Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b98f24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24e8d8d6",
   "metadata": {},
   "source": [
    "### Data Collection & Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68e34ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the daset from csv file to a pandas Dataframe \n",
    "mart_data = pd.read_csv('Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff5ffa97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first 5 rows fo dataframe\n",
    "mart_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f867e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mart_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7b0b695",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   object \n",
      " 1   Item_Weight                7060 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   object \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   object \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   object \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                6113 non-null   object \n",
      " 9   Outlet_Location_Type       8523 non-null   object \n",
      " 10  Outlet_Type                8523 non-null   object \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 799.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# getting some information about the dataset\n",
    "mart_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c37ebf",
   "metadata": {},
   "source": [
    "Categorical Features:\n",
    "\n",
    "-Item_Identifier              \n",
    "-Item_Fat_Content                               \n",
    "-Item_Type                                           \n",
    "-Outlet_Identifier                                      \n",
    "-Outlet_Size                                           \n",
    "-Outlet_Location_Type                                \n",
    "-Outlet_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8cd9141d",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "mart_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9952c77f",
   "metadata": {},
   "source": [
    "Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b565807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.857645184136183"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean value of \"Item_weight\" column\n",
    "mart_data['Item_Weight'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90c472a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling the missing values in \"Item_Weight\" column with \"Mean Value\"\n",
    "mart_data['Item_Weight'].fillna(mart_data['Item_Weight'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0bd7b2e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling the missing values in \"Outlet_Size\" with mode\n",
    "mode_outlet_size = mart_data.pivot_table(values=\"Outlet_Size\", columns = 'Outlet_Type', aggfunc=(lambda x: x.mode()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6f57a844",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Supermarket Type1</th>\n",
       "      <th>Supermarket Type2</th>\n",
       "      <th>Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <td>Small</td>\n",
       "      <td>Small</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Outlet_Type Grocery Store Supermarket Type1 Supermarket Type2  \\\n",
       "Outlet_Size         Small             Small            Medium   \n",
       "\n",
       "Outlet_Type Supermarket Type3  \n",
       "Outlet_Size            Medium  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_outlet_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9512c8ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_values = mart_data['Outlet_Size'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f10c04bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ac80cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "mart_data.loc[missing_values, 'Outlet_Size'] = mart_data.loc[missing_values, 'Outlet_Type'].apply(lambda x:mode_outlet_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab0119ab",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mart_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f021881f",
   "metadata": {},
   "source": [
    "### Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a0da086e",
   "metadata": {},
   "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>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.857645</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>140.992782</td>\n",
       "      <td>1997.831867</td>\n",
       "      <td>2181.288914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.226124</td>\n",
       "      <td>0.051598</td>\n",
       "      <td>62.275067</td>\n",
       "      <td>8.371760</td>\n",
       "      <td>1706.499616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.555000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.290000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>33.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.310000</td>\n",
       "      <td>0.026989</td>\n",
       "      <td>93.826500</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>834.247400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.857645</td>\n",
       "      <td>0.053931</td>\n",
       "      <td>143.012800</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1794.331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.094585</td>\n",
       "      <td>185.643700</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>3101.296400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0.328391</td>\n",
       "      <td>266.888400</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>13086.964800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
       "count  8523.000000      8523.000000  8523.000000                8523.000000   \n",
       "mean     12.857645         0.066132   140.992782                1997.831867   \n",
       "std       4.226124         0.051598    62.275067                   8.371760   \n",
       "min       4.555000         0.000000    31.290000                1985.000000   \n",
       "25%       9.310000         0.026989    93.826500                1987.000000   \n",
       "50%      12.857645         0.053931   143.012800                1999.000000   \n",
       "75%      16.000000         0.094585   185.643700                2004.000000   \n",
       "max      21.350000         0.328391   266.888400                2009.000000   \n",
       "\n",
       "       Item_Outlet_Sales  \n",
       "count        8523.000000  \n",
       "mean         2181.288914  \n",
       "std          1706.499616  \n",
       "min            33.290000  \n",
       "25%           834.247400  \n",
       "50%          1794.331000  \n",
       "75%          3101.296400  \n",
       "max         13086.964800  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical measure about the data\n",
    "mart_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "daa528a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7d30730",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sangram\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Weight distribution\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.distplot(mart_data['Item_Weight'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1ee0e390",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sangram\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_weight distribution\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.distplot(mart_data['Item_Visibility'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b3cf6cc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sangram\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Item MRP distribution\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.distplot(mart_data['Item_MRP'])\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "634a6232",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sangram\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Outlet_Sales distribution\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.distplot(mart_data['Item_Outlet_Sales'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3aee9c08",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sangram\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlet_Establishment_Year \n",
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(mart_data['Outlet_Establishment_Year'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d5a4737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low Fat', 'Regular', 'low fat', 'LF', 'reg'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Item_Fat_Content Column\n",
    "mart_data['Item_Fat_Content'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "503a60fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure\n",
    "sns.countplot(x = 'Item_Fat_Content', data= mart_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "00df573c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Type\n",
       "Baking Goods              648\n",
       "Breads                    251\n",
       "Breakfast                 110\n",
       "Canned                    649\n",
       "Dairy                     682\n",
       "Frozen Foods              856\n",
       "Fruits and Vegetables    1232\n",
       "Hard Drinks               214\n",
       "Health and Hygiene        520\n",
       "Household                 910\n",
       "Meat                      425\n",
       "Others                    169\n",
       "Seafood                    64\n",
       "Snack Foods              1200\n",
       "Soft Drinks               445\n",
       "Starchy Foods             148\n",
       "Name: Item_Type, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mart_data['Item_Type'].groupby(mart_data.Item_Type).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0c6de4fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sangram\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,8))\n",
    "sns.countplot(mart_data['Item_Type'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9001bb42",
   "metadata": {},
   "source": [
    "### Data Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ba669649",
   "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_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>156</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>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</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>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>662</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>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1121</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>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297</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>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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  Item_Type  \\\n",
       "0              156         9.30                 0         0.016047          4   \n",
       "1                8         5.92                 1         0.019278         14   \n",
       "2              662        17.50                 0         0.016760         10   \n",
       "3             1121        19.20                 1         0.000000          6   \n",
       "4             1297         8.93                 0         0.000000          9   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "0  249.8092                  9                       1999   \n",
       "1   48.2692                  3                       2009   \n",
       "2  141.6180                  9                       1999   \n",
       "3  182.0950                  0                       1998   \n",
       "4   53.8614                  1                       1987   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  \n",
       "0                     0            1          3735.1380  \n",
       "1                     2            2           443.4228  \n",
       "2                     0            1          2097.2700  \n",
       "3                     2            0           732.3800  \n",
       "4                     2            1           994.7052  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mart_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "29567739",
   "metadata": {},
   "outputs": [],
   "source": [
    "mart_data.drop(columns=['Outlet_Size'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b4f2ad68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5089\n",
       "Regular    2889\n",
       "LF          316\n",
       "reg         117\n",
       "low fat     112\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mart_data['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "48a3388c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mart_data.replace({'Item_Fat_Content': {'low fat':'Low Fat','LF':'Low Fat','reg':'Regular'}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "685c0576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5517\n",
       "Regular    3006\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mart_data['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbbf2a58",
   "metadata": {},
   "source": [
    "Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4e30b1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6b65cec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "79c7790c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mart_data['Item_Identifier'] = encoder.fit_transform(mart_data['Item_Identifier'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f675ade8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mart_data['Item_Fat_Content'] = encoder.fit_transform(mart_data['Item_Fat_Content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cadc9c12",
   "metadata": {},
   "outputs": [],
   "source": [
    "mart_data['Item_Type'] = encoder.fit_transform(mart_data['Item_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "50d3b327",
   "metadata": {},
   "outputs": [],
   "source": [
    "mart_data['Outlet_Identifier'] = encoder.fit_transform(mart_data['Outlet_Identifier'])\n",
    "mart_data['Outlet_Location_Type'] = encoder.fit_transform(mart_data['Outlet_Location_Type'])\n",
    "mart_data['Outlet_Type'] = encoder.fit_transform(mart_data['Outlet_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "30fa5dc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_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>156</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>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</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>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>662</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>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1121</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>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297</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>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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  Item_Type  \\\n",
       "0              156         9.30                 0         0.016047          4   \n",
       "1                8         5.92                 1         0.019278         14   \n",
       "2              662        17.50                 0         0.016760         10   \n",
       "3             1121        19.20                 1         0.000000          6   \n",
       "4             1297         8.93                 0         0.000000          9   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "0  249.8092                  9                       1999   \n",
       "1   48.2692                  3                       2009   \n",
       "2  141.6180                  9                       1999   \n",
       "3  182.0950                  0                       1998   \n",
       "4   53.8614                  1                       1987   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  \n",
       "0                     0            1          3735.1380  \n",
       "1                     2            2           443.4228  \n",
       "2                     0            1          2097.2700  \n",
       "3                     2            0           732.3800  \n",
       "4                     2            1           994.7052  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mart_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17df498b",
   "metadata": {},
   "source": [
    "Splitting features and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "96bf3b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = mart_data.drop(columns='Item_Outlet_Sales', axis=1)\n",
    "Y = mart_data['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1905f751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>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_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>156</td>\n",
       "      <td>9.300</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>5.920</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>662</td>\n",
       "      <td>17.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1121</td>\n",
       "      <td>19.200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297</td>\n",
       "      <td>8.930</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8518</th>\n",
       "      <td>370</td>\n",
       "      <td>6.865</td>\n",
       "      <td>0</td>\n",
       "      <td>0.056783</td>\n",
       "      <td>13</td>\n",
       "      <td>214.5218</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8519</th>\n",
       "      <td>897</td>\n",
       "      <td>8.380</td>\n",
       "      <td>1</td>\n",
       "      <td>0.046982</td>\n",
       "      <td>0</td>\n",
       "      <td>108.1570</td>\n",
       "      <td>7</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8520</th>\n",
       "      <td>1357</td>\n",
       "      <td>10.600</td>\n",
       "      <td>0</td>\n",
       "      <td>0.035186</td>\n",
       "      <td>8</td>\n",
       "      <td>85.1224</td>\n",
       "      <td>6</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8521</th>\n",
       "      <td>681</td>\n",
       "      <td>7.210</td>\n",
       "      <td>1</td>\n",
       "      <td>0.145221</td>\n",
       "      <td>13</td>\n",
       "      <td>103.1332</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>50</td>\n",
       "      <td>14.800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.044878</td>\n",
       "      <td>14</td>\n",
       "      <td>75.4670</td>\n",
       "      <td>8</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8523 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  \\\n",
       "0                 156        9.300                 0         0.016047   \n",
       "1                   8        5.920                 1         0.019278   \n",
       "2                 662       17.500                 0         0.016760   \n",
       "3                1121       19.200                 1         0.000000   \n",
       "4                1297        8.930                 0         0.000000   \n",
       "...               ...          ...               ...              ...   \n",
       "8518              370        6.865                 0         0.056783   \n",
       "8519              897        8.380                 1         0.046982   \n",
       "8520             1357       10.600                 0         0.035186   \n",
       "8521              681        7.210                 1         0.145221   \n",
       "8522               50       14.800                 0         0.044878   \n",
       "\n",
       "      Item_Type  Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "0             4  249.8092                  9                       1999   \n",
       "1            14   48.2692                  3                       2009   \n",
       "2            10  141.6180                  9                       1999   \n",
       "3             6  182.0950                  0                       1998   \n",
       "4             9   53.8614                  1                       1987   \n",
       "...         ...       ...                ...                        ...   \n",
       "8518         13  214.5218                  1                       1987   \n",
       "8519          0  108.1570                  7                       2002   \n",
       "8520          8   85.1224                  6                       2004   \n",
       "8521         13  103.1332                  3                       2009   \n",
       "8522         14   75.4670                  8                       1997   \n",
       "\n",
       "      Outlet_Location_Type  Outlet_Type  \n",
       "0                        0            1  \n",
       "1                        2            2  \n",
       "2                        0            1  \n",
       "3                        2            0  \n",
       "4                        2            1  \n",
       "...                    ...          ...  \n",
       "8518                     2            1  \n",
       "8519                     1            1  \n",
       "8520                     1            1  \n",
       "8521                     2            2  \n",
       "8522                     0            1  \n",
       "\n",
       "[8523 rows x 10 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "470f1c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3735.1380\n",
       "1        443.4228\n",
       "2       2097.2700\n",
       "3        732.3800\n",
       "4        994.7052\n",
       "          ...    \n",
       "8518    2778.3834\n",
       "8519     549.2850\n",
       "8520    1193.1136\n",
       "8521    1845.5976\n",
       "8522     765.6700\n",
       "Name: Item_Outlet_Sales, Length: 8523, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a625ef15",
   "metadata": {},
   "source": [
    "Spliting the data into Training data & Testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bc452d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3fa57b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 56,
   "id": "95d12c03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523, 10) (6818, 10) (1705, 10)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5dba21f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523,) (6818,) (1705,)\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape, Y_train.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a97ec3",
   "metadata": {},
   "source": [
    "Machine Learning Model Training :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "186299fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a5ea087c",
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "00d6da31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "             gamma=0, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.300000012,\n",
       "             max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5faf18ce",
   "metadata": {},
   "source": [
    "Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f852b648",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on training data\n",
    "training_data_prediction = regressor.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4f59e000",
   "metadata": {},
   "outputs": [],
   "source": [
    "# R squared value\n",
    "from sklearn import metrics\n",
    "r2_train = metrics.r2_score(Y_train, training_data_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ff248ed3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared value =  0.8589562021934799\n"
     ]
    }
   ],
   "source": [
    "print('R Squared value = ', r2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cb6c3f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on test data\n",
    "test_data_prediction = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8f117244",
   "metadata": {},
   "outputs": [],
   "source": [
    "# R squred value\n",
    "r2_test = metrics.r2_score(Y_test, test_data_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1ed9c9f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared value =  0.5304668733482136\n"
     ]
    }
   ],
   "source": [
    "print('R Squared value = ', r2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e1a05a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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