bigmart_sales.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.preprocessing import LabelEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets_dir = \"./datasets/bigmart/\"\n",
    "sales_train_data = pd.read_csv(datasets_dir + \"Train.csv\")\n",
    "sales_train_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Weight                  1463\n",
       "Item_Fat_Content                0\n",
       "Item_Visibility                 0\n",
       "Item_Type                       0\n",
       "Item_MRP                        0\n",
       "Outlet_Identifier               0\n",
       "Outlet_Establishment_Year       0\n",
       "Outlet_Size                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_train_data['Item_Weight'].fillna(sales_train_data['Item_Weight'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Weight                     0\n",
       "Item_Fat_Content                0\n",
       "Item_Visibility                 0\n",
       "Item_Type                       0\n",
       "Item_MRP                        0\n",
       "Outlet_Identifier               0\n",
       "Outlet_Establishment_Year       0\n",
       "Outlet_Size                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlet_Type Grocery Store Supermarket Type1 Supermarket Type2  \\\n",
      "Outlet_Size         Small             Small            Medium   \n",
      "\n",
      "Outlet_Type Supermarket Type3  \n",
      "Outlet_Size            Medium  \n"
     ]
    }
   ],
   "source": [
    "mode_of_outlet_size = sales_train_data.pivot_table(values='Outlet_Size', columns='Outlet_Type', aggfunc=(lambda x: x.mode()[0]))\n",
    "print(mode_of_outlet_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       False\n",
      "1       False\n",
      "2       False\n",
      "3        True\n",
      "4       False\n",
      "        ...  \n",
      "8518    False\n",
      "8519     True\n",
      "8520    False\n",
      "8521    False\n",
      "8522    False\n",
      "Name: Outlet_Size, Length: 8523, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "missing_values = sales_train_data['Outlet_Size'].isnull()\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_train_data.loc[missing_values, 'Outlet_Size'] = sales_train_data.loc[missing_values, 'Outlet_Type'].apply(lambda x: mode_of_outlet_size[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              0\n",
       "Item_Weight                  0\n",
       "Item_Fat_Content             0\n",
       "Item_Visibility              0\n",
       "Item_Type                    0\n",
       "Item_MRP                     0\n",
       "Outlet_Identifier            0\n",
       "Outlet_Establishment_Year    0\n",
       "Outlet_Size                  0\n",
       "Outlet_Location_Type         0\n",
       "Outlet_Type                  0\n",
       "Item_Outlet_Sales            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        text-align: right;\n",
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       "</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>Small</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       Small               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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-25-8f2a8b686084>:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(sales_train_data['Item_Weight'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.distplot(sales_train_data['Item_Weight'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-26-73438fa06a5e>:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(sales_train_data['Item_Visibility'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Visibility\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.distplot(sales_train_data['Item_Visibility'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-27-742ec5ccc151>:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(sales_train_data['Item_MRP'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_MRP\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.distplot(sales_train_data['Item_MRP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-9b4cb60ebdf2>:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(sales_train_data['Item_Outlet_Sales'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Outlet_Sales\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.distplot(sales_train_data['Item_Outlet_Sales'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outlet establishment year\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.countplot(x=sales_train_data['Outlet_Establishment_Year'], data=sales_train_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# item fat content\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.countplot(x=sales_train_data['Item_Fat_Content'], data=sales_train_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# item type\n",
    "plt.figure(figsize=(30, 6))\n",
    "sns.countplot(x=sales_train_data['Item_Type'], data=sales_train_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Small</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       Small               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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_train_data.replace({'Item_Fat_Content': {'low fat': 'Low Fat', 'LF': 'Low Fat', 'reg': 'Regular'}}, inplace=True)\n",
    "sales_train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Outlet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
       "0           FDA15         9.30                 0         0.016047          4   \n",
       "1           DRC01         5.92                 1         0.019278         14   \n",
       "2           FDN15        17.50                 0         0.016760         10   \n",
       "3           FDX07        19.20                 1         0.000000          6   \n",
       "4           NCD19         8.93                 0         0.000000          9   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0  249.8092            OUT049                       1999            1   \n",
       "1   48.2692            OUT018                       2009            1   \n",
       "2  141.6180            OUT049                       1999            1   \n",
       "3  182.0950            OUT010                       1998            2   \n",
       "4   53.8614            OUT013                       1987            0   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  Outlet  \n",
       "0                     0            1          3735.1380       9  \n",
       "1                     2            2           443.4228       3  \n",
       "2                     0            1          2097.2700       9  \n",
       "3                     2            0           732.3800       0  \n",
       "4                     2            1           994.7052       1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "sales_train_data['Outlet'] = encoder.fit_transform(sales_train_data['Outlet_Identifier'])\n",
    "sales_train_data['Outlet_Type'] = encoder.fit_transform(sales_train_data['Outlet_Type'])\n",
    "sales_train_data['Item_Type'] = encoder.fit_transform(sales_train_data['Item_Type'])\n",
    "sales_train_data['Item_Fat_Content'] = encoder.fit_transform(sales_train_data['Item_Fat_Content'])\n",
    "sales_train_data['Outlet_Size'] = encoder.fit_transform(sales_train_data['Outlet_Size'])\n",
    "sales_train_data['Outlet_Location_Type'] = encoder.fit_transform(sales_train_data['Outlet_Location_Type'])\n",
    "sales_train_data['Outlet_Type'] = encoder.fit_transform(sales_train_data['Outlet_Type'])\n",
    "sales_train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sales_train_data.drop(columns=['Item_Identifier', 'Outlet_Identifier', 'Item_Outlet_Sales'])\n",
    "Y = sales_train_data['Item_Outlet_Sales']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = XGBRegressor()\n",
    "regressor.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared value:  0.849989620732928\n"
     ]
    }
   ],
   "source": [
    "# prediction on training data\n",
    "training_data_prediction = regressor.predict(X_train)\n",
    "score_1 = metrics.r2_score(Y_train, training_data_prediction)\n",
    "print(\"R squared value: \", score_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared value:  0.5287653315721402\n"
     ]
    }
   ],
   "source": [
    "# prediction on test data\n",
    "test_data_prediction = regressor.predict(X_test)\n",
    "score_2 = metrics.r2_score(Y_test, test_data_prediction)\n",
    "print(\"R squared value: \", score_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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