Big Sales Mart Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Big Sales Mart\n",
    "\n",
    "Decision Trees\n",
    "\n",
    "Random Forest\n",
    "\n",
    "Bagging & Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dataset\n",
    "\n",
    "train = pd.read_csv(\"~/Downloads/train-file.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look into the structure and dimensions of the dataset\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 13 columns):\n",
      "Item_Identifier              8523 non-null object\n",
      "Item_Weight                  8523 non-null float64\n",
      "Item_Fat_Content             8523 non-null object\n",
      "Item_Visibility              8523 non-null float64\n",
      "Item_Type                    8523 non-null object\n",
      "Item_MRP                     8523 non-null float64\n",
      "Outlet_Identifier            8523 non-null object\n",
      "Outlet_Establishment_Year    8523 non-null int64\n",
      "Outlet_Size                  8523 non-null object\n",
      "Outlet_Location_Type         8523 non-null object\n",
      "Outlet_Type                  8523 non-null object\n",
      "Item_Outlet_Sales            8523 non-null float64\n",
      "Price_Per_Unit               8523 non-null float64\n",
      "dtypes: float64(5), int64(1), object(7)\n",
      "memory usage: 865.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 13)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape # No of Rows and Columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a2e7a9cc0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create some plots\n",
    "plt.scatter(train[\"Item_Visibility\"], train[\"Item_Outlet_Sales\"], color=\"red\") # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a boxplot\n",
    "plt.figure(figsize = (15,6))\n",
    "ax = sns.boxplot(x='Item_Type', y='Item_Outlet_Sales', data=train, palette=\"Set1\")\n",
    "ax.set_title(\"Item_Type vs. Item_Outlet_Sales\", fontsize=15)\n",
    "ax.set_xlabel(\"\", fontsize=18)\n",
    "ax.set_ylabel(\"Item_Outlet_Sales\", fontsize=14)\n",
    "plt.setp(ax.get_xticklabels(), rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot of Item Type Vs Sales\n",
    "plt.figure(figsize=(15,6))\n",
    "plot = train.groupby(\"Item_Type\")[\"Item_Outlet_Sales\"].sum().plot.bar(color=\"coral\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Na\n",
    "newtrain = train.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4650, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.300</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.920</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.500</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.930</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FDP36</td>\n",
       "      <td>10.395</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>51.4008</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>556.6088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15        9.300          Low Fat         0.016047   \n",
       "1           DRC01        5.920          Regular         0.019278   \n",
       "2           FDN15       17.500          Low Fat         0.016760   \n",
       "4           NCD19        8.930          Low Fat         0.000000   \n",
       "5           FDP36       10.395          Regular         0.000000   \n",
       "\n",
       "      Item_Type  Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "0         Dairy  249.8092            OUT049                       1999   \n",
       "1   Soft Drinks   48.2692            OUT018                       2009   \n",
       "2          Meat  141.6180            OUT049                       1999   \n",
       "4     Household   53.8614            OUT013                       1987   \n",
       "5  Baking Goods   51.4008            OUT018                       2009   \n",
       "\n",
       "  Outlet_Size Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
       "0      Medium               Tier 1  Supermarket Type1          3735.1380  \n",
       "1      Medium               Tier 3  Supermarket Type2           443.4228  \n",
       "2      Medium               Tier 1  Supermarket Type1          2097.2700  \n",
       "4        High               Tier 3  Supermarket Type1           994.7052  \n",
       "5      Medium               Tier 3  Supermarket Type2           556.6088  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-99.75424205270768, pvalue=0.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# is the average MRP and Sales Avg Same\n",
    "\n",
    "# H0: Avg MRP and Avg Sales is same\n",
    "# H1: Avg MRP & Avg Sales is not same.\n",
    "\n",
    "import scipy.stats as stats\n",
    "stats.ttest_rel(newtrain.Item_MRP, newtrain.Item_Outlet_Sales)\n",
    "\n",
    "# Since the PValue is 0 and hence it is clear that the Average MRP and Avg Sales is not same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Is there any relation between Item Fat Content and Products Sold\n",
    "\n",
    "# Ho: The Item Fat Content & Products Sold are Independent of Each Other.\n",
    "# H1: The Item Fat Content & Products Sold are Dependent\n",
    "array = pd.crosstab(newtrain.Item_Fat_Content, newtrain.Item_Type)\n",
    "teststats, pvalue, dof, exp_freq = stats.chi2_contingency(array)\n",
    "print(pvalue)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dairy', 'Soft Drinks', 'Meat', 'Household', 'Baking Goods',\n",
       "       'Snack Foods', 'Fruits and Vegetables', 'Breakfast',\n",
       "       'Health and Hygiene', 'Frozen Foods', 'Hard Drinks', 'Canned',\n",
       "       'Starchy Foods', 'Breads', 'Others', 'Seafood'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets Extract a New Feature called Perishables and Non Perishables\n",
    "newtrain.Item_Type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "perishables = [\"Dairy\", \"Meat\", \"Fruits and Vegetables\", \"Breakfast\",\"Canned\",\n",
    "              \"Starchy Foods\",\"Breads\", \"Seafood\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def local(x):\n",
    "    if(x in perishables):\n",
    "        return(\"Perishables\")\n",
    "    else:\n",
    "        return(\"Non Perishables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "newtrain[\"Category\"] = newtrain.Item_Type.apply(local)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.300</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>Perishables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.920</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>Non Perishables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.500</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>Perishables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.930</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>Non Perishables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FDP36</td>\n",
       "      <td>10.395</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>51.4008</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>556.6088</td>\n",
       "      <td>Non Perishables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15        9.300          Low Fat         0.016047   \n",
       "1           DRC01        5.920          Regular         0.019278   \n",
       "2           FDN15       17.500          Low Fat         0.016760   \n",
       "4           NCD19        8.930          Low Fat         0.000000   \n",
       "5           FDP36       10.395          Regular         0.000000   \n",
       "\n",
       "      Item_Type  Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "0         Dairy  249.8092            OUT049                       1999   \n",
       "1   Soft Drinks   48.2692            OUT018                       2009   \n",
       "2          Meat  141.6180            OUT049                       1999   \n",
       "4     Household   53.8614            OUT013                       1987   \n",
       "5  Baking Goods   51.4008            OUT018                       2009   \n",
       "\n",
       "  Outlet_Size Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \\\n",
       "0      Medium               Tier 1  Supermarket Type1          3735.1380   \n",
       "1      Medium               Tier 3  Supermarket Type2           443.4228   \n",
       "2      Medium               Tier 1  Supermarket Type1          2097.2700   \n",
       "4        High               Tier 3  Supermarket Type1           994.7052   \n",
       "5      Medium               Tier 3  Supermarket Type2           556.6088   \n",
       "\n",
       "          Category  \n",
       "0      Perishables  \n",
       "1  Non Perishables  \n",
       "2      Perishables  \n",
       "4  Non Perishables  \n",
       "5  Non Perishables  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlet Size and Product Category are not Related.\n",
    "\n",
    "array = pd.crosstab(newtrain.Outlet_Size, newtrain.Category)\n",
    "teststats, pvalue, dof, exp_freq = stats.chi2_contingency(array)\n",
    "\n",
    "print(pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9998404540411131\n"
     ]
    }
   ],
   "source": [
    "# Outlet Size and Item Type\n",
    "\n",
    "array = pd.crosstab(newtrain.Outlet_Size, newtrain.Item_Type)\n",
    "teststats, pvalue, dof, exp_freq = stats.chi2_contingency(array)\n",
    "\n",
    "print(pvalue) # Outlet Size and Item Type are not Related."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.308241e+07</td>\n",
       "      <td>1.654121e+07</td>\n",
       "      <td>7.391908</td>\n",
       "      <td>0.000623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>4647.0</td>\n",
       "      <td>1.039880e+10</td>\n",
       "      <td>2.237745e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 df        sum_sq       mean_sq         F    PR(>F)\n",
       "Outlet_Size     2.0  3.308241e+07  1.654121e+07  7.391908  0.000623\n",
       "Residual     4647.0  1.039880e+10  2.237745e+06       NaN       NaN"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is the Sales Dependent on the Outlet Size\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "model = ols('Item_Outlet_Sales ~ Outlet_Size', data=newtrain).fit()\n",
    "anova_table = sm.stats.anova_lm(model)\n",
    "anova_table\n",
    "\n",
    "# Since P value is less than 0.05, hence we can say that Outlet Size has an Impact on Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.308241e+07</td>\n",
       "      <td>1.654121e+07</td>\n",
       "      <td>7.438720</td>\n",
       "      <td>5.950693e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.988647e+07</td>\n",
       "      <td>3.494324e+07</td>\n",
       "      <td>15.714268</td>\n",
       "      <td>1.578936e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.058706e+05</td>\n",
       "      <td>4.058706e+05</td>\n",
       "      <td>0.182523</td>\n",
       "      <td>6.692334e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>4645.0</td>\n",
       "      <td>1.032891e+10</td>\n",
       "      <td>2.223663e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          df        sum_sq       mean_sq          F  \\\n",
       "Outlet_Size              2.0  3.308241e+07  1.654121e+07   7.438720   \n",
       "Outlet_Location_Type     2.0  6.988647e+07  3.494324e+07  15.714268   \n",
       "Outlet_Type              1.0  4.058706e+05  4.058706e+05   0.182523   \n",
       "Residual              4645.0  1.032891e+10  2.223663e+06        NaN   \n",
       "\n",
       "                            PR(>F)  \n",
       "Outlet_Size           5.950693e-04  \n",
       "Outlet_Location_Type  1.578936e-07  \n",
       "Outlet_Type           6.692334e-01  \n",
       "Residual                       NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets Find out if the Location Type has an Impact on the Sales\n",
    "\n",
    "model = ols('Item_Outlet_Sales ~ Outlet_Size + Outlet_Location_Type+Outlet_Type', data=newtrain).fit()\n",
    "anova_table = sm.stats.anova_lm(model)\n",
    "anova_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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