          a                570.0                       11.0   \n",
       "2      3         a          a              14130.0                       12.0   \n",
       "3      4         c          c                620.0                        9.0   \n",
       "4      5         a          a              29910.0                        4.0   \n",
       "\n",
       "   CompetitionOpenSinceYear  Promo2  Promo2SinceWeek  Promo2SinceYear  \\\n",
       "0                    2008.0       0              NaN              NaN   \n",
       "1                    2007.0       1             13.0           2010.0   \n",
       "2                    2006.0       1             14.0           2011.0   \n",
       "3                    2009.0       0              NaN              NaN   \n",
       "4                    2015.0       0              NaN              NaN   \n",
       "\n",
       "     PromoInterval  \n",
       "0          Unknown  \n",
       "1  Jan,Apr,Jul,Oct  \n",
       "2  Jan,Apr,Jul,Oct  \n",
       "3          Unknown  \n",
       "4          Unknown  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_cols:\n",
    "    le = LabelEncoder()\n",
    "    store[col] = le.fit_transform(store[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import f_regression, mutual_info_regression, SelectKBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_selector = SelectKBest(k=5, score_func=mutual_info_regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "data = imputer.fit_transform(store.drop('CompetitionDistance',axis=1))\n",
    "\n",
    "#feature_selector.fit_transform(store.drop('CompetitionDistance',axis=1), store.CompetitionDistance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "store.CompetitionDistance.fillna(method='ffill',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = feature_selector.fit_transform(data, store.CompetitionDistance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1115, 5)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(new_data, store.CompetitionDistance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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