eda-and-forecasting-with-rfregressor-final-updated.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "8988aa2f80766a1139663963fcbe879a7edc095d"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "#Data Manipulation and Treatment\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "#Plotting and Visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import itertools\n",
    "#Scikit-Learn for Modeling\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "14ce3406bcdc3ce041fc0c4af9921313a8aed14d"
   },
   "outputs": [],
   "source": [
    "def str_to_date(date):\n",
    "    return datetime.strptime(date, '%Y-%m-%d').date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "b9153ff6044f4b498a3a7d1f7eade84afb5a59bc"
   },
   "outputs": [],
   "source": [
    "#The training Set\n",
    "df_train = pd.read_csv(\"train.csv\",sep=',', parse_dates=['Date'],\n",
    "                       low_memory = False)\n",
    "\n",
    "\n",
    "#Additional Information on those stores \n",
    "df_store = pd.read_csv(\"store.csv\"\n",
    "                       , low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "b64527be2715686ee6f33921b29057e229d8de9f"
   },
   "outputs": [
    {
     "data": {
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       "\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>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>2018-07-31</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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 2018-07-31   5263        555     1      1            0   \n",
       "1      2          5 2018-07-31   6064        625     1      1            0   \n",
       "2      3          5 2018-07-31   8314        821     1      1            0   \n",
       "3      4          5 2018-07-31  13995       1498     1      1            0   \n",
       "4      5          5 2018-07-31   4822        559     1      1            0   \n",
       "\n",
       "   SchoolHoliday  \n",
       "0              1  \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "a53247c57d9c6f1ff773011d99f59b0f7a47b844"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>1017204</th>\n",
       "      <td>1111</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017205</th>\n",
       "      <td>1112</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017206</th>\n",
       "      <td>1113</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017207</th>\n",
       "      <td>1114</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017208</th>\n",
       "      <td>1115</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Store  DayOfWeek       Date  Sales  Customers  Open  Promo  \\\n",
       "1017204   1111          2 2016-01-01      0          0     0      0   \n",
       "1017205   1112          2 2016-01-01      0          0     0      0   \n",
       "1017206   1113          2 2016-01-01      0          0     0      0   \n",
       "1017207   1114          2 2016-01-01      0          0     0      0   \n",
       "1017208   1115          2 2016-01-01      0          0     0      0   \n",
       "\n",
       "        StateHoliday  SchoolHoliday  \n",
       "1017204            a              1  \n",
       "1017205            a              1  \n",
       "1017206            a              1  \n",
       "1017207            a              1  \n",
       "1017208            a              1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "73a7212baa88c18ff910b69b123bec4b43d18c27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Train dataset has 1017209 Rows and 9 Variables\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Store                     int64\n",
       " DayOfWeek                 int64\n",
       " Date             datetime64[ns]\n",
       " Sales                     int64\n",
       " Customers                 int64\n",
       " Open                      int64\n",
       " Promo                     int64\n",
       " StateHoliday             object\n",
       " SchoolHoliday             int64\n",
       " dtype: object, None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.dtypes,print (\"The Train dataset has {} Rows and {} Variables\".format(str(df_train.shape[0]),str(df_train.shape[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "a9c344574cacdc1f2643f6261cdfc4c98a781b79"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "873c481b50c5ce329a3eb130a2901d20ae17acd5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>1110</th>\n",
       "      <td>1111</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>1112</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>1113</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>9260.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>1114</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>1115</td>\n",
       "      <td>d</td>\n",
       "      <td>c</td>\n",
       "      <td>5350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Mar,Jun,Sept,Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Store StoreType Assortment  CompetitionDistance  \\\n",
       "1110   1111         a          a               1900.0   \n",
       "1111   1112         c          c               1880.0   \n",
       "1112   1113         a          c               9260.0   \n",
       "1113   1114         a          c                870.0   \n",
       "1114   1115         d          c               5350.0   \n",
       "\n",
       "      CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
       "1110                        6.0                    2014.0       1   \n",
       "1111                        4.0                    2006.0       0   \n",
       "1112                        NaN                       NaN       0   \n",
       "1113                        NaN                       NaN       0   \n",
       "1114                        NaN                       NaN       1   \n",
       "\n",
       "      Promo2SinceWeek  Promo2SinceYear     PromoInterval  \n",
       "1110             31.0           2013.0   Jan,Apr,Jul,Oct  \n",
       "1111              NaN              NaN               NaN  \n",
       "1112              NaN              NaN               NaN  \n",
       "1113              NaN              NaN               NaN  \n",
       "1114             22.0           2012.0  Mar,Jun,Sept,Dec  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_store.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "b740890be1de9c11a46fcda99110b3ad6a5abde2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Store dataset has 1115 Rows (which means unique Shops) and 10 Variables\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Store                          int64\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                 object\n",
       " dtype: object, None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_store.dtypes ,print (\"The Store dataset has {} Rows (which means unique Shops) and {} Variables\".format(str(df_store.shape[0]),str(df_store.shape[1]))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "d89d35b520e9ba3dcf9a19f26586f209a5cc97c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store            100.0\n",
       "DayOfWeek        100.0\n",
       "Date             100.0\n",
       "Sales            100.0\n",
       "Customers        100.0\n",
       "Open             100.0\n",
       "Promo            100.0\n",
       "StateHoliday     100.0\n",
       "SchoolHoliday    100.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.count(0)/df_train.shape[0] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "af43ccf5a4847221547a313ee1b172706ad42616"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-Over those two years, 172817 is the number of times that different stores closed on given days.\n",
      "\n",
      "-From those closed events, 3067 times occured because there was a school holiday. \n",
      "\n",
      "-And 30140 times it occured because of either a bank holiday or easter or christmas.\n",
      "\n",
      "-But interestingly enough, 139610 times those shops closed on days for no apparent reason when no holiday was announced. In fact, those closings were done with no pattern whatsoever and in this case from 2016 to 2018 at almost any month and any day.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ()\n",
    "print (\"-Over those two years, {} is the number of times that different stores closed on given days.\".format(df_train[(df_train.Open == 0)].count()[0]))\n",
    "print ()\n",
    "print (\"-From those closed events, {} times occured because there was a school holiday. \" .format(df_train[(df_train.Open == 0) & (df_train.SchoolHoliday == 1)&(df_train.StateHoliday == '0') ].count()[0]))\n",
    "print ()\n",
    "print (\"-And {} times it occured because of either a bank holiday or easter or christmas.\".format(df_train[(df_train.Open == 0) &\n",
    "         ((df_train.StateHoliday == 'a') |\n",
    "          (df_train.StateHoliday == 'b') | \n",
    "          (df_train.StateHoliday == 'c'))].count()[0]))\n",
    "print ()\n",
    "print (\"-But interestingly enough, {} times those shops closed on days for no apparent reason when no holiday was announced. In fact, those closings were done with no pattern whatsoever and in this case from 2016 to 2018 at almost any month and any day.\".format(df_train[(df_train.Open == 0) &\n",
    "         (df_train.StateHoliday == \"0\")\n",
    "         &(df_train.SchoolHoliday == 0)].count()[0]))\n",
    "print ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "1b6fdeddaa0024e1bf94eb5a2f773e739a6a4da2"
   },
   "outputs": [],
   "source": [
    "df_train=df_train.drop(df_train[(df_train.Open == 0) & (df_train.Sales == 0)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "19d4496592a67cce99156f8b610d463855d78032"
   },
   "outputs": [],
   "source": [
    "df_train = df_train.reset_index(drop=True) #making sure the indexes are back to [0,1,2,3 etc.] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "d5fc8b2775e1c696a066bb5b4d41035d20e838d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our new training set has now 844392 rows after removing the days when stores were closed\n"
     ]
    }
   ],
   "source": [
    "print (\"Our new training set has now {} rows after removing the days when stores were closed\".format(df_train.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "fb18856ad9c17ea5c52c706688c1c8d7c8e91724"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    844392.000000\n",
       "mean       6955.516113\n",
       "std        3104.221189\n",
       "min           0.000000\n",
       "25%        4859.000000\n",
       "50%        6369.000000\n",
       "75%        8360.000000\n",
       "max       41551.000000\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Sales.describe() \n",
    "#we see here a minimum of 0 which means some stores even opened got 0 sales on some days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "896148bee43e41c9733f2e93d303c04ac37f915c"
   },
   "outputs": [],
   "source": [
    "df_train=df_train.drop(df_train[(df_train.Open == 1) & (df_train.Sales == 0)].index)\n",
    "df_train = df_train.reset_index(drop=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "ff80b0c933147031050f4150f8218c156ce49a74"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mean': 6955.96095639424, 'Median': 6369.0}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a13e1ba160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(17,3.5))\n",
    "axes[0].boxplot(df_train.Sales, showmeans=True,vert=False)\n",
    "axes[0].set_xlim(0,max(df_train[\"Sales\"]+1000))\n",
    "axes[0].set_title('Boxplot For Sales Values')\n",
    "axes[1].hist(df_train.Sales, cumulative=False, bins=20)\n",
    "axes[1].set_title(\"Sales histogram\")\n",
    "axes[1].set_xlim((min(df_train.Sales), max(df_train.Sales)))\n",
    "\n",
    "{\"Mean\":np.mean(df_train.Sales),\"Median\":np.median(df_train.Sales)}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "5472279b9e625630e203a5b7b385966ee0f1dde6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.21% of the time having big sales day (considered outliers).\n",
      "0.00% of the time  having no sales at all.\n"
     ]
    }
   ],
   "source": [
    "print (\"{0:.2f}% of the time having big sales day (considered outliers).\".format(df_train[df_train.Sales>14000].count()[0]/df_train.shape[0]*100))\n",
    "print (\"{0:.2f}% of the time  having no sales at all.\".format(df_train[df_train.Sales==0].count()[0]/df_train.shape[0]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "97bd4439e58ef37f4a1f55d71c7de29635662b20"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    844338.000000\n",
       "mean        762.777429\n",
       "std         401.194973\n",
       "min           8.000000\n",
       "25%         519.000000\n",
       "50%         676.000000\n",
       "75%         893.000000\n",
       "max        7388.000000\n",
       "Name: Customers, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Customers.describe()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "7345ff267bc1eeb734c7d2a8b1adeb3d2977789e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mean': 762.7774291812046, 'Median': 676.0}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a13e22edd8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(17,3.5))\n",
    "axes[0].boxplot(df_train.Customers, showmeans=True,vert=False)\n",
    "axes[0].set_xlim(0,max(df_train[\"Customers\"]+100))\n",
    "axes[0].set_title('Boxplot For Customer Values')\n",
    "axes[1].hist(df_train.Customers, cumulative=False, bins=20)\n",
    "axes[1].set_title(\"Customers histogram\")\n",
    "axes[1].set_xlim((min(df_train.Customers), max(df_train.Customers)))\n",
    "\n",
    "{\"Mean\":np.mean(df_train.Customers),\"Median\":np.median(df_train.Customers)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "72362e9ea155442335516f7cf9a6dc38b4fc3d24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.37% of the time having customers more than usual (considered outliers).\n",
      "0.00% of the time having no customers at all.\n"
     ]
    }
   ],
   "source": [
    "print (\"{0:.2f}% of the time having customers more than usual (considered outliers).\".format(df_train[df_train.Customers>1500].count()[0]/df_train.shape[0]*100))\n",
    "print (\"{0:.2f}% of the time having no customers at all.\".format(df_train[df_train.Customers==0].count()[0]/df_train.shape[0]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "e0b6d9517e55146f23e488e40822488b0f403153"
   },
   "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>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>825160</th>\n",
       "      <td>817</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-22</td>\n",
       "      <td>27190</td>\n",
       "      <td>7388</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  DayOfWeek       Date  Sales  Customers  Open  Promo  \\\n",
       "825160    817          2 2016-01-22  27190       7388     1      1   \n",
       "\n",
       "       StateHoliday  SchoolHoliday  \n",
       "825160            0              0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[df_train.Customers>7000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "a88cf8c759b0e3f2ef78eb42f277a3ca4ffa1a49"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8235520263213598"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(df_train.Customers, df_train.Sales)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "7423ea358e268cd991ad35588457be1bc4bab4f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                        100.000000\n",
       "StoreType                    100.000000\n",
       "Assortment                   100.000000\n",
       "CompetitionDistance           99.730942\n",
       "CompetitionOpenSinceMonth     68.251121\n",
       "CompetitionOpenSinceYear      68.251121\n",
       "Promo2                       100.000000\n",
       "Promo2SinceWeek               51.210762\n",
       "Promo2SinceYear               51.210762\n",
       "PromoInterval                 51.210762\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_store.count(0)/df_store.shape[0] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "41884e53cc271f4820e1041fb6f7b036e0a44a5d"
   },
   "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>290</th>\n",
       "      <td>291</td>\n",
       "      <td>d</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>622</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>d</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Feb,May,Aug,Nov</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store StoreType Assortment  CompetitionDistance  \\\n",
       "290    291         d          a                  NaN   \n",
       "621    622         a          c                  NaN   \n",
       "878    879         d          a                  NaN   \n",
       "\n",
       "     CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
       "290                        NaN                       NaN       0   \n",
       "621                        NaN                       NaN       0   \n",
       "878                        NaN                       NaN       1   \n",
       "\n",
       "     Promo2SinceWeek  Promo2SinceYear    PromoInterval  \n",
       "290              NaN              NaN              NaN  \n",
       "621              NaN              NaN              NaN  \n",
       "878              5.0           2013.0  Feb,May,Aug,Nov  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_store[pd.isnull(df_store.CompetitionDistance)] \n",
    "#rows with missing values for Competition Distance, only 3 rows with null which makes sense since 99.73% is filled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "a9026ef3bb396823d71a6210de0c4c8f685d7088"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mean': 5404.901079136691,\n",
       " 'Median': 2325.0,\n",
       " 'Standard Dev': 7659.7282732764415}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a13e35aef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_store_check_distribution=df_store.drop(df_store[pd.isnull(df_store.CompetitionDistance)].index)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(17,3.5))\n",
    "axes[0].boxplot(df_store_check_distribution.CompetitionDistance, showmeans=True,vert=False,)\n",
    "axes[0].set_xlim(0,max(df_store_check_distribution.CompetitionDistance+1000))\n",
    "axes[0].set_title('Boxplot For Closest Competition')\n",
    "axes[1].hist(df_store_check_distribution.CompetitionDistance, cumulative=False, bins=30)\n",
    "axes[1].set_title(\"Closest Competition histogram\")\n",
    "axes[1].set_xlim((min(df_store_check_distribution.CompetitionDistance), max(df_store_check_distribution.CompetitionDistance)))\n",
    "{\"Mean\":np.nanmean(df_store.CompetitionDistance),\"Median\":np.nanmedian(df_store.CompetitionDistance),\"Standard Dev\":np.nanstd(df_store.CompetitionDistance)}#That's what i thought, very different values, let's see why "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "23a1855ca66999f86527f48176640c212b4438d7"
   },
   "outputs": [],
   "source": [
    "df_store['CompetitionDistance'].fillna(df_store['CompetitionDistance'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "46515bb0e283e8a1ec35868846597c134df0002b"
   },
   "outputs": [],
   "source": [
    "df_store.CompetitionOpenSinceMonth.fillna(0, inplace = True)\n",
    "df_store.CompetitionOpenSinceYear.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "fba348b10da38d86933febced91731f4fe1f63bf"
   },
   "outputs": [],
   "source": [
    "#df_store[pd.isnull(df_store.Promo2SinceWeek)]\n",
    "#df_store[pd.isnull(df_store.Promo2SinceWeek)& (df_store.Promo2==0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "5db02179eece2358cf51c393415b58a559be5d1a"
   },
   "outputs": [],
   "source": [
    "df_store.Promo2SinceWeek.fillna(0,inplace=True)\n",
    "df_store.Promo2SinceYear.fillna(0,inplace=True)\n",
    "df_store.PromoInterval.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "19b16d009ad19164134faa0988654dcf1d34020d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                        100.000000\n",
       "StoreType                    100.000000\n",
       "Assortment                   100.000000\n",
       "CompetitionDistance           99.730942\n",
       "CompetitionOpenSinceMonth     68.251121\n",
       "CompetitionOpenSinceYear      68.251121\n",
       "Promo2                       100.000000\n",
       "Promo2SinceWeek              100.000000\n",
       "Promo2SinceYear              100.000000\n",
       "PromoInterval                100.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_store.count(0)/df_store.shape[0] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "d1590413e9409ecba1fb2bae63986a1b9ee2703c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Train_Store dataset has 844338 Rows and 18 Variables\n"
     ]
    }
   ],
   "source": [
    "#Left-join the train to the store dataset since .Why?\n",
    "#Because you want to make sure you have all events even if some of them don't have their store information ( which shouldn't happen)\n",
    "df_train_store = pd.merge(df_train, df_store, how = 'left', on = 'Store')\n",
    "df_train_store.head() \n",
    "print (\"The Train_Store dataset has {} Rows and {} Variables\".format(str(df_train_store.shape[0]),str(df_train_store.shape[1]))) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "c8a18aef6abc7b1241e1f23faf65f413551502d7"
   },
   "outputs": [],
   "source": [
    "df_train_store['SalesperCustomer']=df_train_store['Sales']/df_train_store['Customers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "a988b486237249a05b3b01d8eac76eea76e9d551"
   },
   "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>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>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",
       "      <th>SalesperCustomer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.482883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>9.702400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>10.126675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.342457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.626118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek       Date  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "0      1          5 2018-07-31   5263        555     1      1            0   \n",
       "1      2          5 2018-07-31   6064        625     1      1            0   \n",
       "2      3          5 2018-07-31   8314        821     1      1            0   \n",
       "3      4          5 2018-07-31  13995       1498     1      1            0   \n",
       "4      5          5 2018-07-31   4822        559     1      1            0   \n",
       "\n",
       "   SchoolHoliday StoreType Assortment  CompetitionDistance  \\\n",
       "0              1         c          a               1270.0   \n",
       "1              1         a          a                570.0   \n",
       "2              1         a          a              14130.0   \n",
       "3              1         c          c                620.0   \n",
       "4              1         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  SalesperCustomer  \n",
       "0              0.0              0.0                0          9.482883  \n",
       "1             13.0           2010.0  Jan,Apr,Jul,Oct          9.702400  \n",
       "2             14.0           2011.0  Jan,Apr,Jul,Oct         10.126675  \n",
       "3              0.0              0.0                0          9.342457  \n",
       "4              0.0              0.0                0          8.626118  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "d91dced7b3b6d9286e3437192bc3e637b90401bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a13e3dd6d8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 3,figsize=(17,10) )\n",
    "palette = itertools.cycle(sns.color_palette(n_colors=4))\n",
    "plt.subplots_adjust(hspace = 0.28)\n",
    "#axes[1].df_train_store.groupby(by=\"StoreType\").count().Store.plot(kind='bar')\n",
    "axes[0,0].bar(df_store.groupby(by=\"StoreType\").count().Store.index,df_store.groupby(by=\"StoreType\").count().Store,color=[next(palette),next(palette),next(palette),next(palette)])\n",
    "axes[0,0].set_title(\"Number of Stores per Store Type \\n Fig 1.1\")\n",
    "axes[0,1].bar(df_train_store.groupby(by=\"StoreType\").sum().Sales.index,df_train_store.groupby(by=\"StoreType\").sum().Sales/1e9,color=[next(palette),next(palette),next(palette),next(palette)])\n",
    "axes[0,1].set_title(\"Total Sales per Store Type (in Billions) \\n Fig 1.2\")\n",
    "axes[0,2].bar(df_train_store.groupby(by=\"StoreType\").sum().Customers.index,df_train_store.groupby(by=\"StoreType\").sum().Customers/1e6,color=[next(palette),next(palette),next(palette),next(palette)])\n",
    "axes[0,2].set_title(\"Total Number of Customers per Store Type (in Millions) \\n Fig 1.3\")\n",
    "axes[1,0].bar(df_train_store.groupby(by=\"StoreType\").sum().Customers.index,df_train_store.groupby(by=\"StoreType\").Sales.mean(),color=[next(palette),next(palette),next(palette),next(palette)])\n",
    "axes[1,0].set_title(\"Average Sales per Store Type \\n Fig 1.4\")\n",
    "axes[1,1].bar(df_train_store.groupby(by=\"StoreType\").sum().Customers.index,df_train_store.groupby(by=\"StoreType\").Customers.mean(),color=[next(palette),next(palette),next(palette),next(palette)])\n",
    "axes[1,1].set_title(\"Average Number of Customers per Store Type \\n Fig 1.5\")\n",
    "axes[1,2].bar(df_train_store.groupby(by=\"StoreType\").sum().Sales.index,df_train_store.groupby(by=\"StoreType\").SalesperCustomer.mean(),color=[next(palette),next(palette),next(palette),next(palette)])\n",
    "axes[1,2].set_title(\"Average Spending per Customer in each Store Type \\n Fig 1.6\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "9fbee94c5e50a167ed4eba41df90f7be284c0c65"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StoreType  Assortment\n",
       "a          a             381\n",
       "           c             221\n",
       "b          a               7\n",
       "           b               9\n",
       "           c               1\n",
       "c          a              77\n",
       "           c              71\n",
       "d          a             128\n",
       "           c             220\n",
       "Name: Assortment, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a13e2836a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "StoretypeXAssortment = sns.countplot(x=\"StoreType\",hue=\"Assortment\",order=[\"a\",\"b\",\"c\",\"d\"], data=df_store,palette=sns.color_palette(\"Set2\", n_colors=3)).set_title(\"Number of Different Assortments per Store Type\")\n",
    "df_store.groupby(by=[\"StoreType\",\"Assortment\"]).Assortment.count()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "e4b9dab77bc6693c7c97d70eb6bf367291d67c60"
   },
   "outputs": [],
   "source": [
    "df_train_store['Month']=df_train_store.Date.dt.month\n",
    "df_train_store['Year']=df_train_store.Date.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "91c1f55ba9fcca06301b4ce1363d25206524b5a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2a13e07fd68>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a13e35ab00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.factorplot(data = df_train_store, x =\"Month\", y = \"Sales\", \n",
    "               col = 'Promo', # per store type in cols\n",
    "               hue = 'Promo2',\n",
    "               row = \"Year\"\n",
    "              ,sharex=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "c0b32781541b7dd7d7a948995832d5a76a6ca6e6",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2a13e28c2b0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a13e32bd68>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(data = df_train_store, x =\"Month\", y = \"SalesperCustomer\", \n",
    "               col = 'Promo', # per store type in cols\n",
    "               hue = 'Promo2',\n",
    "               row = \"Year\"\n",
    "              ,sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "9a7a47726e8b7302d7ed6b47c0af769214d189d7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xddca518>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(data = df_train_store, x =\"DayOfWeek\", y = \"Sales\",\n",
    "                hue='Promo'\n",
    "              ,sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "1a7ca794a18f0696c56de86d195b925125ff07f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Stores opened on Sundays:33\n"
     ]
    }
   ],
   "source": [
    "#33 Stores are opened on Sundays\n",
    "print (\"Number of Stores opened on Sundays:{}\" .format(df_train_store[(df_train_store.Open == 1) & (df_train_store.DayOfWeek == 7)]['Store'].unique().shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "148d69265b5f2d5202b1b88d8c1b046f79a16669"
   },
   "outputs": [],
   "source": [
    "df_train_store['CompetitionDist_Cat']=pd.cut(df_train_store['CompetitionDistance'], 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "34f6e4853205360bc3b0ac8faf2ed0cff7ec1efa"
   },
   "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>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>StoreType</th>\n",
       "      <th>...</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",
       "      <th>SalesperCustomer</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>CompetitionDist_Cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.482883</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>(-55.84, 15188.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>...</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",
       "      <td>9.702400</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>(-55.84, 15188.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>...</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",
       "      <td>10.126675</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>(-55.84, 15188.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.342457</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>(-55.84, 15188.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.626118</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>(15188.0, 30356.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek       Date  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "0      1          5 2018-07-31   5263        555     1      1            0   \n",
       "1      2          5 2018-07-31   6064        625     1      1            0   \n",
       "2      3          5 2018-07-31   8314        821     1      1            0   \n",
       "3      4          5 2018-07-31  13995       1498     1      1            0   \n",
       "4      5          5 2018-07-31   4822        559     1      1            0   \n",
       "\n",
       "   SchoolHoliday StoreType         ...          CompetitionOpenSinceMonth  \\\n",
       "0              1         c         ...                                9.0   \n",
       "1              1         a         ...                               11.0   \n",
       "2              1         a         ...                               12.0   \n",
       "3              1         c         ...                                9.0   \n",
       "4              1         a         ...                                4.0   \n",
       "\n",
       "   CompetitionOpenSinceYear  Promo2  Promo2SinceWeek  Promo2SinceYear  \\\n",
       "0                    2008.0       0              0.0              0.0   \n",
       "1                    2007.0       1             13.0           2010.0   \n",
       "2                    2006.0       1             14.0           2011.0   \n",
       "3                    2009.0       0              0.0              0.0   \n",
       "4                    2015.0       0              0.0              0.0   \n",
       "\n",
       "     PromoInterval  SalesperCustomer Month  Year  CompetitionDist_Cat  \n",
       "0                0          9.482883     7  2018    (-55.84, 15188.0]  \n",
       "1  Jan,Apr,Jul,Oct          9.702400     7  2018    (-55.84, 15188.0]  \n",
       "2  Jan,Apr,Jul,Oct         10.126675     7  2018    (-55.84, 15188.0]  \n",
       "3                0          9.342457     7  2018    (-55.84, 15188.0]  \n",
       "4                0          8.626118     7  2018   (15188.0, 30356.0]  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "4c356c11b9f84c809c154435fbb53522a1c5fbd4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(CompetitionDist_Cat\n",
       " (-55.84, 15188.0]     6961.648918\n",
       " (15188.0, 30356.0]    7041.606049\n",
       " (30356.0, 45524.0]    6471.923849\n",
       " (45524.0, 60692.0]    5980.114445\n",
       " (60692.0, 75860.0]    7775.446449\n",
       " Name: Sales, dtype: float64, CompetitionDist_Cat\n",
       " (-55.84, 15188.0]     771.980164\n",
       " (15188.0, 30356.0]    691.574765\n",
       " (30356.0, 45524.0]    680.101854\n",
       " (45524.0, 60692.0]    643.988246\n",
       " (60692.0, 75860.0]    914.574972\n",
       " Name: Customers, dtype: float64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store.groupby(by=\"CompetitionDist_Cat\").Sales.mean(),df_train_store.groupby(by=\"CompetitionDist_Cat\").Customers.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "11971a02ffa8ddd8036d46416b9c97f1b9e68682",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "del df_train_store[\"CompetitionDist_Cat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "38cfb14f90b3b735b72f697fc6c5cf4f0c5660a0"
   },
   "outputs": [],
   "source": [
    "df_train_store['Day']=df_train_store.Date.dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "a37852bd9a976faa96e18edc32a6243acf4b2a38"
   },
   "outputs": [],
   "source": [
    "del df_train_store[\"Date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "8cbc3899f51d794ff588a91fc8556aad15b8d737"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False, False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store['StoreType'].isnull().any(),df_train_store['Assortment'].isnull().any(),df_train_store['StateHoliday'].isnull().any()\n",
    "#No Null values we can proceed with the transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "156e048cff46dbea9ff7aeb657930356e7f4e912"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(a    457042\n",
       " d    258768\n",
       " c    112968\n",
       " b     15560\n",
       " Name: StoreType, dtype: int64, a    444875\n",
       " c    391254\n",
       " b      8209\n",
       " Name: Assortment, dtype: int64, 0    843428\n",
       " a       694\n",
       " b       145\n",
       " c        71\n",
       " Name: StateHoliday, dtype: int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store[\"StoreType\"].value_counts(),df_train_store[\"Assortment\"].value_counts(),df_train_store[\"StateHoliday\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "c75ccc4e48297619dad9cf74611d0b941475a4ae"
   },
   "outputs": [],
   "source": [
    "df_train_store['StateHoliday'] = df_train_store['StateHoliday'].astype('category')\n",
    "df_train_store['Assortment'] = df_train_store['Assortment'].astype('category')\n",
    "df_train_store['StoreType'] = df_train_store['StoreType'].astype('category')\n",
    "df_train_store['PromoInterval']= df_train_store['PromoInterval'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "a9023329d8ebf52ecdce786d72a953aeba2cd775"
   },
   "outputs": [],
   "source": [
    "df_train_store['StateHoliday_cat'] = df_train_store['StateHoliday'].cat.codes\n",
    "df_train_store['Assortment_cat'] = df_train_store['Assortment'].cat.codes\n",
    "df_train_store['StoreType_cat'] = df_train_store['StoreType'].cat.codes\n",
    "df_train_store['PromoInterval_cat'] = df_train_store['PromoInterval'].cat.codes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "c2e53c2e397436d482b3330f0bda625ed277e2c6"
   },
   "outputs": [],
   "source": [
    "df_train_store['StateHoliday_cat'] = df_train_store['StateHoliday_cat'].astype('float')\n",
    "df_train_store['Assortment_cat'] = df_train_store['Assortment_cat'].astype('float')\n",
    "df_train_store['StoreType_cat'] = df_train_store['StoreType_cat'].astype('float')\n",
    "df_train_store['PromoInterval_cat'] = df_train_store['PromoInterval_cat'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "9012b8b5e19bb04f5e593cad4d2a059b90f32e4a"
   },
   "outputs": [],
   "source": [
    "\n",
    "#df_train_store[['StateHoliday', 'StoreType', 'Assortment']] = df_train_store[['StateHoliday', 'StoreType', 'Assortment']].apply(lambda x: x.cat.codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "3422909bac1d0999470ce9ffaa806d3b7186ddbf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                           int64\n",
       "DayOfWeek                       int64\n",
       "Sales                           int64\n",
       "Customers                       int64\n",
       "Open                            int64\n",
       "Promo                           int64\n",
       "StateHoliday                 category\n",
       "SchoolHoliday                   int64\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",
       "SalesperCustomer              float64\n",
       "Month                           int64\n",
       "Year                            int64\n",
       "Day                             int64\n",
       "StateHoliday_cat              float64\n",
       "Assortment_cat                float64\n",
       "StoreType_cat                 float64\n",
       "PromoInterval_cat             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_uuid": "bc3186eae86d7d35f9c0d865241575e6f7f7e768"
   },
   "outputs": [],
   "source": [
    "df_correlation=df_train_store[['Store', 'DayOfWeek', 'Sales', 'Customers', 'Open', 'Promo',\n",
    "        'SchoolHoliday',\n",
    "       'CompetitionDistance', 'CompetitionOpenSinceMonth',\n",
    "       'CompetitionOpenSinceYear', 'Promo2', 'Promo2SinceWeek',\n",
    "       'Promo2SinceYear', 'SalesperCustomer', 'Month', 'Year',\n",
    "       'Day', 'StateHoliday_cat', 'Assortment_cat', 'StoreType_cat',\n",
    "       'PromoInterval_cat']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_uuid": "cbd36d9532a45edced8ceaabc24a13950d38b413"
   },
   "outputs": [],
   "source": [
    "df_correlation=df_correlation.drop('Open', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_uuid": "7d80d566520e032defe60174c5e3066bf1640790"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Store', 'DayOfWeek', 'Sales', 'Customers', 'Open', 'Promo',\n",
       "       'StateHoliday', 'SchoolHoliday', 'StoreType', 'Assortment',\n",
       "       'CompetitionDistance', 'CompetitionOpenSinceMonth',\n",
       "       'CompetitionOpenSinceYear', 'Promo2', 'Promo2SinceWeek',\n",
       "       'Promo2SinceYear', 'PromoInterval', 'SalesperCustomer', 'Month', 'Year',\n",
       "       'Day', 'StateHoliday_cat', 'Assortment_cat', 'StoreType_cat',\n",
       "       'PromoInterval_cat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_uuid": "8d791008f1b79ece73e8d54f5d687707faa88f12"
   },
   "outputs": [],
   "source": [
    "df_train_store['CompetitionOpenSince'] = np.where((df_train_store['CompetitionOpenSinceMonth']==0) & (df_train_store['CompetitionOpenSinceYear']==0) , 0,(df_train_store.Month - df_train_store.CompetitionOpenSinceMonth) + \n",
    "                                       (12 * (df_train_store.Year - df_train_store.CompetitionOpenSinceYear)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_uuid": "8bd711c154e2ebf7c4d0864e5870f576d5e7f308"
   },
   "outputs": [],
   "source": [
    "#now that CompetitionOpenSince is created \n",
    "#we can get rid of `CompetitionOpenSinceYear` and `CompeitionOpenSinceMonth`\n",
    "del df_train_store['CompetitionOpenSinceYear']\n",
    "del df_train_store['CompetitionOpenSinceMonth']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_uuid": "00a9526a454be95cbd35721b8431bab1c1d3639b"
   },
   "outputs": [],
   "source": [
    "df_train_store[\"is_holiday_state\"] = df_train_store['StateHoliday'].map({\"0\": 0, \"a\": 1, \"b\": 1, \"c\": 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_uuid": "1092155c7941db61e9fe8c404ce3d031f6a69dd3"
   },
   "outputs": [],
   "source": [
    "del df_train_store['StateHoliday_cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "_uuid": "686c1350281bd2893756007555bee38de3242c88"
   },
   "outputs": [],
   "source": [
    "df_train_store=pd.get_dummies(df_train_store, columns=[\"Assortment\", \"StoreType\",\"PromoInterval\"], prefix=[\"is_Assortment\", \"is_StoreType\",\"is_PromoInteval\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_uuid": "b88947ff23c72e7a4672e8e12f2d463cc6fb2124",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "del df_train_store['Assortment_cat']\n",
    "del df_train_store['StoreType_cat']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_uuid": "ef185486a4da740cbe767c9709c67275129a0fa7"
   },
   "outputs": [],
   "source": [
    "del df_train_store['PromoInterval_cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_uuid": "5b0e5442dd7d748634cfe3261c7a60bcb4aa9363"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Store', 'DayOfWeek', 'Sales', 'Customers', 'Open', 'Promo',\n",
       "       'StateHoliday', 'SchoolHoliday', 'CompetitionDistance', 'Promo2',\n",
       "       'Promo2SinceWeek', 'Promo2SinceYear', 'SalesperCustomer', 'Month',\n",
       "       'Year', 'Day', 'CompetitionOpenSince', 'is_holiday_state',\n",
       "       'is_Assortment_a', 'is_Assortment_b', 'is_Assortment_c',\n",
       "       'is_StoreType_a', 'is_StoreType_b', 'is_StoreType_c', 'is_StoreType_d',\n",
       "       'is_PromoInteval_0', 'is_PromoInteval_Feb,May,Aug,Nov',\n",
       "       'is_PromoInteval_Jan,Apr,Jul,Oct', 'is_PromoInteval_Mar,Jun,Sept,Dec'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_uuid": "d6f0e44720942f75a9cc5da61e9de5c7867b19a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Test dataset has 41088 Rows and 8 Variables\n"
     ]
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"test.csv\",sep=',', parse_dates=['Date'],\n",
    "                       low_memory = False)\n",
    "print (\"The Test dataset has {} Rows and {} Variables\".format(str(df_test.shape[0]),str(df_test.shape[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_uuid": "ca67c54a369d77a492dbb71f134cccf5ed5e9c24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Test_Store dataset has 41088 Rows and 17 Variables\n"
     ]
    }
   ],
   "source": [
    "df_test.fillna(1, inplace = True) #11rows with Nans decided to leave them open since its one store 622 which is \n",
    "#usually open\n",
    "#Left-join the train to the store dataset since .Why?\n",
    "#Because you want to make sure you have all events even if some of them don't have their store information ( which shouldn't happen)\n",
    "df_test_store = pd.merge(df_test, df_store, how = 'left', on = 'Store')\n",
    "print (\"The Test_Store dataset has {} Rows and {} Variables\".format(str(df_test_store.shape[0]),str(df_test_store.shape[1]))) \n",
    "df_test_store['Month']=df_test_store.Date.dt.month\n",
    "df_test_store['Year']=df_test_store.Date.dt.year\n",
    "df_test_store['Day']=df_test_store.Date.dt.day\n",
    "\n",
    "df_test_store['StateHoliday'] = df_test_store['StateHoliday'].astype('category')\n",
    "df_test_store['Assortment'] = df_test_store['Assortment'].astype('category')\n",
    "df_test_store['StoreType'] = df_test_store['StoreType'].astype('category')\n",
    "df_test_store['PromoInterval']= df_test_store['PromoInterval'].astype('category')\n",
    "df_test_store['StateHoliday_cat'] = df_test_store['StateHoliday'].cat.codes\n",
    "df_test_store['Assortment_cat'] = df_test_store['Assortment'].cat.codes\n",
    "df_test_store['StoreType_cat'] = df_test_store['StoreType'].cat.codes\n",
    "df_test_store['PromoInterval_cat'] = df_test_store['PromoInterval'].cat.codes\n",
    "df_test_store['StateHoliday_cat'] = df_test_store['StateHoliday_cat'].astype('float')\n",
    "df_test_store['Assortment_cat'] = df_test_store['Assortment_cat'].astype('float')\n",
    "df_test_store['StoreType_cat'] = df_test_store['StoreType_cat'].astype('float')\n",
    "df_test_store['PromoInterval_cat'] = df_test_store['PromoInterval_cat'].astype('float')\n",
    "df_test_store['CompetitionOpenSince'] = np.where((df_test_store['CompetitionOpenSinceMonth']==0) & (df_test_store['CompetitionOpenSinceYear']==0) , 0,(df_test_store.Month - df_test_store.CompetitionOpenSinceMonth) + \n",
    "                                       (12 * (df_test_store.Year - df_test_store.CompetitionOpenSinceYear)) )\n",
    "\n",
    "\n",
    "\n",
    "df_test_store[\"is_holiday_state\"] = df_test_store['StateHoliday'].map({\"0\": 0, \"a\": 1, \"b\": 1, \"c\": 1})\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df_test_store=pd.get_dummies(df_test_store, columns=[\"Assortment\", \"StoreType\",\"PromoInterval\"], prefix=[\"is_Assortment\", \"is_StoreType\",\"is_PromoInteval\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_uuid": "2ff65a425610a10455fc63e544ef96d259e2f84c"
   },
   "outputs": [],
   "source": [
    "del df_test_store['Date']\n",
    "del df_test_store['CompetitionOpenSinceYear']\n",
    "del df_test_store['CompetitionOpenSinceMonth']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_uuid": "0a24cc21745362a94b4f99ad9b0aeca8a789120a"
   },
   "outputs": [],
   "source": [
    "del df_test_store['StateHoliday_cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_uuid": "6549429113aef40cb19a50980d924d0771e4a171"
   },
   "outputs": [],
   "source": [
    "del df_test_store['Assortment_cat']\n",
    "del df_test_store['StoreType_cat']\n",
    "del df_test_store['PromoInterval_cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_uuid": "8e0742b078c9c8ba1981ce1de3c7d1e62b0388b1"
   },
   "outputs": [],
   "source": [
    "del df_test_store['StateHoliday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_uuid": "71146e8d081e20dee7d26e960953d0d9b72e037b"
   },
   "outputs": [],
   "source": [
    "del df_train_store['StateHoliday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "_uuid": "c77d5c6738179ad1c504856541ea94e64a985e4b"
   },
   "outputs": [],
   "source": [
    "def rmspe(y, yhat):\n",
    "    rmspe = np.sqrt(np.mean( (y - yhat)**2 ))\n",
    "    return rmspe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "_uuid": "f61bba9795f60163f4a66ab13761a2d0245dd9e9"
   },
   "outputs": [],
   "source": [
    "features = df_train_store.drop(['Customers', 'Sales', 'SalesperCustomer'], axis = 1) \n",
    "#a rule of thumb is to transform my target value to log if i see the values are very dispersed which is the case\n",
    "#and then of course revert them with np.exp to their real values\n",
    "targets=np.log(df_train_store.Sales)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_uuid": "ed6583fbb13b369b29216e56ec6ab8a0de55efde"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training and testing split was successful.\n"
     ]
    }
   ],
   "source": [
    "X_train, X_train_test, y_train, y_train_test = model_selection.train_test_split(features, targets, test_size=0.20, random_state=15)\n",
    "print (\"Training and testing split was successful.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "_uuid": "86512ec8c3e662369707c0ea18a11d406063225d"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float32').",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-82-a5dfce02afa2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m                              \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m                              warm_start=False)\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mrfr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32me:\\anacondasoftware\\lib\\site-packages\\sklearn\\ensemble\\forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m         \u001b[1;31m# Validate or convert input data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 250\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    251\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'csc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\anacondasoftware\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    572\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 573\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    574\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    575\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\anacondasoftware\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan)\u001b[0m\n\u001b[0;32m     54\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[0;32m     55\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'infinity'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'NaN, infinity'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
     ]
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(n_estimators=10, \n",
    "                             criterion='mse', \n",
    "                             max_depth=5, \n",
    "                             min_samples_split=2, \n",
    "                             min_samples_leaf=1, \n",
    "                             min_weight_fraction_leaf=0.0, \n",
    "                             max_features='auto', \n",
    "                             max_leaf_nodes=None, \n",
    "                             min_impurity_decrease=0.0, \n",
    "                             min_impurity_split=None, \n",
    "                             bootstrap=True, \n",
    "                             oob_score=False,\n",
    "                             n_jobs=4,\n",
    "                             random_state=31, \n",
    "                             verbose=0, \n",
    "                             warm_start=False)\n",
    "rfr.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "_uuid": "0d71c8db27f58b0568a97df0683a19f5682e1e21"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nparams = {'max_depth':(4,6,8,10,12,14,16,20),\\n         'n_estimators':(4,8,16,24,48,72,96,128),\\n         'min_samples_split':(2,4,6,8,10)}\\n#scoring_fnc = metrics.make_scorer(rmspe)\\n#the dimensionality is high, the number of combinations we have to search is enormous, using RandomizedSearchCV \\n# is a better option then GridSearchCV\\ngrid = model_selection.RandomizedSearchCV(estimator=rfr,param_distributions=params,cv=10) \\n#choosing 10 K-Folds makes sure i went through all of the data and didn't miss any pattern.(takes time to run but is worth doing it)\\ngrid.fit(X_train, y_train)\\n\""
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "'''\n",
    "params = {'max_depth':(4,6,8,10,12,14,16,20),\n",
    "         'n_estimators':(4,8,16,24,48,72,96,128),\n",
    "         'min_samples_split':(2,4,6,8,10)}\n",
    "#scoring_fnc = metrics.make_scorer(rmspe)\n",
    "#the dimensionality is high, the number of combinations we have to search is enormous, using RandomizedSearchCV \n",
    "# is a better option then GridSearchCV\n",
    "grid = model_selection.RandomizedSearchCV(estimator=rfr,param_distributions=params,cv=10) \n",
    "#choosing 10 K-Folds makes sure i went through all of the data and didn't miss any pattern.(takes time to run but is worth doing it)\n",
    "grid.fit(X_train, y_train)\n",
    "'''\n",
    "#I AM NOT GOING TO RUN THIS CHUNK TO BE ABLE TO COMMIT AND RUN MY KERNEL ON KAGGLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "_uuid": "1e598a73ed68b84eb62ae472b5dd0c7b62522195"
   },
   "outputs": [],
   "source": [
    "#This is the best combination i got from what i propose to try out with a (mse) score of 0.855 which is quite good\n",
    "#grid.best_params_,grid.best_score_\n",
    "#MY BEST PARAMS ARE :n_estimators=128,max_depth=20,min_samples_split=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "_uuid": "abd32bcaae16d84b3085f02be1f539e7f42b0a5f"
   },
   "outputs": [],
   "source": [
    "#with the optimal parameters i got let's see how it behaves with the validation set\n",
    "rfr_val=RandomForestRegressor(n_estimators=128, \n",
    "                             criterion='rmse', \n",
    "                             max_depth=20, \n",
    "                             min_samples_split=10, \n",
    "                             min_samples_leaf=1, \n",
    "                             min_weight_fraction_leaf=0.0, \n",
    "                             max_features='auto', \n",
    "                             max_leaf_nodes=None, \n",
    "                             min_impurity_decrease=0.0, \n",
    "                             min_impurity_split=None, \n",
    "                             bootstrap=True, \n",
    "                             oob_score=False,\n",
    "                             n_jobs=4, #setting n_jobs to 4 makes sure you're using the full potential of the machine you're running the training on\n",
    "                             random_state=35, \n",
    "                             verbose=0, \n",
    "                             warm_start=False)\n",
    "model_RF_test=rfr_val.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "_uuid": "04bd85c9b9ace7010770382d28f6a31f1ca457c0"
   },
   "outputs": [],
   "source": [
    "yhat=model_RF_test.predict(X_train_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "_uuid": "24937d1451cb0d9650377ea2030a8f2f4ffb05a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3.3000e+01, 3.1500e+02, 1.4240e+03, 7.9370e+03, 3.5344e+04,\n",
       "        7.4586e+04, 3.9543e+04, 7.9330e+03, 1.6740e+03, 7.9000e+01]),\n",
       " array([ 6.62418282,  7.01069701,  7.39721121,  7.7837254 ,  8.17023959,\n",
       "         8.55675378,  8.94326797,  9.32978217,  9.71629636, 10.10281055,\n",
       "        10.48932474]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 149,
     "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.hist(yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "_uuid": "03134041eab3759aa7ab1a6d1d7c67e4b84ec2aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1629166664720833"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error=rmspe(y_train_test,yhat)\n",
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "_uuid": "6a2be57b59e1fcba050ff8d2d36c5893bd66dcc4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = rfr_val.feature_importances_\n",
    "std = np.std([rfr_val.feature_importances_ for tree in rfr_val.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)\n",
    "palette1 = itertools.cycle(sns.color_palette())\n",
    "# Store the feature ranking\n",
    "features_ranked=[]\n",
    "for f in range(X_train.shape[1]):\n",
    "    features_ranked.append(X_train.columns[indices[f]])\n",
    "# Plot the feature importances of the forest\n",
    "\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.barh(range(X_train.shape[1]), importances[indices],\n",
    "            color=[next(palette1)], align=\"center\")\n",
    "plt.yticks(range(X_train.shape[1]), features_ranked)\n",
    "plt.ylabel('Features')\n",
    "plt.ylim([-1, X_train.shape[1]])\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "_uuid": "36c040ffe0593aa9d43bddeaf47135072fa65437"
   },
   "outputs": [],
   "source": [
    "df_test_store1=df_test_store.drop(['Id'],axis=1)\n",
    "kaggle_yhat= model_RF_test.predict(df_test_store1)\n",
    "kaggle_preds= pd.DataFrame({'Id': df_test_store['Id'], \n",
    "                          'Sales': np.exp(kaggle_yhat)})\n",
    "kaggle_preds.to_csv(\"submission.csv\", index = False)\n"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}