Walmart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:00.471566Z",
     "start_time": "2020-02-16T20:22:58.909064Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (0.90)\n",
      "Requirement already satisfied: numpy in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from xgboost) (1.16.4)\n",
      "Requirement already satisfied: scipy in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from xgboost) (1.4.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:01.795002Z",
     "start_time": "2020-02-16T20:23:00.471566Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: lightgbm in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (2.3.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from lightgbm) (1.16.4)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from lightgbm) (0.21.2)\n",
      "Requirement already satisfied: scipy in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from lightgbm) (1.4.1)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from scikit-learn->lightgbm) (0.13.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:40:01.874687Z",
     "start_time": "2020-02-18T17:39:58.213282Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from time import time\n",
    "\n",
    "\n",
    "# Visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "%matplotlib inline\n",
    "\n",
    "# Prediction libraries\n",
    "from sklearn import linear_model\n",
    "from sklearn.preprocessing import MinMaxScaler, RobustScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_squared_log_error\n",
    "from sklearn.metrics.scorer import make_scorer\n",
    "from sklearn.linear_model import LinearRegression, Lasso, ElasticNet\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.model_selection import KFold, cross_val_score, GridSearchCV\n",
    "\n",
    "import xgboost as xgb\n",
    "from lightgbm import LGBMRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:40:03.077320Z",
     "start_time": "2020-02-18T17:40:02.723056Z"
    }
   },
   "outputs": [],
   "source": [
    "test = pd.read_csv(r'C:\\Users\\irind\\Desktop\\MMA_Courses\\INSY 695_Enterprise\\walmart-recruiting-store-sales-forecasting\\Final\\test.csv')\n",
    "train = pd.read_csv(r'C:\\Users\\irind\\Desktop\\MMA_Courses\\INSY 695_Enterprise\\walmart-recruiting-store-sales-forecasting\\Final\\train.csv')\n",
    "features = pd.read_csv(r'C:\\Users\\irind\\Desktop\\MMA_Courses\\INSY 695_Enterprise\\walmart-recruiting-store-sales-forecasting\\Final\\features.csv')\n",
    "sample = pd.read_csv(r'C:\\Users\\irind\\Desktop\\MMA_Courses\\INSY 695_Enterprise\\walmart-recruiting-store-sales-forecasting\\Final\\sampleSubmission.csv')\n",
    "stores = pd.read_csv(r'C:\\Users\\irind\\Desktop\\MMA_Courses\\INSY 695_Enterprise\\walmart-recruiting-store-sales-forecasting\\Final\\stores.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-11T14:42:41.188250Z",
     "start_time": "2020-02-11T14:42:41.172631Z"
    }
   },
   "source": [
    "## Stores DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.212432Z",
     "start_time": "2020-02-16T20:23:04.196812Z"
    }
   },
   "outputs": [
    {
     "data": {
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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>Type</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>202307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>37392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>205863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>34875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Store Type    Size\n",
       "0      1    A  151315\n",
       "1      2    A  202307\n",
       "2      3    B   37392\n",
       "3      4    A  205863\n",
       "4      5    B   34875"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.228055Z",
     "start_time": "2020-02-16T20:23:04.212432Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45 entries, 0 to 44\n",
      "Data columns (total 3 columns):\n",
      "Store    45 non-null int64\n",
      "Type     45 non-null object\n",
      "Size     45 non-null int64\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "stores.info()\n",
    "#There are 45 stores in total in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.259297Z",
     "start_time": "2020-02-16T20:23:04.228055Z"
    }
   },
   "outputs": [
    {
     "data": {
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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>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>130287.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.133926</td>\n",
       "      <td>63825.271991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>34875.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>70713.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>126512.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>202307.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>219622.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Store           Size\n",
       "count  45.000000      45.000000\n",
       "mean   23.000000  130287.600000\n",
       "std    13.133926   63825.271991\n",
       "min     1.000000   34875.000000\n",
       "25%    12.000000   70713.000000\n",
       "50%    23.000000  126512.000000\n",
       "75%    34.000000  202307.000000\n",
       "max    45.000000  219622.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stores.describe()\n",
    "#Average store size is 130,287 SQFT\n",
    "#Most stores are around 202,307 SQFT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look for NULL values in stores DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.290539Z",
     "start_time": "2020-02-16T20:23:04.259297Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Size</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "       Total  Percent\n",
       "Size       0      0.0\n",
       "Type       0      0.0\n",
       "Store      0      0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = stores.isnull().sum().sort_values(ascending=False)\n",
    "percent = (stores.isnull().sum()/stores.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are NULL rows in Stores DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T22:45:19.760565Z",
     "start_time": "2020-02-16T22:45:19.722641Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "A    190992.5\n",
       "B    109865.0\n",
       "C     39511.7\n",
       "Name: Size, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby store size to see how large each store type is on average\n",
    "stores['Size'].groupby(stores['Type']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T22:51:47.530437Z",
     "start_time": "2020-02-16T22:51:47.375878Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['Store A', 'Store B', 'Store C']\n",
    "values = [190992.4, 109865.0, 39511.7]\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.bar(names, values)\n",
    "plt.suptitle('Weekly Sales Distribution by Store Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store type 'A' are the largest types, while 'C' are the smallest Wallmart stores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of distribution of stores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.556103Z",
     "start_time": "2020-02-16T20:23:04.306164Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize = (15,6))\n",
    "ax[0].bar(stores['Type'].unique(), stores['Size'].groupby(stores['Type']).count())\n",
    "ax[0].set_ylabel('Number of Stores')\n",
    "ax[0].set_xlabel('Store Type')\n",
    "ax[0].yaxis.grid(True, linewidth=0.3)\n",
    "\n",
    "ax[1].scatter(stores['Type'], stores['Size'])\n",
    "ax[1].scatter(stores['Type'].unique(), stores['Size'].groupby(stores['Type']).mean()) #Store Type Average Store Size Vs \n",
    "ax[1].set_ylabel('Store Size (Total / Average)')\n",
    "ax[1].set_xlabel('Store Type')\n",
    "ax[1].yaxis.grid(True, linewidth=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most stores in the dataset are part of store type 'A' and distribution of store sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identify outliers in Stores df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.571723Z",
     "start_time": "2020-02-16T20:23:04.556103Z"
    }
   },
   "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>Type</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>37392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>34875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>A</td>\n",
       "      <td>39690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>A</td>\n",
       "      <td>39910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Store Type   Size\n",
       "2       3    B  37392\n",
       "4       5    B  34875\n",
       "32     33    A  39690\n",
       "35     36    A  39910"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stores[(stores['Size'] < 40000) & (~stores['Type'].isin(['C']))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are outliers which show unusually small sized stores in correspondence with the other stores of the same type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Takeaways:\n",
    "\n",
    "   1. Column TYPE is a candidate for dummifying.\n",
    "   2. Most stores are of TYPE='A'. Only a few stores are of TYPE='C'.\n",
    "   3. TYPE columns seem to be linked to Store Size. Average store size of TYPE 'A' is ~ 175k, TYPE 'B' is ~ 100k and TYPE 'C' is ~40k\n",
    "   4. Four stores [3, 5, 33 & 36] whose size is < 40k, seem to have been incorrectly tagged as Types A & B. We should drop theses\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.587346Z",
     "start_time": "2020-02-16T20:23:04.571723Z"
    }
   },
   "outputs": [
    {
     "data": {
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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>Date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  \\\n",
       "0      1  2010-02-05        42.31       2.572        NaN        NaN   \n",
       "1      1  2010-02-12        38.51       2.548        NaN        NaN   \n",
       "2      1  2010-02-19        39.93       2.514        NaN        NaN   \n",
       "3      1  2010-02-26        46.63       2.561        NaN        NaN   \n",
       "4      1  2010-03-05        46.50       2.625        NaN        NaN   \n",
       "\n",
       "   MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  IsHoliday  \n",
       "0        NaN        NaN        NaN  211.096358         8.106      False  \n",
       "1        NaN        NaN        NaN  211.242170         8.106       True  \n",
       "2        NaN        NaN        NaN  211.289143         8.106      False  \n",
       "3        NaN        NaN        NaN  211.319643         8.106      False  \n",
       "4        NaN        NaN        NaN  211.350143         8.106      False  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.602967Z",
     "start_time": "2020-02-16T20:23:04.587346Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8190 entries, 0 to 8189\n",
      "Data columns (total 12 columns):\n",
      "Store           8190 non-null int64\n",
      "Date            8190 non-null object\n",
      "Temperature     8190 non-null float64\n",
      "Fuel_Price      8190 non-null float64\n",
      "MarkDown1       4032 non-null float64\n",
      "MarkDown2       2921 non-null float64\n",
      "MarkDown3       3613 non-null float64\n",
      "MarkDown4       3464 non-null float64\n",
      "MarkDown5       4050 non-null float64\n",
      "CPI             7605 non-null float64\n",
      "Unemployment    7605 non-null float64\n",
      "IsHoliday       8190 non-null bool\n",
      "dtypes: bool(1), float64(9), int64(1), object(1)\n",
      "memory usage: 711.9+ KB\n"
     ]
    }
   ],
   "source": [
    "features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.649832Z",
     "start_time": "2020-02-16T20:23:04.602967Z"
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   },
   "outputs": [
    {
     "data": {
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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>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8190.000000</td>\n",
       "      <td>8190.000000</td>\n",
       "      <td>8190.000000</td>\n",
       "      <td>4032.000000</td>\n",
       "      <td>2921.000000</td>\n",
       "      <td>3613.000000</td>\n",
       "      <td>3464.000000</td>\n",
       "      <td>4050.000000</td>\n",
       "      <td>7605.000000</td>\n",
       "      <td>7605.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>59.356198</td>\n",
       "      <td>3.405992</td>\n",
       "      <td>7032.371786</td>\n",
       "      <td>3384.176594</td>\n",
       "      <td>1760.100180</td>\n",
       "      <td>3292.935886</td>\n",
       "      <td>4132.216422</td>\n",
       "      <td>172.460809</td>\n",
       "      <td>7.826821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.987966</td>\n",
       "      <td>18.678607</td>\n",
       "      <td>0.431337</td>\n",
       "      <td>9262.747448</td>\n",
       "      <td>8793.583016</td>\n",
       "      <td>11276.462208</td>\n",
       "      <td>6792.329861</td>\n",
       "      <td>13086.690278</td>\n",
       "      <td>39.738346</td>\n",
       "      <td>1.877259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-7.290000</td>\n",
       "      <td>2.472000</td>\n",
       "      <td>-2781.450000</td>\n",
       "      <td>-265.760000</td>\n",
       "      <td>-179.260000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-185.170000</td>\n",
       "      <td>126.064000</td>\n",
       "      <td>3.684000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>45.902500</td>\n",
       "      <td>3.041000</td>\n",
       "      <td>1577.532500</td>\n",
       "      <td>68.880000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>304.687500</td>\n",
       "      <td>1440.827500</td>\n",
       "      <td>132.364839</td>\n",
       "      <td>6.634000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>60.710000</td>\n",
       "      <td>3.513000</td>\n",
       "      <td>4743.580000</td>\n",
       "      <td>364.570000</td>\n",
       "      <td>36.260000</td>\n",
       "      <td>1176.425000</td>\n",
       "      <td>2727.135000</td>\n",
       "      <td>182.764003</td>\n",
       "      <td>7.806000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>73.880000</td>\n",
       "      <td>3.743000</td>\n",
       "      <td>8923.310000</td>\n",
       "      <td>2153.350000</td>\n",
       "      <td>163.150000</td>\n",
       "      <td>3310.007500</td>\n",
       "      <td>4832.555000</td>\n",
       "      <td>213.932412</td>\n",
       "      <td>8.567000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>101.950000</td>\n",
       "      <td>4.468000</td>\n",
       "      <td>103184.980000</td>\n",
       "      <td>104519.540000</td>\n",
       "      <td>149483.310000</td>\n",
       "      <td>67474.850000</td>\n",
       "      <td>771448.100000</td>\n",
       "      <td>228.976456</td>\n",
       "      <td>14.313000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Store  Temperature   Fuel_Price      MarkDown1      MarkDown2  \\\n",
       "count  8190.000000  8190.000000  8190.000000    4032.000000    2921.000000   \n",
       "mean     23.000000    59.356198     3.405992    7032.371786    3384.176594   \n",
       "std      12.987966    18.678607     0.431337    9262.747448    8793.583016   \n",
       "min       1.000000    -7.290000     2.472000   -2781.450000    -265.760000   \n",
       "25%      12.000000    45.902500     3.041000    1577.532500      68.880000   \n",
       "50%      23.000000    60.710000     3.513000    4743.580000     364.570000   \n",
       "75%      34.000000    73.880000     3.743000    8923.310000    2153.350000   \n",
       "max      45.000000   101.950000     4.468000  103184.980000  104519.540000   \n",
       "\n",
       "           MarkDown3     MarkDown4      MarkDown5          CPI  Unemployment  \n",
       "count    3613.000000   3464.000000    4050.000000  7605.000000   7605.000000  \n",
       "mean     1760.100180   3292.935886    4132.216422   172.460809      7.826821  \n",
       "std     11276.462208   6792.329861   13086.690278    39.738346      1.877259  \n",
       "min      -179.260000      0.220000    -185.170000   126.064000      3.684000  \n",
       "25%         6.600000    304.687500    1440.827500   132.364839      6.634000  \n",
       "50%        36.260000   1176.425000    2727.135000   182.764003      7.806000  \n",
       "75%       163.150000   3310.007500    4832.555000   213.932412      8.567000  \n",
       "max    149483.310000  67474.850000  771448.100000   228.976456     14.313000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.681081Z",
     "start_time": "2020-02-16T20:23:04.649832Z"
    }
   },
   "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>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MarkDown2</th>\n",
       "      <td>5269</td>\n",
       "      <td>0.643346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown4</th>\n",
       "      <td>4726</td>\n",
       "      <td>0.577045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown3</th>\n",
       "      <td>4577</td>\n",
       "      <td>0.558852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown1</th>\n",
       "      <td>4158</td>\n",
       "      <td>0.507692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown5</th>\n",
       "      <td>4140</td>\n",
       "      <td>0.505495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unemployment</th>\n",
       "      <td>585</td>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPI</th>\n",
       "      <td>585</td>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsHoliday</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Price</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temperature</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Total   Percent\n",
       "MarkDown2      5269  0.643346\n",
       "MarkDown4      4726  0.577045\n",
       "MarkDown3      4577  0.558852\n",
       "MarkDown1      4158  0.507692\n",
       "MarkDown5      4140  0.505495\n",
       "Unemployment    585  0.071429\n",
       "CPI             585  0.071429\n",
       "IsHoliday         0  0.000000\n",
       "Fuel_Price        0  0.000000\n",
       "Temperature       0  0.000000\n",
       "Date              0  0.000000\n",
       "Store             0  0.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#missing data\n",
    "\n",
    "total = features.isnull().sum().sort_values(ascending=False)\n",
    "percent = (features.isnull().sum()/features.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data.head(20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:04.837586Z",
     "start_time": "2020-02-16T20:23:04.681081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of NaNs for all columns\n",
    "\n",
    "features_missing = features.isna().sum()/len(features) * 100\n",
    "\n",
    "#Visualize\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.yticks(np.arange(len(features_missing)),features_missing.index,rotation='horizontal')\n",
    "plt.xlabel('fraction of rows with missing data')\n",
    "plt.barh(np.arange(len(features_missing)), features_missing)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:05.275082Z",
     "start_time": "2020-02-16T20:23:04.837586Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6Yvm4JE9Ick1VfTDJU5PsdvEfAO6vhQ2ZVXVSVb29qvZW1c1V9eKp/RFVdVVVvX/6+fCp/ZSquraq7q2qlx2wrkural9VvXcerwUA5uD6JCdX1WOr6oFJzkmye/+D3X13dx/f3bu6e1eSdyZ5TnfvmU+5ACyLhQ2ZSe5L8tLufnxm/119wXRVvIuSXN3dJye5elpOkrsyuzreL6yyrtclOXPDKwaABdHd9yV5YZIrk+xNcnl331xVr6yq58y3OgCW2bZ5F3Aw3X1Hkjum+5+uqr2ZXbDg7CRnTN0uS3JNkpd3974k+6rqrFXW9WdVtWs9299WyY5j+/AdAZiLe+65Z94lLLzuviLJFQe0veIgfc/YjJoAWH6LvCfzC6aAeFpm3+H1qCmA7g+ijxy4nS9cPe++z9w9arUAAABbxsLuydyvqrYneXOSC7v7U1WrXSxvjO6+JMklSfKgnSf3nZ/duG0BcP9s37593iUAAKtY6D2ZVfWAzALmG7r7LVPzx6tq5/T4ziT75lUfAAAAX2xhQ2bNdlm+Nsne7n71iod2Jzlvun9ekrdudm0AAACsbmFDZpKnJXlekm+rqhun27OTXJzk26vq/Um+fVpOVT26qm5P8pIkP1lVt1fVQ6bH3pjk2iRfO7WfP48XBAAAsOwW9pzM7n5HkoOdFPn0Vfp/LLMvml5tXecOLA0AAICDWOQ9mQAAABxlhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhm27wLWFRPPOGh2XPxWfMuAwAA4KhiTyYAAADDCJkAAAAMI2QCAAAwjJAJAADAMEImAAAAwwiZAAAADCNkAgAAMIyQCQAAwDBCJgAAAMMImQAAAAwjZAIAADCMkAkAAMAwQiYAAADDbJt3AYvqpo/cnV0XvW3eZQBwEB+8+Kx5lwAArMKeTAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEgCVVVWdW1a1VdVtVXbTK4y+pqluq6j1VdXVVfdU86gRguQiZALCEquqYJK9J8qwkpyY5t6pOPaDbu5Kc3t1fl+R3k/zc5lYJwDISMgFgOT0lyW3d/YHu/qckb0py9soO3f327v7MtPjOJCduco0ALCEhEwCW0wlJPrxi+fap7WDOT/JHG1oRAFvCtnkXAABsiFqlrVftWPV9SU5P8m8P8vgFSS5Iksc85jGj6gNgSdmTCQDL6fYkJ61YPjHJRw/sVFXPSPITSZ7T3feutqLuvqS7T+/u03fs2LEhxQKwPIRMAFhO1yc5uaoeW1UPTHJOkt0rO1TVaUl+LbOAuW8ONQKwhIRMAFhC3X1fkhcmuTLJ3iSXd/fNVfXKqnrO1O3nk2xP8jtVdWNV7T7I6gBgzZyTCQBLqruvSHLFAW2vWHH/GZteFABLz55MAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGWdiQWVUnVdXbq2pvVd1cVS+e2h9RVVdV1funnw+f2k+pqmur6t6qetkB6zqzqm6tqtuq6qJ5vB4AAICtYGFDZpL7kry0ux+f5KlJXlBVpya5KMnV3X1ykqun5SS5K8mLkvzCypVU1TFJXpPkWUlOTXLutB4AAAAG2zbvAg6mu+9Icsd0/9NVtTfJCUnOTnLG1O2yJNckeXl370uyr6rOOmBVT0lyW3d/IEmq6k3TOm451Pa3VbLj2B7zYgAY7p577pl3CQDAKhZ5T+YXVNWuJKcluS7Jo6YAuj+IPvIwTz8hyYdXLN8+ta22nQuqak9V7bnvM3ff37IBAAC2nIXdk7lfVW1P8uYkF3b3p6pq3atYpW3VXZTdfUmSS5LkQTtP7js/u+5tAbBJtm/fPu8SAIBVLPSezKp6QGYB8w3d/Zap+eNVtXN6fGeSfYdZze1JTlqxfGKSj46uFQAAgAUOmTXbZfnaJHu7+9UrHtqd5Lzp/nlJ3nqYVV2f5OSqemxVPTDJOdM6AAAAGGyRD5d9WpLnJbmpqm6c2n48ycVJLq+q85N8KMlzk6SqHp1kT5KHJPl8VV2Y5NTpENsXJrkyyTFJLu3umzf3pQAAAGwNCxsyu/sdWf18yiR5+ir9P5bZobCrreuKJFeMqw4AAIDVLOzhsgAAABx9hEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhm27wLWFRPPOGh2XPxWfMuAwAA4KhiTyYAAADDCJkAAAAMI2QCAAAwjJAJAADAMEImAAAAwwiZAAAADCNkAgAAMIyQCQAAwDBCJgAAAMMImQCwpKrqzKq6tapuq6qLVnn8QVX129Pj11XVrs2vEoBlI2QCwBKqqmOSvCbJs5KcmuTcqjr1gG7nJ/lkd39Nkl9M8rObWyUAy0jIBIDl9JQkt3X3B7r7n5K8KcnZB/Q5O8ll0/3fTfL0qqpNrBGAJbRt3gUsqhtuuOGeqrp13nUcZY5P8ol5F3EUMm7rZ8zWz5gd3FfNu4ANckKSD69Yvj3JNx2sT3ffV1V3J/nKHPBeqaoLklwwLd5bVe/dkIqXk8/e+hiv9TNm62O81udrj+RJQubB3drdp8+7iKNJVe0xZutn3NbPmK2fMduSVtsj2UfQJ919SZJLEu+l9TJe62O81s+YrY/xWp+q2nMkz3O4LAAsp9uTnLRi+cQkHz1Yn6raluShSe7alOoAWFpCJgAsp+uTnFxVj62qByY5J8nuA/rsTnLedP+7kvxJd3/JnkwAWA+Hyx7cJfMu4ChkzI6McVs/Y7Z+xmyLmc6xfGGSK5Mck+TS7r65ql6ZZE93707y2iSvr6rbMtuDec4aVu29tD7Ga32M1/oZs/UxXutzRONV/mEJAADAKA6XBQAAYBghEwAAgGGEzANU1ZlVdWtV3VZVF827nkVSVR+sqpuq6sb9lzOuqkdU1VVV9f7p58On9qqqX5rG8T1V9eT5Vr95qurSqtq38nvkjmScquq8qf/7q+q81ba1LA4yZj9dVR+Z3m83VtWzVzz2n6cxu7WqvmNF+5b6/FbVSVX19qraW1U3V9WLp3bvN+63w32equpBVfXb0+PXVdWuza9ycaxhvF5SVbdMn72rq2pZv591Tdb6+7qqvququqq29FdOrGW8quq7p/fYzVX1W5td4yJZw+fxMdP8+a7pM/ns1dazVaz2d9gBj6//7/rudptumV0Y4X8n+eokD0zy7iSnzruuRbkl+WCS4w9o+7kkF033L0rys9P9Zyf5o8y+g+2pSa6bd/2bOE7fkuTJSd57pOOU5BFJPjD9fPh0/+Hzfm2bPGY/neRlq/Q9dfpsPijJY6fP7DFb8fObZGeSJ0/3j0vy19P4eL+53d/31mE/T0l+JMmvTvfPSfLb8657wcfrW5N8xXT/h43X4X9fT7/X/izJO5OcPu+6F3m8kpyc5F37f3cneeS8617w8bokyQ9P909N8sF51z3nMfuSv8MOeHzdf9fbk/nFnpLktu7+QHf/U5I3JTl7zjUturOTXDbdvyzJd65o/82eeWeSh1XVznkUuNm6+8/ypd8zt95x+o4kV3X3Xd39ySRXJTlz46ufj4OM2cGcneRN3X1vd/9Nktsy++xuuc9vd9/R3X813f90kr1JToj3G/ffWj5PK99nv5vk6VVVm1jjIjnseHX327v7M9PiOzP73tKtaq2/r1+V2T/NPruZxS2gtYzXDyV5zfQ7PN29b5NrXCRrGa9O8pDp/kPzpd8hvKWs4e+wdf9dL2R+sROSfHjF8u1TGzOd5I+r6oaqumBqe1R335HM/uBN8sip3Vh+sfWOk/GbeeF0WMal+w/5jDFb1XSo4mlJrov3G/ffWt4TX+jT3fcluTvJV25KdYtnvZ+h8zPbK7BVHXa8quq0JCd19x9uZmELai3vr8cleVxV/XlVvbOqtvI/CtcyXj+d5Puq6vYkVyT50c0p7ai17r8ThMwvttp/YH3Hy794Wnc/Ocmzkrygqr7lEH2N5docbJyMX/IrSf5VkicluSPJf53ajdkBqmp7kjcnubC7P3Worqu0bemx46DW8p7wvvkXax6Lqvq+JKcn+fkNrWixHXK8qurLkvxikpduWkWLbS3vr22ZHTJ7RpJzk/xGVT1sg+taVGsZr3OTvK67T8zsUNDXT+87Vrfu3/cG84vdnuSkFcsnZovvPl+puz86/dyX5PcyOxzh4/t3l08/9x+eYSy/2HrHacuPX3d/vLs/192fT/Lrmb3fEmP2RarqAZkFzDd091umZu837q+1vCe+0KeqtmV2yNlaD3tfNmv6DFXVM5L8RJLndPe9m1TbIjrceB2X5AlJrqmqD2Z2DtjuLXzxn7V+Ht/a3f88nUpya2ahcytay3idn+TyJOnua5Mcm+T4Tanu6LTuvxOEzC92fZKTq+qxVfXAzC5ksHvONS2EqnpwVR23/36SZyZ5b2bjs/9KlOcleet0f3eS75+uRvXUJHfvP3xvi1rvOF2Z5JlV9fDpMNFnTm1bxgHH+v+7zN5vyWzMzpmubPnYzCbRv8wW/PxO57+9Nsne7n71ioe837i/1vJ5Wvk++64kf9LTFSK2oMOO13T4569lFjC38vlyyWHGq7vv7u7ju3tXd+/K7BzW53T3nvmUO3dr+Tz+fmYXl0pVHZ/Z4bMf2NQqF8daxutDSZ6eJFX1+MxC5p2bWuXRZd1/12/bnLqODt19X1W9MLM/ro5Jcml33zznshbFo5L83nRNh21Jfqu7/2dVXZ/k8qo6P7MP7HOn/ldkdvjBbUk+k+QHNr/k+aiqN2Z2uMrx07H+P5Xk4qxjnLr7rqp6VWa/KJPkld29tHsIDjJmZ1TVkzI7HOODSf5jknT3zVV1eZJbktyX5AXd/blpPVvt8/u0JM9LclNV3Ti1/Xi837ifDjYfVtUrk+zp7t2Z/YPj9VV1W2Z7MM+ZX8Xztcbx+vkk25P8zjSXfqi7nzO3oudojePFZI3jtf+fhbck+VySH+vuv5tf1fOzxvF6aZJfr6r/lNnfGc/fwv8kO9jfYQ9Iku7+1RzB3/W1hccTAACAwRwuCwAAwDBCJgAAAMMImQAAAAwjZAIAADCMkAkAAMAwQiYsqKr6L1V1RlV9Z1VdtM7n7qiq66rqXVX1bw547JqqurWq3l1V109fE7Jpqup1VfVdm7lNAAA2j5AJi+ubklyX5N8m+V/rfO7Tk7yvu0/r7tWe+73d/fVJ/kdm3922IarKd/ECAGwxQiYsmKr6+ap6T5JvTHJtkv8nya9U1StW6ftVVXV1Vb1n+vmYac/kzyV5dlXdWFVffojNXZvkhBXre2ZVXVtVf1VVv1NV26f2b6yqv5j2fv5lVR1XVcdW1f9XVTdNe0y/der7/Om5f5Dkj2vml6vqlqp6W5JHrtjexVP7e6rqF+7/6AEAMG/2MsCC6e4fq6rfSfK8JC9Jck13P+0g3X85yW9292VV9YNJfqm7v3MKpKd39wsPs7kzk/x+klTV8Ul+MskzuvsfqurlSV5SVRcn+e0k39Pd11fVQ5L8Y5IXT/U+sapOySxQPm5a7zcn+bruvquq/n2Sr03yxCSPSnJLkkur6hFJ/l2SU7q7q+ph6x0rAAAWj5AJi+m0JDcmOSWzUHYw35zk30/3X5/ZHsy1eENVPTjJMUmePLU9NcmpSf68qpLkgZnt6fzaJHd09/VJ0t2fSpKq+j+T/Pep7X1V9bdJ9ofMq7r7run+tyR5Y3d/LslHq+pPpvZPJflskt+Y9nD+4RprBwBggQmZsECmQ11fl+TEJJ9I8hWz5roxyTd39z8eZhW9xk19b5J3J7k4yWsyC6qVWTg890Ib3pQAACAASURBVICavu4g661DrP8fDldXd99XVU/J7PzRc5K8MMm3rbF+AAAWlHMyYYF0943d/aQkf53ZXsU/SfId3f2kgwTMv8gsoCWz4PiOdWzrnzM7PPapVfX4JO9M8rSq+pokqaqvmA5/fV+S/6OqvnFqP266oM+fTdvM1O8xSW5dZVN/luScqjqmqnYm2X/u5vYkD+3uK5JcmGRTr3ILAMDGsCcTFkxV7Ujyye7+fFWd0t2HOlz2RZmd3/hjSe5M8gPr2VZ3/2NV/dckL+vu86vq+UneWFUPmrr8ZHf/dVV9T5L/Pl1E6B+TPCOzK9P+alXdlOS+JM/v7nunQ21X+r3M9lDelFl4/tOp/bgkb62qYzPbK/qf1lM7AACLqbrXenQdAAAAHJrDZQEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACG2TbvAhbV8ccf37t27Zp3GQBssBtuuOET3b1j3nUcLcyPAFvHkc6RQuZB7Nq1K3v27Jl3GQBssKr623nXcDQxPwJsHUc6RzpcFgAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYJht8y5gUd30kbuz66K3zbsMYAv74MVnzbsE+BLmR2DezI+Lz55MAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYZsNCZlWdVFVvr6q9VXVzVb14an9EVV1VVe+ffj58aj+lqq6tqnur6mUr1nNsVf1lVb17Ws/PHGKb503rfX9VnTe1HVdVN664faKq/ttGvW4AOBTzIwDLbtsGrvu+JC/t7r+qquOS3FBVVyV5fpKru/viqrooyUVJXp7kriQvSvKdB6zn3iTf1t33VNUDkryjqv6ou9+5slNVPSLJTyU5PUlP29vd3Z9M8qQV/W5I8pYNeL0AsBbmRwCW2oaFzO6+I8kd0/1PV9XeJCckOTvJGVO3y5Jck+Tl3b0vyb6qOuuA9XSSe6bFB0y3XmWT35Hkqu6+K0mmCfvMJG/c36GqTk7yyCT/63D1b6tkx7GrbQZgc9xzzz2H78RRx/wIcP+YHxffppyTWVW7kpyW5Lokj5om2P0T7SPX8PxjqurGJPsymyivW6XbCUk+vGL59qltpXOT/PY0Ma+2nQuqak9V7bnvM3cfriwAuF/MjwAso408XDZJUlXbk7w5yYXd/amqWvc6uvtzSZ5UVQ9L8ntV9YTufu+Bm1rtqQcsn5PkeYfYziVJLkmSB+08ue/87PprBRhl+/bt8y6BDWR+BDgy5sfFt6F7MqdzRN6c5A3dvf88j49X1c7p8Z2Z/fd1Tbr77zM7fOjMqvqmFRcreE5m/5k9aUX3E5N8dEUtX59kW3ffcH9eEwDcX+ZHAJbZRl5dtpK8Nsne7n71iod2Jzlvun9ekrceZj07pv/Qpqq+PMkzkryvu6/r7idNt91JrkzyzKp6+HRFvmdObfudmxXnnwDAPJgfAVh2G3m47NMyO/Tmpul8kST58SQXJ7m8qs5P8qEkz02Sqnp0kj1JHpLk81V1YZJTk+xMcllVHZNZKL68u//wwI11911V9aok109Nr9x/kYPJdyd59uDXCADrZX4EYKlt5NVl35HVzwNJkqev0v9jmR3Cc6D3ZHZRhLVs89Iklx7ksa9eyzoAYCOZHwFYdptydVkAAAC2BiETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGCYbfMuYFE98YSHZs/FZ827DABYKOZHAA7HnkwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGGbbvAtYVDd95O7suuht8y4D2MI+ePFZ8y4BvoT5EZg38+PisycTAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYYRMAAAAhhEyAQAAGEbIBAAAYBghEwAAgGGETAAAAIYRMgEAABhGyAQAAGAYIRMAAIBhhEwAAACGETIBAAAYRsgEAABgGCETAACAYbattWNV/esku1Y+p7t/cwNqAoCjijkSAP7FmkJmVb0+yb9KcmOSz03NncQECsCWZo4EgC+21j2Zpyc5tbt7I4sBgKOQORIAVljrOZnvTfLojSwEAI5S5kgAWOGQezKr6g8yO+TnuCS3VNVfJrl3/+Pd/ZyNLQ8AFpM5EgBWd7jDZX9hU6oAgKOPORIAVnHIkNndf5okVfWz3f3ylY9V1c8m+dMNrA0AFpY5EgBWt9ZzMr99lbZnjSwEAI5S5kgAWOFw52T+cJIfSfLVVfWeFQ8dl+QvNrIwAFhk5kgAWN3h9mT+VpL/K8nu6ef+2zd09/ce6olVdVJVvb2q9lbVzVX14qn9EVV1VVW9f/r58Kn9lKq6tqruraqXrVjPsVX1l1X17mk9P3OIbZ43rff9VXXeivYHVtUlVfXXVfW+qvoPh3ndAHA4RzRHmh8BWHaHOyfz7iR3Jzm3qo5J8qjpOdurant3f+gQT78vyUu7+6+q6rgkN1TVVUmen+Tq7r64qi5KclGSlye5K8mLknznAeu5N8m3dfc9VfWAJO+oqj/q7neu7FRVj0jyU5l9X1lP29vd3Z9M8hNJ9nX346rqy5I84nADAwCHcj/mSPMjAEvtcFeXTZJU1QuT/HSSjyf5/NTcSb7uYM/p7juS3DHd/3RV7U1yQpKzk5wxdbssyTVJXt7d+5Lsq6qzDlhPJ7lnWnzAdFvtC6+/I8lV3X3XVPNVSc5M8sYkP5jklGl9n0/yibW8bgA4nPXOkeZHAJbdmkJmkguTfG13/92RbKSqdiU5Lcl1SR41TbDp7juq6pFreP4xSW5I8jVJXtPd163S7YQkH16xfHuSE6rqYdPyq6rqjCT/O8kLu/vjh9rmtkp2HLvaXA2wOe65557Dd2IRHPEcaX4EWD/z4+Jb69VlP5zZIUHrVlXbk7w5yYXd/akjWUd3f667n5TkxCRPqaonrLap1Z6aWZA+Mcmfd/eTk1ybg3y3WVVdUFV7qmrPfZ85opcLwNZzRHOk+RGAZbXWPZkfSHJNVb0ts3NAkiTd/epDPWk6R+TNSd7Q3W+Zmj9eVTun/9LuTLJvrcV2999X1TVJzqyqByf5temhV2T2n9kzVnQ/MbNDjf4uyWeS/N7U/jtJzj/I+i9JckmSPGjnyX3nZ1eblwE2x/bt2+ddAmuz7jnS/Ahw5MyPi2+tezI/lOSqJA/M7NLs+28HVVWV5LVJ9h4w0e5Osv/Kducleeth1rNj/yE9VfXlSZ6R5H3dfV13P2m67U5yZZJnVtXDpyvyPTPJldM5K3+Qf5lgn57klrW9bAA4rHXNkeZHAJbdmvZkdvfPJMl0Fbzu7rUcCP20JM9LclNV3Ti1/XiSi5NcXlXnZzYxP3da96OT7EnykCSfr6oLk5yaZGeSy6bzTr4syeXd/Yer1HhXVb0qyfVT0yv3X+Qgs6vzvb6q/luSO5P8wFpeNwAczhHMkeZHAJbaWq8u+4Qkr890afOq+kSS7+/umw/2nO5+R1Y/DySZ/bf0wP4fy+wQngO9J7OLIhxWd1+a5NJV2v82ybesZR0AsB7rnSPNjwAsu7UeLntJkpd091d191cleWmSX9+4sgDgqGGOBIAV1hoyH9zdb9+/0N3XJHnwhlQEAEcXcyQArLDmq8tW1f+b2eFASfJ9Sf5mY0oCgKOKORIAVljrnswfTLIjyVsyu9T5jrg4AAAk5kgA+CJrvbrsJ5O8aINrAYCjjjkSAL7YIUNmVe0+1OPd/Zyx5QDA0cEcCQCrO9yezG9O8uEkb0xyXQ5+yXUA2GrMkQCwisOFzEcn+fYk5yb5v5O8LckbD/X9mACwRZgjAWAVh7zwT3d/rrv/Z3efl+SpSW5Lck1V/eimVAcAC8ocCQCrO+yFf6rqQUnOyuw/tbuS/FJmV9ADgC3NHAkAX+pwF/65LMkTkvxRkp/p7vduSlUAsODMkQCwusPtyXxekn9I8rgkL6r6wjUNKkl390M2sDYAWGTmSABYxSFDZncf8pxNANiqzJEAsDoTJAAAAMMImQAAAAwjZAIAADCMkAkAAMAwQiYAAADDCJkAAAAMI2QCAAAwjJAJAADAMEImAAAAwwiZAAAADCNkAgAAMIyQCQAAwDBCJgAAAMMImQAAAAwjZAIA8P+3d/fRklXlnce/v3SDIBAUeUmnxWC0EyREW6JA1DgYjaLMiM5oxKBCYkKcgVEyuiYkMdGJa01INHENvk10xEYGO4MBtFGDEgQRRKANDQ0CAwuIggQERqQ1QYFn/jj7hupL1X3pOrfr3u7vZ61aVWfXedm176566tm1z7mS1BuTTEmSJElSb0wyJUmSJEm9McmUJEmSJPXGJFOSJEmS1BuTTEmSJElSb5ZPugKL1S+u3J31Jx8x6WpIkrSoGB8lSbPxl0xJkiRJUm9MMiVJkiRJvTHJlCRJkiT1xiRTkiRJktQbk0xJkiRJUm9MMiVJkiRJvTHJlCRJkiT1xiRTkiRJktQbk0xJkiRJUm9MMiVJkiRJvTHJlCRJkiT1xiRTkiRJktQbk0xJkiRJUm9MMiVJkiRJvTHJlCRJkiT1xiRTkiRJktQbk0xJkiRJUm9MMiVJkiRJvVk+6QosVhvvuJ/9Tvr8pKshSRrhtpOPmHQVtkvGR0la/CYdI/0lU5IkSZLUG5NMSZIkSVJvTDIlSZIkSb0xyZQkSZIk9cYkU5IkSZLUG5NMSZIkSVJvTDIlSZIkSb0xyZQkSZIk9cYkU5IkSZLUG5NMSZIkSVJvTDIlSZIkSb0xyZQkSZIk9cYkU5IkSZLUG5NMSZIkSVJvTDIlSZIkSb0xyZQkSZIk9cYkU5IkSZLUG5NMSZIkSVJvTDIlSZIkSb0xyZQkSZIk9cYkU5IkSZLUG5NMSZIkSVJvTDIlSZIkSb0xyZQkSZIk9cYkU5IkSZLUG5NMSZIkSVJvTDIlSZIkSb0xyZQkSZIk9cYkU5IkSZLUG5NMSZIkSVJvTDIlSZIkSb0xyZQkSZIk9cYkU5IkSZLUG5NMSZIkSVJvTDIlSZIkSb1ZsCQzyb5JLkxyfZLrkrytle+R5PwkN7X7J7by/ZNcluTBJO8Y2M9OSa5IcnXbz3+b4ZjHtP3elOSYgfKLktyYZEO77b1Qr1uSpJkYHyVJ27qF/CXzIeDtVfUM4FDg+CQHACcBF1TVKuCCtgxwH/BW4H3T9vMg8KtV9SxgNXB4kkOnHyzJHsC7gEOAg4F3TQXo5uiqWt1ud/f2KiVJmh/joyRpm7Z8oXZcVXcCd7bHDyS5HlgJHAkc1lY7DbgI+P0W2O5OcsS0/RSwqS3u0G415JAvA86vqvsAkpwPHA6s3ZL6Lw/stdOww0iSFoNNmzbNvtIiZHyUJC20ScfIrXJOZpL9gGcDlwP7tAA7FWhnnZqTZFmSDcDddIHy8iGrrQS+PbB8eyub8ok2FeiPk2TEcY5Lsj7J+od+eP8cXpkkSVvO+ChJ2hYt2C+ZU5LsCpwFnFhV3x8Rv2ZUVQ8Dq5M8ATgnyYFVde30Qw3btN0fXVV3JNmt1eWNwCeHHOejwEcBHrdiVX33X+ZfV0nS1rHrrrtOugpjMT5KkhbKpGPkgv6SmWQHuqB1RlWd3YrvSrKiPb+CbvR1Tqrqe3TThw5PcsjAhQpeSTcyu+/A6k8GvtO2u6PdPwB8iu6cFEmSJsL4KEnali3k1WUDfBy4vqr+auCpdcDUle2OAT47y372aiO0JNkZeAlwQ1VdPnChgnXAF4GXJnliu6DBS4EvJlmeZM+2/Q7AvwWmj/JKkrRVGB8lSdu6hZwu+3y6aTcb2/kiAH8InAycmeTNwLeA1wIk+SlgPfCTwCNJTgQOAFYApyVZRpcUn1lVn5t+sKq6L8l7gCtb0Z+2sl3ogukOwDLg74GPLcgrliRpdsZHSdI2bSGvLnsJw88DAXjxkPX/iW4Kz3TX0F0UYS7HPBU4dVrZD4Bfmsv2kiQtNOOjJGlbt1WuLitJkiRJ2j6YZEqSJEmSemOSKUmSJEnqjUmmJEmSJKk3JpmSJEmSpN6YZEqSJEmSemOSKUmSJEnqjUmmJEmSJKk3JpmSJEmSpN6YZEqSJEmSemOSKUmSJEnqjUmmJEmSJKk3JpmSJEmSpN6YZEqSJEmSemOSKUmSJEnqjUmmJEmSJKk3JpmSJEmSpN6YZEqSJEmSemOSKUmSJEnqjUmmJEmSJKk3JpmSJEmSpN6YZEqSJEmSemOSKUmSJEnqjUmmJEmSJKk3JpmSJEmSpN6YZEqSJEmSemOSKUmSJEnqjUmmJEmSJKk3JpmSJEmSpN6YZEqSJEmSemOSKUmSJEnqjUmmJEmSJKk3yyddgcXqF1fuzvqTj5h0NSRJWlSMj5Kk2fhLpiRJkiSpNyaZkiRJkqTemGRKkiRJknpjkilJkiRJ6o1JpiRJkiSpNyaZkiRJkqTemGRKkiRJknpjkilJkiRJ6o1JpiRJkiSpNyaZkiRJkqTemGRKkiRJknpjkilJkiRJ6o1JpiRJkiSpN6mqSddhUUryAHDjpOuxhO0J3DPpSixxtuF4bL/xbS9t+DNVtdekK7FUGB97sb28txaK7Tc+23A821P7bVGMXL4QNdlG3FhVz5l0JZaqJOttv/HYhuOx/cZnG2oE4+OYfG+Nx/Ybn204Httvdk6XlSRJkiT1xiRTkiRJktQbk8zRPjrpCixxtt/4bMPx2H7jsw01jP1ifLbheGy/8dmG47H9ZuGFfyRJkiRJvfGXTEmSJElSb0wyJUmSJEm9McmcJsnhSW5McnOSkyZdn6UiyW1JNibZkGR9K9sjyflJbmr3T5x0PReLJKcmuTvJtQNlQ9srnVNan7wmyUGTq/niMaIN353kjtYPNyR5xcBzf9Da8MYkL5tMrRePJPsmuTDJ9UmuS/K2Vm4/1EjGyPkzPs6fMXI8xsfxGSPHZ5I5IMky4EPAy4EDgNcnOWCytVpSXlRVqwf+b9BJwAVVtQq4oC2rswY4fFrZqPZ6ObCq3Y4DPrKV6rjYreGxbQjw/tYPV1fVFwDa+/go4BfaNh9u7/ft2UPA26vqGcChwPGtneyHGsoYORbj4/yswRg5jjUYH8dljByTSebmDgZurqpbqupHwN8AR064TkvZkcBp7fFpwKsmWJdFpaouBu6bVjyqvY4EPlmdrwNPSLJi69R08RrRhqMcCfxNVT1YVbcCN9O937dbVXVnVf1De/wAcD2wEvuhRjNG9sf4OANj5HiMj+MzRo7PJHNzK4FvDyzf3so0uwK+lOQbSY5rZftU1Z3QvVmBvSdWu6VhVHvZL+fnhDZV5dSBKWi24QyS7Ac8G7gc+6FGsw9sGeNjP/xsGp/xcQsYI7eMSebmMqTM//EyN8+vqoPopgscn+SFk67QNsR+OXcfAZ4GrAbuBP6ylduGIyTZFTgLOLGqvj/TqkPKbMPti31gyxgfF5b9cm6Mj1vAGLnlTDI3dzuw78Dyk4HvTKguS0pVfafd3w2cQzfV4q6pqQLt/u7J1XBJGNVe9ss5qqq7qurhqnoE+BiPTvmxDYdIsgNd8Dyjqs5uxfZDjWIf2ALGx9742TQG4+P8GSPHY5K5uSuBVUmemmRHuhOh1024Totekl2S7Db1GHgpcC1d2x3TVjsG+OxkarhkjGqvdcCb2pXLDgXun5qqoc1NO//h1XT9ELo2PCrJ45I8le7E/Cu2dv0WkyQBPg5cX1V/NfCU/VCjGCPnyfjYKz+bxmB8nB9j5PiWT7oCi0lVPZTkBOCLwDLg1Kq6bsLVWgr2Ac7p3o8sBz5VVecluRI4M8mbgW8Br51gHReVJGuBw4A9k9wOvAs4meHt9QXgFXQn4/8Q+M2tXuFFaEQbHpZkNd0UlduA3wWoquuSnAl8k+6KccdX1cOTqPci8nzgjcDGJBta2R9iP9QIxsgtYnzcAsbI8Rgfe2GMHFOqtuvpwpIkSZKkHjldVpIkSZLUG5NMSZIkSVJvTDIlSZIkSb0xyZQkSZIk9cYkU5IkSZLUG5NMzSrJnyU5LMmrkpw0z233SnJ5kquS/Mq05y5KcmOSq5Nc2S6tvdUkWZPkNT3t6zlJTmmPD0vyvPkeJ0klOX1geXmS7yb5XFt+5Xzbv233tfluM2QfuyS5N8nu08o/k+TXZ9jusKn6b21Jbkuy52KoSzv+pkkde5gk+yX5jUnXQ9L8GZfntC/j8vDtJhmXd0hycpKbklyb5IokL2/P3ZZkY+t7X0ryUwPle868Zy1GJpmai0OAy4F/A3x1ntu+GLihqp5dVcO2PbqqngV8GHjveNUcLcmC/k/YqlpfVW9ti4cBz5th9VF+AByYZOe2/GvAHQPHWFdVJ29B3bakLtP38QPgS8CrpspaYHsBMLHETWPZDzDJlJYm4/IsjMuL0nuAFcCBVXUg8O+A3Qaef1Hre+vp/ielljCTTI2U5L1JrgGeC1wG/DbwkSR/MmTdn0lyQZJr2v1T2gjoXwCvSLJh4EN6mMuAlQP7e2mSy5L8Q5JPJ9m1lT83ydfaSNcVSXZLslOST7QRsKuSvKite2zb9lzgS+l8MMk3k3we2HvgeCe38muSvG/I69uY5AltH/cmeVMrPz3JS6ZGBpPsB7wF+L32mqdGiV/Y6n3LLKOnfwcc0R6/Hlg7UIdjk3ywPX5tGwW8OsnFrewXWptsaK9jVSvf1O4Pa6PUf5vkhiRnJN1/CE/yilZ2SZJTRoxyrgWOGlh+NXBeVf0wycHt9V3V7n9+SBu+O8k7Bpavbe1FkjcM1P2vkyxrtzVtvY1Jfm+GdpuXVpdTW3vckuStA889pi6tfFOSP0/yjSR/317z1PavbOscm+SzSc5L92vAu4YcO+29NfW6XtfKT09y5MB6Z6QbJT823cj0uUluTXJCkv/S2vrrSfZo6z+tHfcbSb6aZP9Wvqb9Taf3v5OBX2mvs7e2lbRwYlwefH3G5SUUl5M8Hvgd4D9X1YMAVXVXVZ05ZPWLgafPdd9apKrKm7eRN+Bg4APADsClM6x3LnBMe/xbwGfa42OBD47Y5iLgOe3xicB/b4/3pPuA2aUt/z7wJ8COwC3Ac1v5TwLLgbcDn2hl+wPfAnZqx74d2KM99++B84FlwE8D3wNeA+wB3AikrfeEIXX9n3RB5kDgSuBjrfwmYFe6UdLPtbJ3A+8Y2HYN8Gm6QZ0DgJtHtMcm4JnA37b6b5i2339tS2AjsHKwvu3vdHR7vCOw89R+2/1hwP3Ak1tdLqMb8dwJ+Dbw1Lbe2qljTqvfjsDdwJPa8nnAEYN/i/b4JcBZA8cc1S7X0v2a9gy6/rNDK/8w8Cbgl4DzB9Z/zN9llr57G7DnwPL0unwNeBxdf7uXro8PrUt7XMDL2+Nz6EaQdwCeBWwY+BvdCTwJ2Lm9xqk+PvV3+A882g/3oeuvK+h+kZh63+wO3ErXv48FbqYb7d2r/Q3f0tZ7P3Bie3wBsKo9PgT48kz9b7A9vHnztnRuGJen6mpcXkJxubXjVTM8fxstZgMfBP58erm3pXVb0KkK2iY8m+5DdX/gmzOs98t0wQLgdLqR0rk4I8kudAHmoFZ2KN2H/qVtQG9Hug/enwfurKorAarq+wBJXkD3QU5V3ZDkH4Gfa/s6v6rua49fCKytqoeB7yT5civ/PvAvwP9qI6nDRgu/2rb/R+AjwHFJVgL3VdWmVs+ZfKaqHgG+mWSfUStV1TVtFPH1wBdm2N+lwJokZwJnt7LLgD9K8mTg7Kq6ach2V1TV7QBJNtAFk03ALVV1a1tnLXDckLr9KMk64DVJzgJW0yVa0CVFp7VR2qL78jNXL6YLXFe2dtyZLmieC/xskg8Anx841lzVLGWfr2409cEkd9MlfKPqAvAjugAO3ZeJB6vqx0k20rXjlPOr6l6AJGfTfWFYP/D8C3i0H96V5Ct0X9DWJflQkr3p3ktnVdVDrR4XVtUDwANJ7m9tM1WPZ7ZfFJ4HfHqgLz5u4Jhz6n+SlgTjcse4vPTi8mwuTPIwcA3wzp73ra3MJFNDpZtSs4ZudO0e4PFdcTYAv1xV/zzLLoZ9wR/maOBquql7H6ILiKELQq+fVqdnjtjvTJHkB7PVq32RP5juQ/UonjDmEgAABA1JREFU4ATgV6etdjFwPPAU4I/opqS8hrmfC/PgHOsLsA54H91o45OGrVBVb0lyCN0o7oYkq6vqU0kub2VfTPLbVfXlaZsO1uNhus+AWSPxgLV0H/wBPltVP27l76FLhF7dgvFFQ7Z9iM2n6O/U7gOcVlV/MH2DJM8CXkbX9r9ONxo/9dwy4BttcV1VTZ8udi/wRLr+C93I+D0Dz49qi6F1AX5cVVP955Gp7avqkWx+btH0PjZ9eab2Pp3uPXEUA691Wl0fGVh+pNX7J4DvVdWoi3TMp/9JWoSMy8blEZZKXL4ZeEqS3dqg6TAvqqp7RjynJcZzMjVUVW1oX1j/L93o5ZeBl1XV6hGB7Gs8el7A0cAl8zjWj+k+IA9N8gzg68Dzkzwdunn8SX4OuAH46STPbeW7tS/3F7dj0tZ7Ct00m+kuBo5q5xSsAKbOEdkV2L2qvkA3PegxX9Sr6tt004VWVdUt7fW9g+HB7AE2P5F9vk4F/rSqNo5aIcnTqury9gF+D7Bvkp+lG/k8hS4gPnOOx7uBbmRyv7b8uhnWvRBYRRdc1g6U786jF0M4dsS2t9FGxZMcBDy1lV9ANwq7d3tuj3TnEu0J/ERVnQX8MY+OqANQVQ+3/rh6SIIJXUB9Y9vnMuANrf4zGVqXWbaZ7tfadjvTXZDh0mnPXwy8rvXDvehG4q9oz62h64NU1XVzPWD79eDWJK9t9U77IjCTcfuppK3IuPyYOhqXO0siLlfVD4GPA6ck2bHtd0WSN8zw2rSEmWRqpPYF+P+16ST7V9VM03LeCvxmugsSvBF423yO1QLkX9KdG/Bdug/EtW1/X2/H/xHdB+0HklxNdx7HTnTnCixr0xb/D3BsmwY53Tl052pspJta85VWvhvwuXasrwCjTmS/nC64QxfEVjI8aJ8LvDqbX2Bgzqrq9qr6H7Os9t50J91fSxekr6Zrm2vbqPb+wCfneLx/Bv4TcF6SS4C76M4RGbbuI8BZdCO5Fw889RfAnyW5lG6K1TBnAXu0+v1HWlu2fvVOuotAXEP3d11B174XtfXXAMN+XZzJe4Cnt75yFd0o6v+eaYMZ6jIfl9D9IrmBbsrr+mnPn0M3Fehqui+J/7Wq/qkd/y7geuAT8zwmdF/o3txe73XAkbOsfw3wULqLVHjhH2kJMC4/hnF5acXldwLfpZuifC3wmbasbdDUCdWStmNJdm3nsIRuetRNVfX+SddrqUlyLN1FM07Ywu0fT/dl66CqGvqFQpK07TMua6nzl0xJAL/TRiavo5ti89cTrs92J8lL6KZIfcAEU5K2e8ZlLWn+kilJkiRJ6o2/ZEqSJEmSemOSKUmSJEnqjUmmJEmSJKk3JpmSJEmSpN6YZEqSJEmSevP/AbfhW9j/Hw99AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize = (15,12))\n",
    "\n",
    "# Plot 1: Year Vs # of Records\n",
    "ax[0,0].barh(features['Date'].str.slice(start=0, stop=4).unique(), \n",
    "          features['Date'].str.slice(start=0, stop=4).value_counts())\n",
    "ax[0,0].set_xlabel('# of Records')\n",
    "ax[0,0].set_ylabel('Year')\n",
    "ax[0,0].yaxis.grid(True, linewidth=0.3)\n",
    "\n",
    "# Plot 2: Month Vs # of Records with Missing Values - Unemployment\n",
    "ax[1,0].barh(features['Date'].str.slice(start=0, stop=7)[features['Unemployment'].isna()].unique(), \n",
    "          features['Date'].str.slice(start=0, stop=7)[features['Unemployment'].isna()].value_counts())\n",
    "ax[1,0].set_xlabel('# of Records with Missing Values - Unemployment')\n",
    "ax[1,0].set_ylabel('Month')\n",
    "ax[1,0].yaxis.grid(True, linewidth=0.3)\n",
    "\n",
    "# Plot 3: Month Vs # of Records with Missing Values - CPI\n",
    "ax[1,1].barh(features['Date'].str.slice(start=0, stop=7)[features['CPI'].isna()].unique(), \n",
    "          features['Date'].str.slice(start=0, stop=7)[features['CPI'].isna()].value_counts())\n",
    "ax[1,1].set_xlabel('# of Records with Missing Values - CPI')\n",
    "ax[1,1].set_ylabel('Month')\n",
    "ax[1,1].yaxis.grid(True, linewidth=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of records missing for various years. Followed by break down by month for Unemployment and CPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:05.290636Z",
     "start_time": "2020-02-16T20:23:05.275082Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "holidays = ['2010-02-12', '2011-02-11', '2012-02-10', '2013-02-08', #Super Bowl\n",
    "           '2010-09-10', '2011-09-09', '2012-09-07', '2013-02-06',  #Labor Day\n",
    "           '2010-11-26', '2011-11-25', '2012-11-23', '2013-11-29',  #Thanksgiving\n",
    "           '2010-12-31', '2011-12-30', '2012-12-28', '2013-12-27']  #Christmas\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:05.306259Z",
     "start_time": "2020-02-16T20:23:05.290636Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    585\n",
       "Name: IsHoliday, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Validate Holidays\n",
    "features['IsHoliday'][features['Date'].isin(holidays)].value_counts()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:05.321878Z",
     "start_time": "2020-02-16T20:23:05.306259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Date, dtype: int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features['Date'][features['IsHoliday'].isin([1])][~features['Date'].isin(holidays)].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:05.353091Z",
     "start_time": "2020-02-16T20:23:05.321878Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">1</th>\n",
       "      <th>2010-02</th>\n",
       "      <td>211.236828</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03</th>\n",
       "      <td>211.241116</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04</th>\n",
       "      <td>210.552135</td>\n",
       "      <td>7.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05</th>\n",
       "      <td>210.547812</td>\n",
       "      <td>7.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06</th>\n",
       "      <td>211.356237</td>\n",
       "      <td>7.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07</th>\n",
       "      <td>211.207476</td>\n",
       "      <td>7.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08</th>\n",
       "      <td>211.578688</td>\n",
       "      <td>7.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09</th>\n",
       "      <td>211.536531</td>\n",
       "      <td>7.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10</th>\n",
       "      <td>211.800525</td>\n",
       "      <td>7.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11</th>\n",
       "      <td>211.899611</td>\n",
       "      <td>7.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12</th>\n",
       "      <td>211.457702</td>\n",
       "      <td>7.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01</th>\n",
       "      <td>211.721611</td>\n",
       "      <td>7.742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02</th>\n",
       "      <td>213.071770</td>\n",
       "      <td>7.742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03</th>\n",
       "      <td>214.224260</td>\n",
       "      <td>7.742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04</th>\n",
       "      <td>215.258255</td>\n",
       "      <td>7.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05</th>\n",
       "      <td>215.749441</td>\n",
       "      <td>7.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06</th>\n",
       "      <td>215.101584</td>\n",
       "      <td>7.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07</th>\n",
       "      <td>215.345630</td>\n",
       "      <td>7.962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08</th>\n",
       "      <td>215.638236</td>\n",
       "      <td>7.962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09</th>\n",
       "      <td>216.157134</td>\n",
       "      <td>7.962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10</th>\n",
       "      <td>217.398324</td>\n",
       "      <td>7.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11</th>\n",
       "      <td>218.130899</td>\n",
       "      <td>7.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>219.149949</td>\n",
       "      <td>7.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01</th>\n",
       "      <td>219.917832</td>\n",
       "      <td>7.348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02</th>\n",
       "      <td>220.374964</td>\n",
       "      <td>7.348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03</th>\n",
       "      <td>221.153294</td>\n",
       "      <td>7.348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04</th>\n",
       "      <td>221.531958</td>\n",
       "      <td>7.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05</th>\n",
       "      <td>221.721270</td>\n",
       "      <td>7.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06</th>\n",
       "      <td>221.781152</td>\n",
       "      <td>7.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07</th>\n",
       "      <td>221.920490</td>\n",
       "      <td>6.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">2</th>\n",
       "      <th>2011-02</th>\n",
       "      <td>212.727665</td>\n",
       "      <td>8.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03</th>\n",
       "      <td>213.877488</td>\n",
       "      <td>8.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04</th>\n",
       "      <td>214.908582</td>\n",
       "      <td>7.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05</th>\n",
       "      <td>215.398826</td>\n",
       "      <td>7.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06</th>\n",
       "      <td>214.754360</td>\n",
       "      <td>7.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07</th>\n",
       "      <td>214.998246</td>\n",
       "      <td>7.852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08</th>\n",
       "      <td>215.291750</td>\n",
       "      <td>7.852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09</th>\n",
       "      <td>215.810080</td>\n",
       "      <td>7.852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10</th>\n",
       "      <td>217.047711</td>\n",
       "      <td>7.441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11</th>\n",
       "      <td>217.777737</td>\n",
       "      <td>7.441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>218.792544</td>\n",
       "      <td>7.441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01</th>\n",
       "      <td>219.558147</td>\n",
       "      <td>7.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02</th>\n",
       "      <td>220.014484</td>\n",
       "      <td>7.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03</th>\n",
       "      <td>220.791516</td>\n",
       "      <td>7.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04</th>\n",
       "      <td>221.170021</td>\n",
       "      <td>6.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05</th>\n",
       "      <td>221.358968</td>\n",
       "      <td>6.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06</th>\n",
       "      <td>221.419302</td>\n",
       "      <td>6.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07</th>\n",
       "      <td>221.557961</td>\n",
       "      <td>6.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08</th>\n",
       "      <td>221.721490</td>\n",
       "      <td>6.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09</th>\n",
       "      <td>222.331393</td>\n",
       "      <td>6.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10</th>\n",
       "      <td>222.992375</td>\n",
       "      <td>6.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11</th>\n",
       "      <td>223.159882</td>\n",
       "      <td>6.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12</th>\n",
       "      <td>223.427985</td>\n",
       "      <td>6.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01</th>\n",
       "      <td>223.821473</td>\n",
       "      <td>6.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02</th>\n",
       "      <td>223.925989</td>\n",
       "      <td>6.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03</th>\n",
       "      <td>224.439928</td>\n",
       "      <td>6.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04</th>\n",
       "      <td>224.781713</td>\n",
       "      <td>6.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      CPI  Unemployment\n",
       "Store Date                             \n",
       "1     2010-02  211.236828         8.106\n",
       "      2010-03  211.241116         8.106\n",
       "      2010-04  210.552135         7.808\n",
       "      2010-05  210.547812         7.808\n",
       "      2010-06  211.356237         7.808\n",
       "      2010-07  211.207476         7.787\n",
       "      2010-08  211.578688         7.787\n",
       "      2010-09  211.536531         7.787\n",
       "      2010-10  211.800525         7.838\n",
       "      2010-11  211.899611         7.838\n",
       "      2010-12  211.457702         7.838\n",
       "      2011-01  211.721611         7.742\n",
       "      2011-02  213.071770         7.742\n",
       "      2011-03  214.224260         7.742\n",
       "      2011-04  215.258255         7.682\n",
       "      2011-05  215.749441         7.682\n",
       "      2011-06  215.101584         7.682\n",
       "      2011-07  215.345630         7.962\n",
       "      2011-08  215.638236         7.962\n",
       "      2011-09  216.157134         7.962\n",
       "      2011-10  217.398324         7.866\n",
       "      2011-11  218.130899         7.866\n",
       "      2011-12  219.149949         7.866\n",
       "      2012-01  219.917832         7.348\n",
       "      2012-02  220.374964         7.348\n",
       "      2012-03  221.153294         7.348\n",
       "      2012-04  221.531958         7.143\n",
       "      2012-05  221.721270         7.143\n",
       "      2012-06  221.781152         7.143\n",
       "      2012-07  221.920490         6.908\n",
       "...                   ...           ...\n",
       "2     2011-02  212.727665         8.028\n",
       "      2011-03  213.877488         8.028\n",
       "      2011-04  214.908582         7.931\n",
       "      2011-05  215.398826         7.931\n",
       "      2011-06  214.754360         7.931\n",
       "      2011-07  214.998246         7.852\n",
       "      2011-08  215.291750         7.852\n",
       "      2011-09  215.810080         7.852\n",
       "      2011-10  217.047711         7.441\n",
       "      2011-11  217.777737         7.441\n",
       "      2011-12  218.792544         7.441\n",
       "      2012-01  219.558147         7.057\n",
       "      2012-02  220.014484         7.057\n",
       "      2012-03  220.791516         7.057\n",
       "      2012-04  221.170021         6.891\n",
       "      2012-05  221.358968         6.891\n",
       "      2012-06  221.419302         6.891\n",
       "      2012-07  221.557961         6.565\n",
       "      2012-08  221.721490         6.565\n",
       "      2012-09  222.331393         6.565\n",
       "      2012-10  222.992375         6.170\n",
       "      2012-11  223.159882         6.170\n",
       "      2012-12  223.427985         6.170\n",
       "      2013-01  223.821473         6.237\n",
       "      2013-02  223.925989         6.237\n",
       "      2013-03  224.439928         6.237\n",
       "      2013-04  224.781713         6.112\n",
       "      2013-05         NaN           NaN\n",
       "      2013-06         NaN           NaN\n",
       "      2013-07         NaN           NaN\n",
       "\n",
       "[84 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[['CPI','Unemployment']].groupby([features['Store'], features['Date'].str.slice(start=0, stop=7)]).mean().head(84)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:05.384366Z",
     "start_time": "2020-02-16T20:23:05.353091Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2011-01</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11</th>\n",
       "      <td>134</td>\n",
       "      <td>124</td>\n",
       "      <td>124</td>\n",
       "      <td>110</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>220</td>\n",
       "      <td>169</td>\n",
       "      <td>218</td>\n",
       "      <td>192</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01</th>\n",
       "      <td>180</td>\n",
       "      <td>179</td>\n",
       "      <td>155</td>\n",
       "      <td>150</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02</th>\n",
       "      <td>180</td>\n",
       "      <td>179</td>\n",
       "      <td>159</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03</th>\n",
       "      <td>224</td>\n",
       "      <td>210</td>\n",
       "      <td>198</td>\n",
       "      <td>195</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04</th>\n",
       "      <td>180</td>\n",
       "      <td>122</td>\n",
       "      <td>170</td>\n",
       "      <td>155</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05</th>\n",
       "      <td>179</td>\n",
       "      <td>11</td>\n",
       "      <td>163</td>\n",
       "      <td>151</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06</th>\n",
       "      <td>224</td>\n",
       "      <td>172</td>\n",
       "      <td>160</td>\n",
       "      <td>192</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07</th>\n",
       "      <td>180</td>\n",
       "      <td>143</td>\n",
       "      <td>172</td>\n",
       "      <td>150</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08</th>\n",
       "      <td>222</td>\n",
       "      <td>168</td>\n",
       "      <td>212</td>\n",
       "      <td>207</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09</th>\n",
       "      <td>179</td>\n",
       "      <td>115</td>\n",
       "      <td>154</td>\n",
       "      <td>157</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10</th>\n",
       "      <td>178</td>\n",
       "      <td>45</td>\n",
       "      <td>161</td>\n",
       "      <td>146</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11</th>\n",
       "      <td>224</td>\n",
       "      <td>166</td>\n",
       "      <td>209</td>\n",
       "      <td>194</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12</th>\n",
       "      <td>180</td>\n",
       "      <td>67</td>\n",
       "      <td>168</td>\n",
       "      <td>153</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01</th>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>148</td>\n",
       "      <td>147</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02</th>\n",
       "      <td>180</td>\n",
       "      <td>179</td>\n",
       "      <td>155</td>\n",
       "      <td>167</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03</th>\n",
       "      <td>225</td>\n",
       "      <td>80</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04</th>\n",
       "      <td>180</td>\n",
       "      <td>132</td>\n",
       "      <td>160</td>\n",
       "      <td>150</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05</th>\n",
       "      <td>225</td>\n",
       "      <td>181</td>\n",
       "      <td>213</td>\n",
       "      <td>186</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06</th>\n",
       "      <td>179</td>\n",
       "      <td>148</td>\n",
       "      <td>154</td>\n",
       "      <td>153</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07</th>\n",
       "      <td>179</td>\n",
       "      <td>151</td>\n",
       "      <td>162</td>\n",
       "      <td>151</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5\n",
       "Date                                                          \n",
       "2010-02          0          0          0          0          0\n",
       "2010-03          0          0          0          0          0\n",
       "2010-04          0          0          0          0          0\n",
       "2010-05          0          0          0          0          0\n",
       "2010-06          0          0          0          0          0\n",
       "2010-07          0          0          0          0          0\n",
       "2010-08          0          0          0          0          0\n",
       "2010-09          0          0          0          0          0\n",
       "2010-10          0          0          0          0          0\n",
       "2010-11          0          0          0          0          0\n",
       "2010-12          0          0          0          0          0\n",
       "2011-01          0          0          0          0          0\n",
       "2011-02          0          0          0          0          0\n",
       "2011-03          0          0          0          0          0\n",
       "2011-04          0          0          0          0          0\n",
       "2011-05          0          0          0          0          0\n",
       "2011-06          0          0          0          0          0\n",
       "2011-07          0          0          0          0          0\n",
       "2011-08          0          0          0          0          0\n",
       "2011-09          0          0          0          0          0\n",
       "2011-10          0          0          0          0          0\n",
       "2011-11        134        124        124        110        135\n",
       "2011-12        220        169        218        192        225\n",
       "2012-01        180        179        155        150        180\n",
       "2012-02        180        179        159        160        180\n",
       "2012-03        224        210        198        195        225\n",
       "2012-04        180        122        170        155        180\n",
       "2012-05        179         11        163        151        180\n",
       "2012-06        224        172        160        192        225\n",
       "2012-07        180        143        172        150        180\n",
       "2012-08        222        168        212        207        225\n",
       "2012-09        179        115        154        157        180\n",
       "2012-10        178         45        161        146        180\n",
       "2012-11        224        166        209        194        225\n",
       "2012-12        180         67        168        153        180\n",
       "2013-01        180        180        148        147        180\n",
       "2013-02        180        179        155        167        180\n",
       "2013-03        225         80        198        198        225\n",
       "2013-04        180        132        160        150        180\n",
       "2013-05        225        181        213        186        225\n",
       "2013-06        179        148        154        153        180\n",
       "2013-07        179        151        162        151        180"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.groupby(features['Date'].str.slice(start=0, stop=7))['MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count of Markdown by Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:06.649997Z",
     "start_time": "2020-02-16T20:23:05.384366Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: setuptools in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (41.0.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install setuptools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:06.696890Z",
     "start_time": "2020-02-16T20:23:06.649997Z"
    }
   },
   "outputs": [],
   "source": [
    "import setuptools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:06.722245Z",
     "start_time": "2020-02-16T20:23:06.698544Z"
    }
   },
   "outputs": [],
   "source": [
    "#Explore Distribution\n",
    "#distribution(features, ['Temperature','Fuel_Price'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Takeaways:\n",
    "\n",
    "   1. Data requires pre-processing\n",
    "   2. Column(s) ISHOLIDAY has been validated\n",
    "   3. Column(s) UNEMPLOYMENT & CPI have missing values for May, Jun & Jul 2013. For these columns as the values dont change significantly month on month, value from Apr 2013 would be propogated over for each store.\n",
    "   4. Column(s) MARKDOWN* have missing values for 2010 (entire year) and 2011 (until Nov). Additionally, there are missing values for other other dates as well.\n",
    "   5. CPI and UNEMPLOYMENT value are a bit skewed. MARKDOWN* columns are skewed.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:06.737867Z",
     "start_time": "2020-02-16T20:23:06.722245Z"
    }
   },
   "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>Dept</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>24924.50</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>46039.49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>19403.54</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  Dept        Date  Weekly_Sales  IsHoliday\n",
       "0      1     1  2010-02-05      24924.50      False\n",
       "1      1     1  2010-02-12      46039.49       True\n",
       "2      1     1  2010-02-19      41595.55      False\n",
       "3      1     1  2010-02-26      19403.54      False\n",
       "4      1     1  2010-03-05      21827.90      False"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:06.800386Z",
     "start_time": "2020-02-16T20:23:06.737867Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 421570 entries, 0 to 421569\n",
      "Data columns (total 5 columns):\n",
      "Store           421570 non-null int64\n",
      "Dept            421570 non-null int64\n",
      "Date            421570 non-null object\n",
      "Weekly_Sales    421570 non-null float64\n",
      "IsHoliday       421570 non-null bool\n",
      "dtypes: bool(1), float64(1), int64(2), object(1)\n",
      "memory usage: 13.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:06.862862Z",
     "start_time": "2020-02-16T20:23:06.800386Z"
    }
   },
   "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>Dept</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>421570.000000</td>\n",
       "      <td>421570.000000</td>\n",
       "      <td>421570.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.200546</td>\n",
       "      <td>44.260317</td>\n",
       "      <td>15981.258123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.785297</td>\n",
       "      <td>30.492054</td>\n",
       "      <td>22711.183519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-4988.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2079.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>7612.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>20205.852500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>693099.360000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Store           Dept   Weekly_Sales\n",
       "count  421570.000000  421570.000000  421570.000000\n",
       "mean       22.200546      44.260317   15981.258123\n",
       "std        12.785297      30.492054   22711.183519\n",
       "min         1.000000       1.000000   -4988.940000\n",
       "25%        11.000000      18.000000    2079.650000\n",
       "50%        22.000000      37.000000    7612.030000\n",
       "75%        33.000000      74.000000   20205.852500\n",
       "max        45.000000      99.000000  693099.360000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:06.878490Z",
     "start_time": "2020-02-16T20:23:06.862862Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1285"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of rows where Weekly Sales is 0\n",
    "\n",
    "train['Store'][train['Weekly_Sales'] < 0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:06.956567Z",
     "start_time": "2020-02-16T20:23:06.878490Z"
    }
   },
   "outputs": [],
   "source": [
    "#Merge Train DF to Stores\n",
    "train_outliers = pd.merge(train, stores, how='left', on=['Store'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:07.019081Z",
     "start_time": "2020-02-16T20:23:06.956567Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "A    20099.568043\n",
       "B    12237.075977\n",
       "C     9519.532538\n",
       "Name: Weekly_Sales, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average Weekly Sales by Store Type\n",
    "train_outliers.groupby(['Type'])['Weekly_Sales'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:07.050292Z",
     "start_time": "2020-02-16T20:23:07.019081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store  Type\n",
       "3      B       6373.033983\n",
       "5      B       5053.415813\n",
       "33     A       5728.414053\n",
       "36     A       8584.412563\n",
       "Name: Weekly_Sales, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Average Weekly Sales for possibly misclassified Stores\n",
    "train_outliers = train_outliers[train_outliers['Store'].isin([3,5,33,36])]\n",
    "train_outliers.groupby(['Store','Type'])['Weekly_Sales'].mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:07.331503Z",
     "start_time": "2020-02-16T20:23:07.050292Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average Weekly Sales by Store Type\n",
    "fig, ax = plt.subplots(1, 2, figsize = (15,6))\n",
    "ax[0].bar(train_outliers['Type'].unique(), train_outliers.groupby(['Type'])['Weekly_Sales'].mean())\n",
    "ax[0].set_ylabel('Average Weekly Sales')\n",
    "ax[0].set_xlabel('Store Type')\n",
    "ax[0].yaxis.grid(True, linewidth=0.3)\n",
    "\n",
    "ax[1].bar([3,5,33,36], train_outliers.groupby(['Store','Type'])['Weekly_Sales'].mean())\n",
    "ax[1].set_ylabel('Average Weekly Sales')\n",
    "ax[1].set_xlabel('Store ID')\n",
    "ax[1].yaxis.grid(True, linewidth=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:07.347123Z",
     "start_time": "2020-02-16T20:23:07.331503Z"
    }
   },
   "outputs": [],
   "source": [
    "train_outliers = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Takeaways:\n",
    "\n",
    "   1. Column DATE is non-numeric and is a candidate for pre-processing.\n",
    "   2. 1285 records with Weekly Sales < 0\n",
    "   3. Data spans years 2010, 2011 and 2012\n",
    "   4. As suspected above, four stores [3, 5, 33 & 36] seem to have incorrectly classified as Type A & B. Average Weekly Sales for these stores is in line with the average for Type C. Hence, these would need to be reclassified as Type C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:07.362721Z",
     "start_time": "2020-02-16T20:23:07.347123Z"
    }
   },
   "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>Dept</th>\n",
       "      <th>Date</th>\n",
       "      <th>IsHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-11-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-11-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-11-16</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-11-23</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-11-30</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  Dept        Date  IsHoliday\n",
       "0      1     1  2012-11-02      False\n",
       "1      1     1  2012-11-09      False\n",
       "2      1     1  2012-11-16      False\n",
       "3      1     1  2012-11-23       True\n",
       "4      1     1  2012-11-30      False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:07.393989Z",
     "start_time": "2020-02-16T20:23:07.362721Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 115064 entries, 0 to 115063\n",
      "Data columns (total 4 columns):\n",
      "Store        115064 non-null int64\n",
      "Dept         115064 non-null int64\n",
      "Date         115064 non-null object\n",
      "IsHoliday    115064 non-null bool\n",
      "dtypes: bool(1), int64(2), object(1)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:07.425234Z",
     "start_time": "2020-02-16T20:23:07.393989Z"
    }
   },
   "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>Dept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>115064.000000</td>\n",
       "      <td>115064.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.238207</td>\n",
       "      <td>44.339524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.809930</td>\n",
       "      <td>30.656410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Store           Dept\n",
       "count  115064.000000  115064.000000\n",
       "mean       22.238207      44.339524\n",
       "std        12.809930      30.656410\n",
       "min         1.000000       1.000000\n",
       "25%        11.000000      18.000000\n",
       "50%        22.000000      37.000000\n",
       "75%        33.000000      74.000000\n",
       "max        45.000000      99.000000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:23:07.472070Z",
     "start_time": "2020-02-16T20:23:07.425234Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    88275\n",
       "2012    26789\n",
       "Name: Date, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Date'].str.slice(start=0, stop=4).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Takeaways:\n",
    "\n",
    "   1.  Column DATE is non-numeric and is a candidate for pre-processing.\n",
    "   2.  Data spans years 2012 and 2013\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correcting values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change store that were categorized incorrectly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:40:09.556361Z",
     "start_time": "2020-02-18T17:40:09.540735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 4, 32, 35], dtype='int64')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stores[stores['Store'].isin([3,5,33,36])].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:40:10.292025Z",
     "start_time": "2020-02-18T17:40:10.261009Z"
    }
   },
   "outputs": [],
   "source": [
    "stores.iat[2, 1] = stores.iat[4, 1] = stores.iat[32, 1] = stores.iat[35, 1] = 'C'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-13T20:35:28.379561Z",
     "start_time": "2020-02-13T20:35:28.286005Z"
    }
   },
   "source": [
    "The store numbers 3,5,33, and 36 are classified as 'C'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change negative values of Markdowns to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:40:11.662074Z",
     "start_time": "2020-02-18T17:40:11.624219Z"
    }
   },
   "outputs": [],
   "source": [
    "features['MarkDown1'] = features['MarkDown1'].apply(lambda x: 0 if x < 0 else x)\n",
    "features['MarkDown2'] = features['MarkDown2'].apply(lambda x: 0 if x < 0 else x)\n",
    "features['MarkDown3'] = features['MarkDown3'].apply(lambda x: 0 if x < 0 else x)\n",
    "features['MarkDown4'] = features['MarkDown4'].apply(lambda x: 0 if x < 0 else x)\n",
    "features['MarkDown5'] = features['MarkDown5'].apply(lambda x: 0 if x < 0 else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Propagate for NaN rows of CPI and Unemployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:40:14.784040Z",
     "start_time": "2020-02-18T17:40:12.844320Z"
    }
   },
   "outputs": [],
   "source": [
    "# Using 26/04/2013 values of CPI and Unemployment to impute into missing rows as this day is the \n",
    "#last day before we encounter NaN values\n",
    "\n",
    "for i in range(len(features)):\n",
    "\n",
    "    if features.iloc[i]['Date'] == '2013-04-26':\n",
    "        CPI_new = features.iloc[i]['CPI']\n",
    "        Unemployment_new = features.iloc[i]['Unemployment']\n",
    "    \n",
    "    if np.isnan(features.iloc[i]['CPI']):\n",
    "        features.iat[i, 9] = CPI_new\n",
    "        features.iat[i, 10] = Unemployment_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impute Markdown for years 2010 - 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:40:54.127114Z",
     "start_time": "2020-02-18T17:40:50.803687Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "#Set weeks for each date to make it easier to categorize Markdown values\n",
    "\n",
    "features['Week'] = 0\n",
    "\n",
    "for i in range(len(features)):\n",
    "    features.iat[i, 12] = datetime.date(int(features.iloc[i]['Date'][0:4]), \n",
    "                                        int(features.iloc[i]['Date'][5:7]), \n",
    "                                        int(features.iloc[i]['Date'][8:10])).isocalendar()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:40:56.852048Z",
     "start_time": "2020-02-18T17:40:56.820786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>Date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>Week</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  \\\n",
       "0      1  2010-02-05        42.31       2.572        NaN        NaN   \n",
       "1      1  2010-02-12        38.51       2.548        NaN        NaN   \n",
       "2      1  2010-02-19        39.93       2.514        NaN        NaN   \n",
       "3      1  2010-02-26        46.63       2.561        NaN        NaN   \n",
       "4      1  2010-03-05        46.50       2.625        NaN        NaN   \n",
       "\n",
       "   MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  IsHoliday  Week  \\\n",
       "0        NaN        NaN        NaN  211.096358         8.106      False     5   \n",
       "1        NaN        NaN        NaN  211.242170         8.106       True     6   \n",
       "2        NaN        NaN        NaN  211.289143         8.106      False     7   \n",
       "3        NaN        NaN        NaN  211.319643         8.106      False     8   \n",
       "4        NaN        NaN        NaN  211.350143         8.106      False     9   \n",
       "\n",
       "   Year  \n",
       "0  2010  \n",
       "1  2010  \n",
       "2  2010  \n",
       "3  2010  \n",
       "4  2010  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features['Year'] = features['Date'].str.slice(start=0, stop=4)\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:40:59.124430Z",
     "start_time": "2020-02-18T17:40:59.071060Z"
    }
   },
   "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>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MarkDown2</th>\n",
       "      <td>1062</td>\n",
       "      <td>0.287805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown4</th>\n",
       "      <td>528</td>\n",
       "      <td>0.143089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown3</th>\n",
       "      <td>419</td>\n",
       "      <td>0.113550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown1</th>\n",
       "      <td>12</td>\n",
       "      <td>0.003252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Total   Percent\n",
       "MarkDown2   1062  0.287805\n",
       "MarkDown4    528  0.143089\n",
       "MarkDown3    419  0.113550\n",
       "MarkDown1     12  0.003252"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Break down of missing rows for Markdown\n",
    "\n",
    "total = features[features['Year'].isin(['2012','2013'])].isnull().sum().sort_values(ascending=False)\n",
    "percent = (features[features['Year'].isin(['2012','2013'])].isnull().sum()/\n",
    "           features[features['Year'].isin(['2012','2013'])].isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:41:56.439775Z",
     "start_time": "2020-02-18T17:41:01.578381Z"
    }
   },
   "outputs": [],
   "source": [
    "# Impute Markdown values from 2012 for missing rows in 2010 and 2011\n",
    "\n",
    "# Iterate through stores\n",
    "for i in range(1, len(features['Store'].unique())):\n",
    "    \n",
    "    # For 2010, loop through weeks 5 to 52\n",
    "    for j in range(5, 52):\n",
    "        indx = features.loc[(features.Year == '2010') & (features.Store == i) & (features.Week == j),['Date']].index[0]\n",
    "        \n",
    "        features.iat[indx, 4] = features.loc[(features.Year == '2012') & (features.Store == i) & (features.Week == j),['MarkDown1']].values[0]\n",
    "        features.iat[indx, 5] = features.loc[(features.Year == '2012') & (features.Store == i) & (features.Week == j),['MarkDown2']].values[0]\n",
    "        features.iat[indx, 6] = features.loc[(features.Year == '2012') & (features.Store == i) & (features.Week == j),['MarkDown3']].values[0]\n",
    "        features.iat[indx, 7] = features.loc[(features.Year == '2012') & (features.Store == i) & (features.Week == j),['MarkDown4']].values[0]\n",
    "        features.iat[indx, 8] = features.loc[(features.Year == '2012') & (features.Store == i) & (features.Week == j),['MarkDown5']].values[0]\n",
    "        \n",
    "    # For 2011, loop through weeks 1 to 44\n",
    "    for j in range(1, 44):\n",
    "        indx = features.loc[(features.Year == '2011') & (features.Store == i) & (features.Week == j),['Date']].index[0]\n",
    "        \n",
    "        features.iat[indx, 4] = features.loc[(features.Year == '2012') & (features.Store == i) & (features.Week == j),['MarkDown1']].values[0]\n",
    "        features.iat[indx, 5] = features.loc[(features.Year == '2012') & (features.Store == i) & (features.Week == j),['MarkDown2']].values[0]\n",
    "        features.iat[indx, 6] = features.loc[(features.Year == '2012') & (features.Store == i) & (features.Week == j),['MarkDown3']].values[0]\n",
    "        features.iat[indx, 7] = features.loc[(features.Year == '2012') & (features.Store == i) & (features.Week == j),['MarkDown4']].values[0]\n",
    "        features.iat[indx, 8] = features.loc[(features.Year == '2012') & (features.Store == i) & (features.Week == j),['MarkDown5']].values[0]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T22:07:15.184586Z",
     "start_time": "2020-02-16T22:07:15.172598Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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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>Date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>Week</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>34577.06</td>\n",
       "      <td>3579.21</td>\n",
       "      <td>160.53</td>\n",
       "      <td>32403.87</td>\n",
       "      <td>5630.40</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>13925.06</td>\n",
       "      <td>6927.23</td>\n",
       "      <td>101.64</td>\n",
       "      <td>8471.88</td>\n",
       "      <td>6886.04</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>9873.33</td>\n",
       "      <td>11062.27</td>\n",
       "      <td>9.80</td>\n",
       "      <td>8409.31</td>\n",
       "      <td>3402.66</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>9349.61</td>\n",
       "      <td>7556.01</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1219.92</td>\n",
       "      <td>8143.59</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>15441.40</td>\n",
       "      <td>1569.00</td>\n",
       "      <td>10.80</td>\n",
       "      <td>25390.88</td>\n",
       "      <td>8067.61</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  \\\n",
       "0      1  2010-02-05        42.31       2.572   34577.06    3579.21   \n",
       "1      1  2010-02-12        38.51       2.548   13925.06    6927.23   \n",
       "2      1  2010-02-19        39.93       2.514    9873.33   11062.27   \n",
       "3      1  2010-02-26        46.63       2.561    9349.61    7556.01   \n",
       "4      1  2010-03-05        46.50       2.625   15441.40    1569.00   \n",
       "\n",
       "   MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  IsHoliday  Week  \\\n",
       "0     160.53   32403.87    5630.40  211.096358         8.106      False     5   \n",
       "1     101.64    8471.88    6886.04  211.242170         8.106       True     6   \n",
       "2       9.80    8409.31    3402.66  211.289143         8.106      False     7   \n",
       "3       3.20    1219.92    8143.59  211.319643         8.106      False     8   \n",
       "4      10.80   25390.88    8067.61  211.350143         8.106      False     9   \n",
       "\n",
       "   Year  \n",
       "0  2010  \n",
       "1  2010  \n",
       "2  2010  \n",
       "3  2010  \n",
       "4  2010  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:00.752039Z",
     "start_time": "2020-02-18T17:42:00.736375Z"
    }
   },
   "outputs": [],
   "source": [
    "# Fill remaining mising values of Markdown with 0\n",
    "features.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make negative sales values in train dataset to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:03.231839Z",
     "start_time": "2020-02-18T17:42:03.131799Z"
    }
   },
   "outputs": [],
   "source": [
    "train['Weekly_Sales'] = train['Weekly_Sales'].apply(lambda x: 0 if x < 0 else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Datasets to merge:\n",
    "\n",
    "    For training purposes: Stores + Features + Train\n",
    "    For testing purposes: Stores + Features + Test\n",
    "    \n",
    "Remove duplicate columns from each dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:06.641548Z",
     "start_time": "2020-02-18T17:42:06.425378Z"
    }
   },
   "outputs": [],
   "source": [
    "train = pd.merge(train, stores, how='left', on=['Store'])\n",
    "train = pd.merge(train, features, how='left', on=['Store','Date'])\n",
    "\n",
    "test = pd.merge(test, stores, how='left', on=['Store'])\n",
    "test = pd.merge(test, features, how='left', on=['Store','Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:08.204034Z",
     "start_time": "2020-02-18T17:42:08.172786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Store'][train['IsHoliday_x'] != train['IsHoliday_y']].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:09.066959Z",
     "start_time": "2020-02-18T17:42:09.051374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Store'][test['IsHoliday_x'] != test['IsHoliday_y']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:11.124023Z",
     "start_time": "2020-02-18T17:42:11.008079Z"
    }
   },
   "outputs": [],
   "source": [
    "train.drop(columns=['IsHoliday_y'], axis=1, inplace=True)\n",
    "test.drop(columns=['IsHoliday_y'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:11.602595Z",
     "start_time": "2020-02-18T17:42:11.555728Z"
    }
   },
   "outputs": [],
   "source": [
    "train.rename(columns={'IsHoliday_x': 'IsHoliday'}, inplace=True)\n",
    "test.rename(columns={'IsHoliday_x': 'IsHoliday'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:13.043837Z",
     "start_time": "2020-02-18T17:42:13.028213Z"
    }
   },
   "outputs": [],
   "source": [
    "train['Total_Markdowns'] = train['MarkDown1'] + train['MarkDown2'] + train['MarkDown3'] + train['MarkDown4'] + train['MarkDown5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:13.801183Z",
     "start_time": "2020-02-18T17:42:13.769930Z"
    }
   },
   "outputs": [],
   "source": [
    "test['Total_Markdowns'] = test['MarkDown1'] + test['MarkDown2'] + test['MarkDown3'] + test['MarkDown4'] + test['MarkDown5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:15.156780Z",
     "start_time": "2020-02-18T17:42:15.072147Z"
    }
   },
   "outputs": [],
   "source": [
    "test= test.drop(['MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5'], axis = 1)\n",
    "train= train.drop(['MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:16.721332Z",
     "start_time": "2020-02-18T17:42:16.691185Z"
    }
   },
   "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>Dept</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>Week</th>\n",
       "      <th>Year</th>\n",
       "      <th>Total_Markdowns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>24924.50</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>76351.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>46039.49</td>\n",
       "      <td>True</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>36311.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>7</td>\n",
       "      <td>2010</td>\n",
       "      <td>32757.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>19403.54</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "      <td>26272.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>50479.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  Dept        Date  Weekly_Sales  IsHoliday Type    Size  Temperature  \\\n",
       "0      1     1  2010-02-05      24924.50      False    A  151315        42.31   \n",
       "1      1     1  2010-02-12      46039.49       True    A  151315        38.51   \n",
       "2      1     1  2010-02-19      41595.55      False    A  151315        39.93   \n",
       "3      1     1  2010-02-26      19403.54      False    A  151315        46.63   \n",
       "4      1     1  2010-03-05      21827.90      False    A  151315        46.50   \n",
       "\n",
       "   Fuel_Price         CPI  Unemployment  Week  Year  Total_Markdowns  \n",
       "0       2.572  211.096358         8.106     5  2010         76351.07  \n",
       "1       2.548  211.242170         8.106     6  2010         36311.85  \n",
       "2       2.514  211.289143         8.106     7  2010         32757.37  \n",
       "3       2.561  211.319643         8.106     8  2010         26272.33  \n",
       "4       2.625  211.350143         8.106     9  2010         50479.69  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:21.396229Z",
     "start_time": "2020-02-18T17:42:21.265130Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 421570 entries, 0 to 421569\n",
      "Data columns (total 14 columns):\n",
      "Store              421570 non-null int64\n",
      "Dept               421570 non-null int64\n",
      "Date               421570 non-null object\n",
      "Weekly_Sales       421570 non-null float64\n",
      "IsHoliday          421570 non-null bool\n",
      "Type               421570 non-null object\n",
      "Size               421570 non-null int64\n",
      "Temperature        421570 non-null float64\n",
      "Fuel_Price         421570 non-null float64\n",
      "CPI                421570 non-null float64\n",
      "Unemployment       421570 non-null float64\n",
      "Week               421570 non-null int64\n",
      "Year               421570 non-null object\n",
      "Total_Markdowns    421570 non-null float64\n",
      "dtypes: bool(1), float64(6), int64(4), object(3)\n",
      "memory usage: 45.4+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummify IsHoliday from bool to int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:24.066477Z",
     "start_time": "2020-02-18T17:42:23.903742Z"
    }
   },
   "outputs": [],
   "source": [
    "train['IsHoliday'] = train['IsHoliday'].apply(lambda x: 1 if x==True else 0)\n",
    "test['IsHoliday'] = test['IsHoliday'].apply(lambda x: 1 if x==True else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:24.451940Z",
     "start_time": "2020-02-18T17:42:24.289210Z"
    }
   },
   "outputs": [],
   "source": [
    "train = pd.get_dummies(train, columns=['Type'])\n",
    "test = pd.get_dummies(test, columns=['Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:24.991262Z",
     "start_time": "2020-02-18T17:42:24.975637Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>Size</th>\n",
       "      <th>Temperature</th>\n",
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       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
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       "      <th>Type_A</th>\n",
       "      <th>Type_B</th>\n",
       "      <th>Type_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
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       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>76351.07</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>46039.49</td>\n",
       "      <td>1</td>\n",
       "      <td>151315</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>36311.85</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>7</td>\n",
       "      <td>2010</td>\n",
       "      <td>32757.37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>19403.54</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "      <td>26272.33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>50479.69</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  Dept        Date  Weekly_Sales  IsHoliday    Size  Temperature  \\\n",
       "0      1     1  2010-02-05      24924.50          0  151315        42.31   \n",
       "1      1     1  2010-02-12      46039.49          1  151315        38.51   \n",
       "2      1     1  2010-02-19      41595.55          0  151315        39.93   \n",
       "3      1     1  2010-02-26      19403.54          0  151315        46.63   \n",
       "4      1     1  2010-03-05      21827.90          0  151315        46.50   \n",
       "\n",
       "   Fuel_Price         CPI  Unemployment  Week  Year  Total_Markdowns  Type_A  \\\n",
       "0       2.572  211.096358         8.106     5  2010         76351.07       1   \n",
       "1       2.548  211.242170         8.106     6  2010         36311.85       1   \n",
       "2       2.514  211.289143         8.106     7  2010         32757.37       1   \n",
       "3       2.561  211.319643         8.106     8  2010         26272.33       1   \n",
       "4       2.625  211.350143         8.106     9  2010         50479.69       1   \n",
       "\n",
       "   Type_B  Type_C  \n",
       "0       0       0  \n",
       "1       0       0  \n",
       "2       0       0  \n",
       "3       0       0  \n",
       "4       0       0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:27.304881Z",
     "start_time": "2020-02-18T17:42:27.211121Z"
    }
   },
   "outputs": [],
   "source": [
    "#Remove date column as it is non-numeric\n",
    "train.drop(columns=['Date'], axis=1, inplace=True)\n",
    "test.drop(columns=['Date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:30.345854Z",
     "start_time": "2020-02-18T17:42:28.928460Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29cad8b70b8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = plt.cm.RdBu\n",
    "corr = train.astype(float).corr()\n",
    "\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.set(font_scale=0.9)\n",
    "sns.heatmap(round(corr,2),linewidths=0.1,vmax=1.0, square=True, \n",
    "            cmap=colormap, linecolor='white', annot=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:31.327066Z",
     "start_time": "2020-02-18T17:42:31.295798Z"
    }
   },
   "outputs": [],
   "source": [
    "corr_cutoff = 0.8\n",
    "columns = np.full((corr.shape[0],), True, dtype=bool)\n",
    "\n",
    "for i in range(corr.shape[0]):\n",
    "    for j in range(i+1, corr.shape[0]):\n",
    "        if corr.iloc[i,j] >= corr_cutoff:\n",
    "            if columns[j]:\n",
    "                columns[j] = False\n",
    "                \n",
    "selected_columns = train.columns[columns]\n",
    "highcorr_columns = train.columns.difference(selected_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:33.386207Z",
     "start_time": "2020-02-18T17:42:33.354958Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Type_A'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highcorr_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:42:34.664061Z",
     "start_time": "2020-02-18T17:42:34.618316Z"
    }
   },
   "outputs": [],
   "source": [
    "train.drop(columns=highcorr_columns, axis=1, inplace=True)\n",
    "test.drop(columns=highcorr_columns, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drop Type_A as it has a correlation of 0.86 with size. Our criteria for elimination was any variable above 0.8.\n",
    "This was based on the correlation heatmap that we created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:24:17.738170Z",
     "start_time": "2020-02-16T20:24:17.724180Z"
    }
   },
   "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>Dept</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>Size</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>Week</th>\n",
       "      <th>Year</th>\n",
       "      <th>Total_Markdowns</th>\n",
       "      <th>Type_B</th>\n",
       "      <th>Type_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24924.50</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>76351.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46039.49</td>\n",
       "      <td>1</td>\n",
       "      <td>151315</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>36311.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>7</td>\n",
       "      <td>2010</td>\n",
       "      <td>32757.37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19403.54</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "      <td>26272.33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>50479.69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  Dept  Weekly_Sales  IsHoliday    Size  Temperature  Fuel_Price  \\\n",
       "0      1     1      24924.50          0  151315        42.31       2.572   \n",
       "1      1     1      46039.49          1  151315        38.51       2.548   \n",
       "2      1     1      41595.55          0  151315        39.93       2.514   \n",
       "3      1     1      19403.54          0  151315        46.63       2.561   \n",
       "4      1     1      21827.90          0  151315        46.50       2.625   \n",
       "\n",
       "          CPI  Unemployment  Week  Year  Total_Markdowns  Type_B  Type_C  \n",
       "0  211.096358         8.106     5  2010         76351.07       0       0  \n",
       "1  211.242170         8.106     6  2010         36311.85       0       0  \n",
       "2  211.289143         8.106     7  2010         32757.37       0       0  \n",
       "3  211.319643         8.106     8  2010         26272.33       0       0  \n",
       "4  211.350143         8.106     9  2010         50479.69       0       0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:47:40.941687Z",
     "start_time": "2020-02-18T17:47:40.910751Z"
    }
   },
   "outputs": [],
   "source": [
    "X = train[['Dept','IsHoliday','Size','Temperature','Fuel_Price','CPI','Unemployment','Total_Markdowns','Type_B','Type_C']]\n",
    "y = train['Weekly_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:48:16.993280Z",
     "start_time": "2020-02-18T17:47:42.024277Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\irind\\Anaconda3\\envs\\MMA_Test\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dept\n",
      "Size\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predictor</th>\n",
       "      <th>Gini coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dept</td>\n",
       "      <td>0.618438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IsHoliday</td>\n",
       "      <td>0.009831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Size</td>\n",
       "      <td>0.228494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Temperature</td>\n",
       "      <td>0.023128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fuel_Price</td>\n",
       "      <td>0.014253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CPI</td>\n",
       "      <td>0.037308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Unemployment</td>\n",
       "      <td>0.027320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Total_Markdowns</td>\n",
       "      <td>0.032004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Type_B</td>\n",
       "      <td>0.006588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Type_C</td>\n",
       "      <td>0.002636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         predictor  Gini coefficient\n",
       "0             Dept          0.618438\n",
       "1        IsHoliday          0.009831\n",
       "2             Size          0.228494\n",
       "3      Temperature          0.023128\n",
       "4       Fuel_Price          0.014253\n",
       "5              CPI          0.037308\n",
       "6     Unemployment          0.027320\n",
       "7  Total_Markdowns          0.032004\n",
       "8           Type_B          0.006588\n",
       "9           Type_C          0.002636"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "randomforest = RandomForestRegressor(random_state=0)\n",
    "\n",
    "model = randomforest.fit(X, y)\n",
    "\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "sfm = SelectFromModel(model, threshold=0.05)\n",
    "sfm.fit(X, y)\n",
    "for feature_list_index in sfm.get_support(indices=True):\n",
    "    print(X.columns[feature_list_index])\n",
    "    \n",
    "pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Weekly Sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:53:41.872049Z",
     "start_time": "2020-02-18T17:53:41.840792Z"
    }
   },
   "outputs": [],
   "source": [
    "X = train[['Dept','IsHoliday','Size','Temperature','Fuel_Price','CPI','Unemployment','Total_Markdowns','Type_B','Type_C']]\n",
    "y = train['Weekly_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:53:42.763735Z",
     "start_time": "2020-02-18T17:53:42.694690Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.3, random_state = 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:53:43.528239Z",
     "start_time": "2020-02-18T17:53:43.381143Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm_sales = LinearRegression()\n",
    "model_lm = lm_sales.fit(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:53:44.425424Z",
     "start_time": "2020-02-18T17:53:44.394210Z"
    }
   },
   "outputs": [],
   "source": [
    "y_val_pred = lm_sales.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:53:45.396052Z",
     "start_time": "2020-02-18T17:53:45.364812Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21445.67365885541\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mse_lm = mean_squared_error(y_val, y_val_pred)\n",
    "\n",
    "RMSE_lm = (mse_lm)**(1/2)\n",
    "print(RMSE_lm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T21:25:29.152186Z",
     "start_time": "2020-02-14T21:25:29.143213Z"
    }
   },
   "source": [
    "## Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T17:56:22.878003Z",
     "start_time": "2020-02-18T17:53:53.698167Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5511.033728839515\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(random_state=5, n_estimators = 100)\n",
    "model = rf.fit(X_train,y_train)\n",
    "\n",
    "y_test_pred = rf.predict(X_val)\n",
    "\n",
    "mse_rf = mean_squared_error(y_val,y_test_pred)\n",
    "RMSE_rf = (mse_rf)**(1/2)\n",
    "print(RMSE_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T21:56:08.086249Z",
     "start_time": "2020-02-14T21:56:08.070630Z"
    }
   },
   "source": [
    "## LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:00:06.108225Z",
     "start_time": "2020-02-18T18:00:06.070472Z"
    }
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:00:06.872924Z",
     "start_time": "2020-02-18T18:00:06.587783Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\irind\\Anaconda3\\envs\\MMA_Test\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n",
      "C:\\Users\\irind\\Anaconda3\\envs\\MMA_Test\\lib\\site-packages\\xgboost\\core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    }
   ],
   "source": [
    "data_dmatrix = xgb.DMatrix(data=X,label=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:00:08.442557Z",
     "start_time": "2020-02-18T18:00:08.426918Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "                max_depth = 5, alpha = 10, n_estimators = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:00:21.929819Z",
     "start_time": "2020-02-18T18:00:09.190914Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:00:09] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "xg_reg.fit(X_train,y_train)\n",
    "\n",
    "preds = xg_reg.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:00:22.308684Z",
     "start_time": "2020-02-18T18:00:22.293052Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 10403.909351\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(y_val, preds))\n",
    "print(\"RMSE: %f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:01:50.188050Z",
     "start_time": "2020-02-18T18:01:50.172425Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:01:50.936596Z",
     "start_time": "2020-02-18T18:01:50.773828Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "standardizer = StandardScaler()\n",
    "X_std = standardizer.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:01:51.511982Z",
     "start_time": "2020-02-18T18:01:51.389503Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_std, y, test_size = 0.33, random_state = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:01:59.471168Z",
     "start_time": "2020-02-18T18:01:51.975658Z"
    }
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsRegressor(n_neighbors=2)\n",
    "model_KNN = knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:02:04.452266Z",
     "start_time": "2020-02-18T18:01:59.704191Z"
    }
   },
   "outputs": [],
   "source": [
    "y_test_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:02:04.710499Z",
     "start_time": "2020-02-18T18:02:04.694840Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "443527966.89054954\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mse_KNN = mean_squared_error(y_test, y_test_pred)\n",
    "print(mse_KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:02:08.204429Z",
     "start_time": "2020-02-18T18:02:08.174300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21060.103677108276\n"
     ]
    }
   ],
   "source": [
    "RMSE_KNN = (mse_KNN)**(1/2)\n",
    "print(RMSE_KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:02:25.711486Z",
     "start_time": "2020-02-18T18:02:22.744066Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mlxtend in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (0.17.1)\n",
      "Requirement already satisfied: pandas>=0.24.2 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from mlxtend) (0.24.2)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from mlxtend) (3.1.0)\n",
      "Requirement already satisfied: setuptools in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from mlxtend) (41.0.1)\n",
      "Requirement already satisfied: numpy>=1.16.2 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from mlxtend) (1.16.4)\n",
      "Requirement already satisfied: scikit-learn>=0.20.3 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from mlxtend) (0.21.2)\n",
      "Requirement already satisfied: joblib>=0.13.2 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from mlxtend) (0.13.2)\n",
      "Requirement already satisfied: scipy>=1.2.1 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from mlxtend) (1.4.1)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2019.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.4.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\irind\\anaconda3\\envs\\mma_test\\lib\\site-packages (from python-dateutil>=2.5.0->pandas>=0.24.2->mlxtend) (1.12.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install mlxtend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:02:27.374078Z",
     "start_time": "2020-02-18T18:02:27.295582Z"
    },
    "run_control": {
     "marked": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from mlxtend.regressor import StackingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:02:28.222833Z",
     "start_time": "2020-02-18T18:02:28.207202Z"
    }
   },
   "outputs": [],
   "source": [
    "stregr = StackingRegressor(regressors=[model_lm, xg_reg,model_KNN], meta_regressor=rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:05:22.048055Z",
     "start_time": "2020-02-18T18:02:29.426089Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\irind\\Anaconda3\\envs\\MMA_Test\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n",
      "C:\\Users\\irind\\Anaconda3\\envs\\MMA_Test\\lib\\site-packages\\xgboost\\core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:02:29] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "StackingRegressor(meta_regressor=RandomForestRegressor(bootstrap=True,\n",
       "                                                       criterion='mse',\n",
       "                                                       max_depth=None,\n",
       "                                                       max_features='auto',\n",
       "                                                       max_leaf_nodes=None,\n",
       "                                                       min_impurity_decrease=0.0,\n",
       "                                                       min_impurity_split=None,\n",
       "                                                       min_samples_leaf=1,\n",
       "                                                       min_samples_split=2,\n",
       "                                                       min_weight_fraction_leaf=0.0,\n",
       "                                                       n_estimators=100,\n",
       "                                                       n_jobs=None,\n",
       "                                                       oob_score=False,\n",
       "                                                       random_state=5,\n",
       "                                                       verbose=0,\n",
       "                                                       warm_start=False),\n",
       "                  ref...\n",
       "                                           n_jobs=1, nthread=None,\n",
       "                                           objective='reg:linear',\n",
       "                                           random_state=0, reg_alpha=0,\n",
       "                                           reg_lambda=1, scale_pos_weight=1,\n",
       "                                           seed=None, silent=None, subsample=1,\n",
       "                                           verbosity=1),\n",
       "                              KNeighborsRegressor(algorithm='auto',\n",
       "                                                  leaf_size=30,\n",
       "                                                  metric='minkowski',\n",
       "                                                  metric_params=None,\n",
       "                                                  n_jobs=None, n_neighbors=2,\n",
       "                                                  p=2, weights='uniform')],\n",
       "                  store_train_meta_features=False,\n",
       "                  use_features_in_secondary=False, verbose=0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stregr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:07:05.963905Z",
     "start_time": "2020-02-18T18:06:48.375642Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([28691.9195, 44288.9065, 42372.8655, ...,  1081.9412,   955.671 ,\n",
       "         900.459 ])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stregr.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:07:26.599178Z",
     "start_time": "2020-02-18T18:07:09.459006Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 6432607.9489\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared Error: %.4f\"\n",
    "      % np.mean((stregr.predict(X) - y) ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:07:44.375577Z",
     "start_time": "2020-02-18T18:07:28.784586Z"
    }
   },
   "outputs": [],
   "source": [
    "mse = np.mean((stregr.predict(X) - y) ** 2)\n",
    "rmse = (mse)**(1/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T18:07:53.636533Z",
     "start_time": "2020-02-18T18:07:53.614373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 2536.2587\n"
     ]
    }
   ],
   "source": [
    "print(\"RMSE: %.4f\" \n",
    "      % rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Causal Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:31:25.260634Z",
     "start_time": "2020-02-16T20:31:25.245012Z"
    }
   },
   "outputs": [],
   "source": [
    "import dowhy\n",
    "from dowhy import CausalModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:50:57.054913Z",
     "start_time": "2020-02-16T20:50:57.046938Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['IsHoliday'] on outcome ['Weekly_Sales']\n"
     ]
    }
   ],
   "source": [
    "model=CausalModel(\n",
    "        data = train,\n",
    "        treatment=[\"IsHoliday\"],\n",
    "        outcome=\"Weekly_Sales\",\n",
    "        common_causes = ['Store','Dept','Size','Temperature','Fuel_Price','CPI','Unemployment','Total_Markdowns','Type_B','Type_C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:55:07.727268Z",
     "start_time": "2020-02-16T20:51:00.377367Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_identifier:Common causes of treatment and outcome:['U', 'Temperature', 'Dept', 'Unemployment', 'Total_Markdowns', 'Type_C', 'Store', 'Type_B', 'CPI', 'Size', 'Fuel_Price']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:b: Weekly_Sales~IsHoliday+Temperature+Dept+Unemployment+Total_Markdowns+Type_C+Store+Type_B+CPI+Size+Fuel_Price\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Target estimand\n",
      "Estimand type: nonparametric-ate\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "     d                                                                        \n",
      "────────────(Expectation(Weekly_Sales|Temperature,Dept,Unemployment,Total_Mark\n",
      "d[IsHoliday]                                                                  \n",
      "\n",
      "                                               \n",
      "downs,Type_C,Store,Type_B,CPI,Size,Fuel_Price))\n",
      "                                               \n",
      "Estimand assumption 1, Unconfoundedness: If U→{IsHoliday} and U→Weekly_Sales then P(Weekly_Sales|IsHoliday,Temperature,Dept,Unemployment,Total_Markdowns,Type_C,Store,Type_B,CPI,Size,Fuel_Price,U) = P(Weekly_Sales|IsHoliday,Temperature,Dept,Unemployment,Total_Markdowns,Type_C,Store,Type_B,CPI,Size,Fuel_Price)\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "No such variable found!\n",
      "\n",
      "## Realized estimand\n",
      "b: Weekly_Sales~IsHoliday+Temperature+Dept+Unemployment+Total_Markdowns+Type_C+Store+Type_B+CPI+Size+Fuel_Price\n",
      "## Estimate\n",
      "Value: 740.0316245247413\n",
      "\n",
      "## Statistical Significance\n",
      "p-value: <0.001\n",
      "\n",
      "Causal Estimate is 740.0316245247413\n"
     ]
    }
   ],
   "source": [
    "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
    "\n",
    "\n",
    "estimate = model.estimate_effect(identified_estimand,\n",
    "                                 method_name=\"backdoor.linear_regression\",test_significance=True)\n",
    "print(estimate)\n",
    "print(\"Causal Estimate is \" + str(estimate.value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:55:16.716709Z",
     "start_time": "2020-02-16T20:55:16.396568Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:b: Weekly_Sales~IsHoliday+Temperature+Dept+Unemployment+Total_Markdowns+Type_C+Store+Type_B+CPI+Size+Fuel_Price+w_random\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add a Random Common Cause\n",
      "Estimated effect:(740.0316245247413,)\n",
      "New effect:(740.1212040839982,)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_random=model.refute_estimate(identified_estimand, estimate, method_name=\"random_common_cause\")\n",
    "print(res_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:44:01.890235Z",
     "start_time": "2020-02-16T20:44:01.882257Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['Unemployment'] on outcome ['Weekly_Sales']\n"
     ]
    }
   ],
   "source": [
    "model2=CausalModel(\n",
    "        data = train,\n",
    "        treatment=[\"Unemployment\"],\n",
    "        outcome=\"Weekly_Sales\",\n",
    "        common_causes = ['Store','Dept','IsHoliday','Size','Temperature','Fuel_Price','CPI','Total_Markdowns','Type_B','Type_C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:48:48.384576Z",
     "start_time": "2020-02-16T20:44:30.394106Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_identifier:Common causes of treatment and outcome:['U', 'Temperature', 'Dept', 'IsHoliday', 'Total_Markdowns', 'Type_C', 'Store', 'Type_B', 'CPI', 'Size', 'Fuel_Price']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:b: Weekly_Sales~Unemployment+Temperature+Dept+IsHoliday+Total_Markdowns+Type_C+Store+Type_B+CPI+Size+Fuel_Price\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Target estimand\n",
      "Estimand type: nonparametric-ate\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "       d                                                                      \n",
      "───────────────(Expectation(Weekly_Sales|Temperature,Dept,IsHoliday,Total_Mark\n",
      "d[Unemployment]                                                               \n",
      "\n",
      "                                               \n",
      "downs,Type_C,Store,Type_B,CPI,Size,Fuel_Price))\n",
      "                                               \n",
      "Estimand assumption 1, Unconfoundedness: If U→{Unemployment} and U→Weekly_Sales then P(Weekly_Sales|Unemployment,Temperature,Dept,IsHoliday,Total_Markdowns,Type_C,Store,Type_B,CPI,Size,Fuel_Price,U) = P(Weekly_Sales|Unemployment,Temperature,Dept,IsHoliday,Total_Markdowns,Type_C,Store,Type_B,CPI,Size,Fuel_Price)\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "No such variable found!\n",
      "\n",
      "## Realized estimand\n",
      "b: Weekly_Sales~Unemployment+Temperature+Dept+IsHoliday+Total_Markdowns+Type_C+Store+Type_B+CPI+Size+Fuel_Price\n",
      "## Estimate\n",
      "Value: -233.69428936730765\n",
      "\n",
      "## Statistical Significance\n",
      "p-value: <0.001\n",
      "\n",
      "Causal Estimate is -233.69428936730765\n"
     ]
    }
   ],
   "source": [
    "identified_estimand = model2.identify_effect(proceed_when_unidentifiable=True)\n",
    "\n",
    "\n",
    "estimate = model2.estimate_effect(identified_estimand,\n",
    "                                 method_name=\"backdoor.linear_regression\",test_significance=True)\n",
    "print(estimate)\n",
    "print(\"Causal Estimate is \" + str(estimate.value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T20:49:54.379078Z",
     "start_time": "2020-02-16T20:49:54.057971Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:b: Weekly_Sales~Unemployment+Temperature+Dept+IsHoliday+Total_Markdowns+Type_C+Store+Type_B+CPI+Size+Fuel_Price+w_random\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add a Random Common Cause\n",
      "Estimated effect:(-233.69428936730765,)\n",
      "New effect:(-233.6890168870741,)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_random=model2.refute_estimate(identified_estimand, estimate, method_name=\"random_common_cause\")\n",
    "print(res_random)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Targeting specific Departments for promotions will be more useful.\n",
    "2) Markdowns don't have a very strong effect on sales.\n",
    "3) Should not worry too much about Macro-economic conditions because Walmart already is at an affordable price point.\n",
    "4) Focus on larger stores such as Type A and B as they contribute the most in total Sales.\n",
    "5) Focus on satisfying the interests of employees and suppliers to improve stakeholder management."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Future Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Adding data on extreme weather conditions (ie. hurricanes, etc.).  Supermarkets usually run out of stocks and are almost never prepared for such events.\n",
    "2) Including location vs. population.  Zip code information with nationality information can help refine assortment, and should be included in a model.\n",
    "3) Ecommerce presence to check the significance of sales coming from online retail."
   ]
  }
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