Rossman.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('C:\\\\Users\\\\deeks\\\\Documents\\\\rossmann-store-sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31/07/2015</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>d</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>31/07/2015</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>d</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>31/07/2015</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>d</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>31/07/2015</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>d</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>31/07/2015</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>d</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek        Date  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "0      1          5  31/07/2015   5263        555     1      1            d   \n",
       "1      2          5  31/07/2015   6064        625     1      1            d   \n",
       "2      3          5  31/07/2015   8314        821     1      1            d   \n",
       "3      4          5  31/07/2015  13995       1498     1      1            d   \n",
       "4      5          5  31/07/2015   4822        559     1      1            d   \n",
       "\n",
       "   SchoolHoliday  \n",
       "0              1  \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.017209e+06</td>\n",
       "      <td>1.017209e+06</td>\n",
       "      <td>1017209</td>\n",
       "      <td>1.017209e+06</td>\n",
       "      <td>1.017209e+06</td>\n",
       "      <td>1.017209e+06</td>\n",
       "      <td>1.017209e+06</td>\n",
       "      <td>1017209</td>\n",
       "      <td>1.017209e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29/04/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>d</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>986159</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.584297e+02</td>\n",
       "      <td>3.998341e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.773819e+03</td>\n",
       "      <td>6.331459e+02</td>\n",
       "      <td>8.301067e-01</td>\n",
       "      <td>3.815145e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.786467e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.219087e+02</td>\n",
       "      <td>1.997391e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.849926e+03</td>\n",
       "      <td>4.644117e+02</td>\n",
       "      <td>3.755392e-01</td>\n",
       "      <td>4.857586e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.830564e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.800000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.727000e+03</td>\n",
       "      <td>4.050000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.580000e+02</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.744000e+03</td>\n",
       "      <td>6.090000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.380000e+02</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.856000e+03</td>\n",
       "      <td>8.370000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.115000e+03</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.155100e+04</td>\n",
       "      <td>7.388000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Store     DayOfWeek        Date         Sales     Customers  \\\n",
       "count   1.017209e+06  1.017209e+06     1017209  1.017209e+06  1.017209e+06   \n",
       "unique           NaN           NaN         942           NaN           NaN   \n",
       "top              NaN           NaN  29/04/2013           NaN           NaN   \n",
       "freq             NaN           NaN        1115           NaN           NaN   \n",
       "mean    5.584297e+02  3.998341e+00         NaN  5.773819e+03  6.331459e+02   \n",
       "std     3.219087e+02  1.997391e+00         NaN  3.849926e+03  4.644117e+02   \n",
       "min     1.000000e+00  1.000000e+00         NaN  0.000000e+00  0.000000e+00   \n",
       "25%     2.800000e+02  2.000000e+00         NaN  3.727000e+03  4.050000e+02   \n",
       "50%     5.580000e+02  4.000000e+00         NaN  5.744000e+03  6.090000e+02   \n",
       "75%     8.380000e+02  6.000000e+00         NaN  7.856000e+03  8.370000e+02   \n",
       "max     1.115000e+03  7.000000e+00         NaN  4.155100e+04  7.388000e+03   \n",
       "\n",
       "                Open         Promo StateHoliday  SchoolHoliday  \n",
       "count   1.017209e+06  1.017209e+06      1017209   1.017209e+06  \n",
       "unique           NaN           NaN            4            NaN  \n",
       "top              NaN           NaN            d            NaN  \n",
       "freq             NaN           NaN       986159            NaN  \n",
       "mean    8.301067e-01  3.815145e-01          NaN   1.786467e-01  \n",
       "std     3.755392e-01  4.857586e-01          NaN   3.830564e-01  \n",
       "min     0.000000e+00  0.000000e+00          NaN   0.000000e+00  \n",
       "25%     1.000000e+00  0.000000e+00          NaN   0.000000e+00  \n",
       "50%     1.000000e+00  0.000000e+00          NaN   0.000000e+00  \n",
       "75%     1.000000e+00  1.000000e+00          NaN   0.000000e+00  \n",
       "max     1.000000e+00  1.000000e+00          NaN   1.000000e+00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store            0\n",
       "DayOfWeek        0\n",
       "Date             0\n",
       "Sales            0\n",
       "Customers        0\n",
       "Open             0\n",
       "Promo            0\n",
       "StateHoliday     0\n",
       "SchoolHoliday    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1017209, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    986159\n",
       "a     20260\n",
       "b      6690\n",
       "c      4100\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(7, sharex=True, sharey=True)\n",
    "for i in range(1, 8):\n",
    "    mask = train[train['DayOfWeek'] == i]\n",
    "    ax[i - 1].set_title(\"Day {0}\".format(i))\n",
    "    ax[i - 1].scatter(mask['Customers'], mask['Sales'], label=i)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Customers')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting data where stores were open\n",
    "sales_when_store_open = train[train['Open'] == 1]\n",
    "\n",
    "# Average sales by each Storeb\n",
    "mean_sales = sales_when_store_open['Sales'].groupby(sales_when_store_open.Store).aggregate(np.mean)\n",
    "\n",
    "# Average Customers per day in each store\n",
    "customers_per_day = train['Customers'].groupby(train.DayOfWeek).aggregate(np.mean)\n",
    "\n",
    "# Average Sales per day in each store\n",
    "sales_per_day = train['Sales'].groupby(train.DayOfWeek).aggregate(np.mean)\n",
    "\n",
    "# Average Sales per day in a week with respect to 'Open' stores\n",
    "normalized_sales_per_day = sales_when_store_open['Sales'].groupby(sales_when_store_open.DayOfWeek).aggregate(np.mean)\n",
    "\n",
    "# Average Count of customers per day with respect to 'Open' stores\n",
    "normalized_customer_count = sales_when_store_open['Customers'].groupby(sales_when_store_open.DayOfWeek).aggregate(np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Sales and customers per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "\n",
    "# Plot\n",
    "x_axes = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "plt.xticks([1,2,3,4,5,6,7], x_axes)\n",
    "\n",
    "#Renaming names of series\n",
    "customers_per_day.name = 'Avg_Customers_Count'\n",
    "sales_per_day.name = 'Weekly_Avg_Sales'\n",
    "normalized_sales_per_day.name = 'Weekly_Avg_Sales_Only_For_Open_Stores'\n",
    "normalized_customer_count.name = 'Avg_Customers_Count_Only_For_Open_Stores'\n",
    "\n",
    "customers_per_day.plot(kind='line', legend=True)\n",
    "sales_per_day.plot(kind='line', legend=True)\n",
    "normalized_sales_per_day.plot(kind='line', legend=True)\n",
    "normalized_customer_count.plot(kind='line', legend=True)\n",
    "\n",
    "plt.savefig('eda_1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Sales by with/without promos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "sales_by_promo = sales_when_store_open['Sales'].groupby(sales_when_store_open.Promo).aggregate(np.mean)\n",
    "cust_by_promo = sales_when_store_open['Customers'].groupby(sales_when_store_open.Promo).aggregate(np.mean)\n",
    "sales_by_promo.plot(kind='bar', label='mean sales')\n",
    "plt.legend()\n",
    "plt.savefig('eda_train_1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Customers by with/without promos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "cust_by_promo.plot(kind='bar', label='average customers')\n",
    "plt.legend()\n",
    "plt.savefig('eda_train_2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,6))\n",
    "for i in [0, 1]:\n",
    "    data = train[train['SchoolHoliday'] == i]\n",
    "    if (len(data) == 0):\n",
    "        continue\n",
    "    plt.scatter(data['Customers'], data['Sales'], label=i)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Customers')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()\n",
    "plt.savefig('eda_train_3.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It appears that school holiday doesn't seem to have much of an impact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "for i in [0, 1]:\n",
    "    data = train[train['Promo'] == i]\n",
    "    if (len(data) == 0):\n",
    "        continue\n",
    "    plt.scatter(data['Customers'], data['Sales'], label=i)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Customers')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()\n",
    "plt.savefig('eda_train_3.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It appears that promos may have some different effects for two groups of stores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "store = pd.read_csv(\"store.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>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>NaN</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>NaN</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>NaN</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              NaN  \n",
       "1  Jan,Apr,Jul,Oct  \n",
       "2  Jan,Apr,Jul,Oct  \n",
       "3              NaN  \n",
       "4              NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>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>count</th>\n",
       "      <td>1115.00000</td>\n",
       "      <td>1115</td>\n",
       "      <td>1115</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",
       "      <td>571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>602</td>\n",
       "      <td>593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>558.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>322.01708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>279.50000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>558.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>836.50000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1115.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Store StoreType Assortment  CompetitionDistance  \\\n",
       "count   1115.00000      1115       1115          1112.000000   \n",
       "unique         NaN         4          3                  NaN   \n",
       "top            NaN         a          a                  NaN   \n",
       "freq           NaN       602        593                  NaN   \n",
       "mean     558.00000       NaN        NaN          5404.901079   \n",
       "std      322.01708       NaN        NaN          7663.174720   \n",
       "min        1.00000       NaN        NaN            20.000000   \n",
       "25%      279.50000       NaN        NaN           717.500000   \n",
       "50%      558.00000       NaN        NaN          2325.000000   \n",
       "75%      836.50000       NaN        NaN          6882.500000   \n",
       "max     1115.00000       NaN        NaN         75860.000000   \n",
       "\n",
       "        CompetitionOpenSinceMonth  CompetitionOpenSinceYear       Promo2  \\\n",
       "count                  761.000000                761.000000  1115.000000   \n",
       "unique                        NaN                       NaN          NaN   \n",
       "top                           NaN                       NaN          NaN   \n",
       "freq                          NaN                       NaN          NaN   \n",
       "mean                     7.224704               2008.668857     0.512108   \n",
       "std                      3.212348                  6.195983     0.500078   \n",
       "min                      1.000000               1900.000000     0.000000   \n",
       "25%                      4.000000               2006.000000     0.000000   \n",
       "50%                      8.000000               2010.000000     1.000000   \n",
       "75%                     10.000000               2013.000000     1.000000   \n",
       "max                     12.000000               2015.000000     1.000000   \n",
       "\n",
       "        Promo2SinceWeek  Promo2SinceYear    PromoInterval  \n",
       "count        571.000000       571.000000              571  \n",
       "unique              NaN              NaN                3  \n",
       "top                 NaN              NaN  Jan,Apr,Jul,Oct  \n",
       "freq                NaN              NaN              335  \n",
       "mean          23.595447      2011.763573              NaN  \n",
       "std           14.141984         1.674935              NaN  \n",
       "min            1.000000      2009.000000              NaN  \n",
       "25%           13.000000      2011.000000              NaN  \n",
       "50%           22.000000      2012.000000              NaN  \n",
       "75%           37.000000      2013.000000              NaN  \n",
       "max           50.000000      2015.000000              NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distributin of Store Competition Distance')"
      ]
     },
     "execution_count": 19,
     "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": [
    "# Distribution of competition distance\n",
    "sns.distplot(store.CompetitionDistance.dropna())\n",
    "plt.title(\"Distributin of Store Competition Distance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['SalesPerCustomer'] = train['Sales'] / train['Customers']\n",
    "\n",
    "avg_store = train.groupby('Store')[['Sales', 'Customers', 'SalesPerCustomer']].mean()\n",
    "avg_store.rename(columns=lambda x: 'Avg' + x, inplace=True)\n",
    "store_eda = pd.merge(avg_store.reset_index(), store, on='Store')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "for i in ['a', 'b', 'c', 'd']:\n",
    "    data = store_eda[store_eda['StoreType'] == i]\n",
    "    if (len(data) == 0):\n",
    "        continue\n",
    "    plt.scatter(data['AvgCustomers'], data['AvgSales'], label=i)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Average Customers')\n",
    "plt.ylabel('Average Sales')\n",
    "plt.show()\n",
    "plt.savefig('eda_store_1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From above plot we are able to tell that certain store types' customers spend less, like 'b'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "store[\"CompetitionDistance\"].fillna(-1)\n",
    "plt.scatter(store_eda['CompetitionDistance'], store_eda['AvgSales'])\n",
    "\n",
    "plt.xlabel('CompetitionDistance')\n",
    "plt.ylabel('Average Sales')\n",
    "plt.show()\n",
    "plt.savefig('eda_store_2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Who knew? Closer competition means more profits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                          0\n",
       "StoreType                      0\n",
       "Assortment                     0\n",
       "CompetitionDistance            3\n",
       "CompetitionOpenSinceMonth    354\n",
       "CompetitionOpenSinceYear     354\n",
       "Promo2                         0\n",
       "Promo2SinceWeek              544\n",
       "Promo2SinceYear              544\n",
       "PromoInterval                544\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_level_unique = store['PromoInterval'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_level_mapping = {PromoInterval: idx for idx, PromoInterval in enumerate(categorical_level_unique, 1)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{nan: 1, 'Jan,Apr,Jul,Oct': 2, 'Feb,May,Aug,Nov': 3, 'Mar,Jun,Sept,Dec': 4}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_level_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "store['PromoInterval'] = store['PromoInterval'].map(categorical_level_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "store['PromoInterval'] = store['PromoInterval'].replace(1, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>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>NaN</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>2.0</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>2.0</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>NaN</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>NaN</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            NaN  \n",
       "1            2.0  \n",
       "2            2.0  \n",
       "3            NaN  \n",
       "4            NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                          0\n",
       "StoreType                      0\n",
       "Assortment                     0\n",
       "CompetitionDistance            3\n",
       "CompetitionOpenSinceMonth    354\n",
       "CompetitionOpenSinceYear     354\n",
       "Promo2                         0\n",
       "Promo2SinceWeek              544\n",
       "Promo2SinceYear              544\n",
       "PromoInterval                544\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "store['PromoInterval'] = store['PromoInterval'].fillna(store['PromoInterval'].mode().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                          0\n",
       "StoreType                      0\n",
       "Assortment                     0\n",
       "CompetitionDistance            3\n",
       "CompetitionOpenSinceMonth    354\n",
       "CompetitionOpenSinceYear     354\n",
       "Promo2                         0\n",
       "Promo2SinceWeek              544\n",
       "Promo2SinceYear              544\n",
       "PromoInterval                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "store['CompetitionDistance'].fillna(store['CompetitionDistance'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(train, store, how = 'left' , on = 'Store')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_test = pd.read_csv('test.csv')\n",
    "raw_test['Open'].fillna(raw_test['Open'].median(), inplace = True)\n",
    "test = pd.merge(raw_test, store, how = 'left' , on = 'Store')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                               0\n",
       "Store                            0\n",
       "DayOfWeek                        0\n",
       "Date                             0\n",
       "Open                             0\n",
       "Promo                            0\n",
       "StateHoliday                     0\n",
       "SchoolHoliday                    0\n",
       "StoreType                        0\n",
       "Assortment                       0\n",
       "CompetitionDistance              0\n",
       "CompetitionOpenSinceMonth    15216\n",
       "CompetitionOpenSinceYear     15216\n",
       "Promo2                           0\n",
       "Promo2SinceWeek              17232\n",
       "Promo2SinceYear              17232\n",
       "PromoInterval                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>SalesPerCustomer</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",
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       "      <th>0</th>\n",
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       "      <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",
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       "      <td>9.702400</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>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>8314</td>\n",
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       "      <td>10.126675</td>\n",
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       "      <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>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>31/07/2015</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>d</td>\n",
       "      <td>1</td>\n",
       "      <td>9.342457</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>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>31/07/2015</td>\n",
       "      <td>4822</td>\n",
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       "      <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>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek        Date  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "0      1          5  31/07/2015   5263        555     1      1            d   \n",
       "1      2          5  31/07/2015   6064        625     1      1            d   \n",
       "2      3          5  31/07/2015   8314        821     1      1            d   \n",
       "3      4          5  31/07/2015  13995       1498     1      1            d   \n",
       "4      5          5  31/07/2015   4822        559     1      1            d   \n",
       "\n",
       "   SchoolHoliday  SalesPerCustomer StoreType Assortment  CompetitionDistance  \\\n",
       "0              1          9.482883         c          a               1270.0   \n",
       "1              1          9.702400         a          a                570.0   \n",
       "2              1         10.126675         a          a              14130.0   \n",
       "3              1          9.342457         c          c                620.0   \n",
       "4              1          8.626118         a          a              29910.0   \n",
       "\n",
       "   CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
       "0                        9.0                    2008.0       0   \n",
       "1                       11.0                    2007.0       1   \n",
       "2                       12.0                    2006.0       1   \n",
       "3                        9.0                    2009.0       0   \n",
       "4                        4.0                    2015.0       0   \n",
       "\n",
       "   Promo2SinceWeek  Promo2SinceYear  PromoInterval  \n",
       "0              NaN              NaN            2.0  \n",
       "1             13.0           2010.0            2.0  \n",
       "2             14.0           2011.0            2.0  \n",
       "3              NaN              NaN            2.0  \n",
       "4              NaN              NaN            2.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of \"train\" df without merging: (1017209, 10)\n",
      "size of \"store\" df without merging: (1115, 10)\n",
      "size of \"final\" df after merging: (1017209, 19)\n",
      "size of \"test\" df after merging: (41088, 17)\n"
     ]
    }
   ],
   "source": [
    "print(f'size of \"train\" df without merging: {train.shape}')\n",
    "print(f'size of \"store\" df without merging: {store.shape}')\n",
    "print(f'size of \"final\" df after merging: {data.shape}')\n",
    "print(f'size of \"test\" df after merging: {test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CompetitionOpenSinceYear</th>\n",
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       "      <th>Promo2SinceYear</th>\n",
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       "      <td>2006.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>31/07/2015</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>d</td>\n",
       "      <td>1</td>\n",
       "      <td>9.342457</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>2.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>2015.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek        Date  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "0      1          5  31/07/2015   5263        555     1      1            d   \n",
       "1      2          5  31/07/2015   6064        625     1      1            d   \n",
       "2      3          5  31/07/2015   8314        821     1      1            d   \n",
       "3      4          5  31/07/2015  13995       1498     1      1            d   \n",
       "4      5          5  31/07/2015   4822        559     1      1            d   \n",
       "\n",
       "   SchoolHoliday  SalesPerCustomer StoreType Assortment  CompetitionDistance  \\\n",
       "0              1          9.482883         c          a               1270.0   \n",
       "1              1          9.702400         a          a                570.0   \n",
       "2              1         10.126675         a          a              14130.0   \n",
       "3              1          9.342457         c          c                620.0   \n",
       "4              1          8.626118         a          a              29910.0   \n",
       "\n",
       "   CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
       "0                        9.0                    2008.0       0   \n",
       "1                       11.0                    2007.0       1   \n",
       "2                       12.0                    2006.0       1   \n",
       "3                        9.0                    2009.0       0   \n",
       "4                        4.0                    2015.0       0   \n",
       "\n",
       "   Promo2SinceWeek  Promo2SinceYear  PromoInterval  \n",
       "0              NaN              NaN            2.0  \n",
       "1             13.0           2010.0            2.0  \n",
       "2             14.0           2011.0            2.0  \n",
       "3              NaN              NaN            2.0  \n",
       "4              NaN              NaN            2.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_engg(data):\n",
    "    mappings = {'0':\"0\", 'a':\"1\", 'b':\"2\", 'c':\"3\", 'd':\"4\"}\n",
    "    mappings1 = {'0':\"0\", 'Jan,Apr,Jul,Oct':\"1\", 'Feb,May,Aug,Nov':\"2\", 'Mar,Jun,Sept,Dec':\"3\"}\n",
    "    data[\"StoreType\"].replace(mappings, inplace=True)\n",
    "    data.Assortment.replace(mappings, inplace=True)\n",
    "    data.StateHoliday.replace(mappings, inplace=True)\n",
    "\n",
    "    data['Year'] = pd.DatetimeIndex(data['Date']).year\n",
    "    data['Month'] = pd.DatetimeIndex(data['Date']).month\n",
    "    data['Day'] = pd.DatetimeIndex(data['Date']).day\n",
    "    data['WeekOfYear'] = pd.DatetimeIndex(data['Date']).weekofyear\n",
    "    data['WeekDay_name'] = pd.DatetimeIndex(data['Date']).weekday_name\n",
    "    mappings2 = {'Sunday':\"0\", 'Monday':\"1\", 'Tuesday':\"2\", 'Wednesday':\"3\", 'Thursday':\"4\", 'Friday':\"5\", 'Saturday':\"6\"}\n",
    "    data[\"WeekDay_name\"].replace(mappings2, inplace=True)\n",
    "    data['CompetitionOpen'] = 12 * (data.Year - data.CompetitionOpenSinceYear) + \\\n",
    "        (data.Month - data.CompetitionOpenSinceMonth)\n",
    "    data['CompetitionOpen'] = data.CompetitionOpen.apply(lambda x: x if x > 0 else 0)\n",
    "    data['PrvsHoliday'] = np.where((data[\"StateHoliday\"].shift(1)==1) | (data[\"SchoolHoliday\"].shift(1)==1),1,0)\n",
    "    data['NxtHoliday'] = np.where((data[\"StateHoliday\"].shift(-1)==1) | (data[\"SchoolHoliday\"].shift(-1)==1),1,0)\n",
    "    data['PromoOpen'] = 12 * (data.Year - data.Promo2SinceYear) + \\\n",
    "        (data.WeekOfYear - data.Promo2SinceWeek) / 4.0\n",
    "    data['PromoOpen'] = data[\"PromoOpen\"].apply(lambda x: x if x > 0 else 0)\n",
    "    data.loc[data.Promo2SinceYear == 0, 'PromoOpen'] = 0\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_1 = feature_engg(data)\n",
    "test = feature_engg(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                          int64\n",
       "DayOfWeek                      int64\n",
       "Date                          object\n",
       "Sales                          int64\n",
       "Customers                      int64\n",
       "Open                           int64\n",
       "Promo                          int64\n",
       "StateHoliday                  object\n",
       "SchoolHoliday                  int64\n",
       "SalesPerCustomer             float64\n",
       "StoreType                     object\n",
       "Assortment                    object\n",
       "CompetitionDistance          float64\n",
       "CompetitionOpenSinceMonth    float64\n",
       "CompetitionOpenSinceYear     float64\n",
       "Promo2                         int64\n",
       "Promo2SinceWeek              float64\n",
       "Promo2SinceYear              float64\n",
       "PromoInterval                float64\n",
       "Year                           int64\n",
       "Month                          int64\n",
       "Day                            int64\n",
       "WeekOfYear                     int64\n",
       "WeekDay_name                  object\n",
       "CompetitionOpen              float64\n",
       "PrvsHoliday                    int32\n",
       "NxtHoliday                     int32\n",
       "PromoOpen                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def type_conv(df):\n",
    "    df['Assortment'] = df['Assortment'].astype('category')\n",
    "    df['StoreType'] = df['StoreType'].astype('category')\n",
    "    df['PromoInterval']= df['Promo2'].astype('category')\n",
    "    df['WeekDay_name']= df['WeekDay_name'].astype('int32')\n",
    "    df['StateHoliday']= df['StateHoliday'].astype('int32')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_1 = type_conv(train_1)\n",
    "test = type_conv(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trend Lines on 'Sales' w.r.t 'Year' & 'Month'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = sns.color_palette()\n",
    "temp=train_1.groupby('Year')['Sales'].sum()\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.pointplot(temp.index,temp.values, alpha=0.8, color=color[1],)\n",
    "plt.ylabel('Overall Sales', fontsize=12)\n",
    "plt.xlabel('year', fontsize=12)\n",
    "plt.title('Sales for every year ', fontsize=15)\n",
    "plt.xticks(rotation='vertical')\n",
    "\n",
    "# month over month sales\n",
    "temp=train_1.groupby(['Year','Month'])['Sales'].sum()\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.pointplot(temp.index,temp.values, alpha=0.8, color=color[2],)\n",
    "plt.ylabel('Overall Sales', fontsize=12)\n",
    "plt.xlabel('Month', fontsize=12)\n",
    "plt.title('Monthly sales variation', fontsize=15)\n",
    "plt.xticks(rotation='vertical')\n",
    "\n",
    "plt.show()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                           int64\n",
       "DayOfWeek                       int64\n",
       "Date                           object\n",
       "Sales                           int64\n",
       "Customers                       int64\n",
       "Open                            int64\n",
       "Promo                           int64\n",
       "StateHoliday                    int32\n",
       "SchoolHoliday                   int64\n",
       "SalesPerCustomer              float64\n",
       "StoreType                    category\n",
       "Assortment                   category\n",
       "CompetitionDistance           float64\n",
       "CompetitionOpenSinceMonth     float64\n",
       "CompetitionOpenSinceYear      float64\n",
       "Promo2                          int64\n",
       "Promo2SinceWeek               float64\n",
       "Promo2SinceYear               float64\n",
       "PromoInterval                category\n",
       "Year                            int64\n",
       "Month                           int64\n",
       "Day                             int64\n",
       "WeekOfYear                      int64\n",
       "WeekDay_name                    int32\n",
       "CompetitionOpen               float64\n",
       "PrvsHoliday                     int32\n",
       "NxtHoliday                      int32\n",
       "PromoOpen                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_1 = train_1.drop([\"CompetitionOpenSinceMonth\",\"CompetitionOpenSinceYear\",\"Customers\",\"Promo2SinceWeek\",\"Promo2SinceYear\",\"Date\",\"Open\",\"SalesPerCustomer\"],axis=1)\n",
    "test = test.drop([\"CompetitionOpenSinceMonth\",\"CompetitionOpenSinceYear\",\"Promo2SinceWeek\",\"Promo2SinceYear\",\"Date\",\"Id\",\"Open\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of train (1017209, 20)\n",
      "shape of test (41088, 19)\n"
     ]
    }
   ],
   "source": [
    "print(f'shape of train {train_1.shape}')\n",
    "print(f'shape of test {test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(train_1[[\"StoreType\",\"Assortment\",\n",
    "                          \"PromoInterval\"]],prefix_sep=\"_\",drop_first=True)\n",
    "df1 = pd.get_dummies(test[[\"StoreType\",\"Assortment\",\n",
    "                          \"PromoInterval\"]],prefix_sep=\"_\",drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_1 = pd.concat([train_1,df],axis=1)\n",
    "test = pd.concat([test,df1],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_1 = train_1.drop([\"StoreType\",\"Assortment\",\"PromoInterval\"],axis=1)\n",
    "test = test.drop([\"StoreType\",\"Assortment\",\"PromoInterval\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final shape of train (1017209, 23)\n",
      "Final shape of test (41088, 22)\n"
     ]
    }
   ],
   "source": [
    "print(f'Final shape of train {train_1.shape}')\n",
    "print(f'Final shape of test {test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_1.drop(['Sales'],axis = 1)\n",
    "y = np.log(train_1['Sales'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                    int64\n",
       "DayOfWeek                int64\n",
       "Promo                    int64\n",
       "StateHoliday             int32\n",
       "SchoolHoliday            int64\n",
       "CompetitionDistance    float64\n",
       "Promo2                   int64\n",
       "Year                     int64\n",
       "Month                    int64\n",
       "Day                      int64\n",
       "WeekOfYear               int64\n",
       "WeekDay_name             int32\n",
       "CompetitionOpen        float64\n",
       "PrvsHoliday              int32\n",
       "NxtHoliday               int32\n",
       "PromoOpen              float64\n",
       "StoreType_2              uint8\n",
       "StoreType_3              uint8\n",
       "StoreType_4              uint8\n",
       "Assortment_2             uint8\n",
       "Assortment_3             uint8\n",
       "PromoInterval_1          uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                  0\n",
       "DayOfWeek              0\n",
       "Promo                  0\n",
       "StateHoliday           0\n",
       "SchoolHoliday          0\n",
       "CompetitionDistance    0\n",
       "Promo2                 0\n",
       "Year                   0\n",
       "Month                  0\n",
       "Day                    0\n",
       "WeekOfYear             0\n",
       "WeekDay_name           0\n",
       "CompetitionOpen        0\n",
       "PrvsHoliday            0\n",
       "NxtHoliday             0\n",
       "PromoOpen              0\n",
       "StoreType_2            0\n",
       "StoreType_3            0\n",
       "StoreType_4            0\n",
       "Assortment_2           0\n",
       "Assortment_3           0\n",
       "PromoInterval_1        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "test_scale = scaler.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_eval, y_train, y_eval = train_test_split(X,y, test_size = 0.3, random_state = 126)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODEL BUILDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)\n",
    "y_pred_linear = lin_reg.predict(X_eval)\n",
    "train_pred = lin_reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for train set with linear regression: 2.004095701657737\n",
      "RMSE for validation with linear regression: 1.9988355179153638\n",
      "R-square for validation in linear regression: 0.6348962099922618\n",
      "R-square for train set in linear regression: 0.6342322751875119\n"
     ]
    }
   ],
   "source": [
    "print(f'RMSE for train set with linear regression: {np.sqrt(mean_squared_error(y_train , train_pred))}')\n",
    "print(f'RMSE for validation with linear regression: {np.sqrt(mean_squared_error(y_eval , y_pred_linear))}')\n",
    "print(f'R-square for validation in linear regression: {r2_score(y_eval, y_pred_linear)}')\n",
    "print(f'R-square for train set in linear regression: {r2_score(y_train, train_pred)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "# The below parameters are found by doing Grid Search on it\n",
    "dt = DecisionTreeRegressor(criterion='mse',max_depth=2,max_leaf_nodes=2,min_samples_leaf=2,min_samples_split=2)\n",
    "dt.fit(X_train , y_train)\n",
    "y_pred_dt = dt.predict(X_eval)\n",
    "train_pred_dt = dt.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-square for train with tree: 0.7515548349088375\n",
      "R-square for validation with tree: 0.7538006809291129\n",
      "MSE for train with tree: 1.6516995135774253\n",
      "MSE for validation with tree: 1.6413919950070008\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score , mean_squared_error\n",
    "# R-square\n",
    "print(f'R-square for train with tree: {r2_score(y_train , train_pred_dt)}')\n",
    "print(f'R-square for validation with tree: {r2_score(y_eval , y_pred_dt)}')\n",
    "# MSE\n",
    "print(f'MSE for train with tree: {np.sqrt(mean_squared_error(y_train , train_pred_dt))}')\n",
    "print(f'MSE for validation with tree: {np.sqrt(mean_squared_error(y_eval , y_pred_dt))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245099    9.468619\n",
       "983910    8.591187\n",
       "795554    8.759982\n",
       "946283    9.073948\n",
       "358057    8.838407\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras import optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "Epoch 1/15\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\deeks\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "712046/712046 [==============================] - 10s 14us/step - loss: 23.1855 - mean_squared_error: 23.1855 - mean_absolute_error: 3.9622 - mean_absolute_percentage_error: 953037297.1740\n",
      "Epoch 2/15\n",
      "712046/712046 [==============================] - 10s 14us/step - loss: 13.4652 - mean_squared_error: 13.4652 - mean_absolute_error: 2.9053 - mean_absolute_percentage_error: 1173588675.4592\n",
      "Epoch 3/15\n",
      "712046/712046 [==============================] - 9s 12us/step - loss: 12.9976 - mean_squared_error: 12.9976 - mean_absolute_error: 2.8276 - mean_absolute_percentage_error: 1177915310.7537 6s - loss: 13.0781 - mean_squared_\n",
      "Epoch 4/15\n",
      "712046/712046 [==============================] - 8s 11us/step - loss: 12.7251 - mean_squared_error: 12.7251 - mean_absolute_error: 2.7828 - mean_absolute_percentage_error: 1177205314.0290\n",
      "Epoch 5/15\n",
      "712046/712046 [==============================] - 8s 11us/step - loss: 12.4292 - mean_squared_error: 12.4292 - mean_absolute_error: 2.7413 - mean_absolute_percentage_error: 1169326435.8997\n",
      "Epoch 6/15\n",
      "712046/712046 [==============================] - 10s 14us/step - loss: 12.1589 - mean_squared_error: 12.1589 - mean_absolute_error: 2.7286 - mean_absolute_percentage_error: 1140223995.6984\n",
      "Epoch 7/15\n",
      "712046/712046 [==============================] - 8s 12us/step - loss: 11.2673 - mean_squared_error: 11.2673 - mean_absolute_error: 2.6373 - mean_absolute_percentage_error: 1082553908.1577\n",
      "Epoch 8/15\n",
      "712046/712046 [==============================] - 8s 12us/step - loss: 11.0107 - mean_squared_error: 11.0107 - mean_absolute_error: 2.6476 - mean_absolute_percentage_error: 1026258886.7859\n",
      "Epoch 9/15\n",
      "712046/712046 [==============================] - 8s 12us/step - loss: 11.0554 - mean_squared_error: 11.0554 - mean_absolute_error: 2.6585 - mean_absolute_percentage_error: 1009981553.2874\n",
      "Epoch 10/15\n",
      "712046/712046 [==============================] - 8s 11us/step - loss: 10.8214 - mean_squared_error: 10.8214 - mean_absolute_error: 2.6107 - mean_absolute_percentage_error: 1020745665.8174\n",
      "Epoch 11/15\n",
      "712046/712046 [==============================] - 8s 12us/step - loss: 9.8599 - mean_squared_error: 9.8599 - mean_absolute_error: 2.4973 - mean_absolute_percentage_error: 955763212.4042\n",
      "Epoch 12/15\n",
      "712046/712046 [==============================] - 8s 11us/step - loss: 9.5279 - mean_squared_error: 9.5279 - mean_absolute_error: 2.4845 - mean_absolute_percentage_error: 871053231.6102\n",
      "Epoch 13/15\n",
      "712046/712046 [==============================] - 8s 12us/step - loss: 8.5915 - mean_squared_error: 8.5915 - mean_absolute_error: 2.3355 - mean_absolute_percentage_error: 831591944.0671\n",
      "Epoch 14/15\n",
      "712046/712046 [==============================] - 8s 11us/step - loss: 8.0519 - mean_squared_error: 8.0519 - mean_absolute_error: 2.2581 - mean_absolute_percentage_error: 764997999.4646\n",
      "Epoch 15/15\n",
      "712046/712046 [==============================] - 8s 12us/step - loss: 7.2709 - mean_squared_error: 7.2709 - mean_absolute_error: 2.1038 - mean_absolute_percentage_error: 707324942.9366\n"
     ]
    }
   ],
   "source": [
    "# Initialising the ANN\n",
    "model = Sequential()    \n",
    "model.add(Dense(units = 60, kernel_initializer = 'uniform', activation = 'relu', input_dim = X_train.shape[1]))\n",
    "model.add(Dropout(p = 0.3))\n",
    "model.add(Dense(units = 90, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dropout(p = 0.3))\n",
    "model.add(Dense(units = 60, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dropout(p = 0.3))\n",
    "model.add(Dense(units = 90, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dropout(p = 0.3))\n",
    "model.add(Dense(units = 60, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dropout(p = 0.3))\n",
    "model.add(Dense(units = 1, kernel_initializer = 'uniform', activation='linear'))\n",
    "model.compile(loss='mse', optimizer='adam', metrics=['mse', 'mae', 'mape'])\n",
    "history = model.fit(X_train, y_train, batch_size = 10000, epochs = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-square of train set: 0.4422078959462322\n",
      "R-square of validation set: 0.4446915230597891\n",
      "Mean Square Error for train set: 6.124968993313904\n",
      "Mean Square Error for validation set: 6.0767599088221695\n",
      "Mean Absolute Error for train: 1.8905565433381966\n",
      "Mean Absolute Error for test: 1.8843139665208686\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_eval)\n",
    "train_pred_nn = model.predict(X_train)\n",
    "# R-Square\n",
    "print(f'R-square of train set: {r2_score(y_train, train_pred_nn)}')\n",
    "print(f'R-square of validation set: {r2_score(y_eval, y_pred)}')\n",
    "# MSE\n",
    "print(f'Mean Square Error for train set: {mean_squared_error(y_train, train_pred_nn)}')\n",
    "print(f'Mean Square Error for validation set: {mean_squared_error(y_eval, y_pred)}')\n",
    "# MAE\n",
    "print(f'Mean Absolute Error for train: {mean_absolute_error(y_train, train_pred_nn)}')\n",
    "print(f'Mean Absolute Error for test: {mean_absolute_error(y_eval, y_pred)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15cad638a20>]"
      ]
     },
     "execution_count": 66,
     "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": [
    "plt.plot(history.history['mean_squared_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15cad126eb8>]"
      ]
     },
     "execution_count": 67,
     "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": [
    "plt.plot(history.history['mean_absolute_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}