BigMart_Sales-Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LVXbklWLb_Qm"
   },
   "source": [
    "Importing the Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "fwjseYJUbhIu"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T6Qv_wKjrP1A"
   },
   "source": [
    "Data Collection and Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "AFU14Fvbcs8A"
   },
   "outputs": [],
   "source": [
    "big_mart_data = pd.read_csv(\"C:\\\\Users\\\\Supreeth\\\\Documents\\\\Train.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 307
    },
    "id": "22sDgYb0da9x",
    "outputId": "26a50c31-3dca-4fbb-b4f9-6d4c7b81a8f2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    "big_mart_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t_HuzdTTdiqb",
    "outputId": "090c93ea-7090-4d43-e566-c51aa70bedc1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of data points & number of features\n",
    "big_mart_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2cncN8QXfeXm"
   },
   "source": [
    "Categorical Features:\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": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fcsNUlTtej1f",
    "outputId": "fb1f2da1-43c0-4f09-e079-52902643686c"
   },
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "big_mart_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Y6hOo6zl1JE"
   },
   "source": [
    "*Handling Missing Values*:\n",
    "\n",
    "Mean --> average\n",
    "\n",
    "Mode --> more repeated value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0Iim8oBml0H6",
    "outputId": "77cbbd5b-bad5-46c3-dc1a-f378a2b65ca0"
   },
   "outputs": [],
   "source": [
    "# filling the missing values in \"Item_weight column\" with \"Mean\" value -> (12.857645)\n",
    "\n",
    "big_mart_data['Item_Weight'].fillna(big_mart_data['Item_Weight'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "euBwdTyMnzCr",
    "outputId": "d83c13fd-4982-40b7-e640-d4f839c73642"
   },
   "outputs": [],
   "source": [
    "# mode of \"Outlet_Size\" column\n",
    "big_mart_data['Outlet_Size'].mode()\n",
    "\n",
    "# filling the missing values in \"Outlet_Size\" column with Mode\n",
    "mode_of_Outlet_size = big_mart_data.pivot_table(values='Outlet_Size', columns='Outlet_Type', aggfunc=(lambda x: x.mode()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "uMBQoiBC_NQf"
   },
   "outputs": [],
   "source": [
    "miss_values = big_mart_data['Outlet_Size'].isnull()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zyt9Y0dd_20a",
    "outputId": "2a11018b-5b21-449c-a6da-88354f84de7e"
   },
   "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": [
    "print(miss_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "torjizDt_lRK"
   },
   "outputs": [],
   "source": [
    "big_mart_data.loc[miss_values, 'Outlet_Size'] = big_mart_data.loc[miss_values,'Outlet_Type'].apply(lambda x: mode_of_Outlet_size[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Oo1Jor0qme3C",
    "outputId": "0bedb003-cd88-4310-a4b4-b93ca9497a7c"
   },
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "big_mart_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rIC7GbtbsBRd"
   },
   "source": [
    "Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "id": "E4Vyd4StBoBE",
    "outputId": "34b03c7a-3d0d-48b2-ce3c-02a18f527766"
   },
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_mart_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GdDN9rvWuICQ"
   },
   "source": [
    "Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "1GKY5qyrtJqs"
   },
   "outputs": [],
   "source": [
    "sns.set()"
   ]
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   "execution_count": 14,
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Weight distribution\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.displot(big_mart_data['Item_Weight'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "id": "LE1yDwIxsOAl",
    "outputId": "6450d8e5-b99f-4c76-d619-b631da5b1c7f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item Visibility distribution\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.displot(big_mart_data['Item_Visibility'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "id": "-qLPjmVSsYVN",
    "outputId": "c3e5adbb-1ecf-4f0d-e9d6-300322cef34f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item MRP distribution\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.histplot(big_mart_data['Item_MRP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "id": "pEPbiUEjttpz",
    "outputId": "109abccb-e359-44f1-8731-bb790ff368d9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Outlet_Sales distribution\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.histplot(big_mart_data['Item_Outlet_Sales'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IGtSKeFnuNJy"
   },
   "source": [
    "Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "6hXxeo9dt-Js",
    "outputId": "1a80d61a-851d-4c8d-8034-e5853687e836"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QolFRUaE333xTr7zyijZt2qRdu3aptLRUOTk5Ki4uVllZmWpqalReXi5JyszM1LJly7RlyxZZlqX169dLkvLz85Weni632634+HgVFxcHa2QAwFkELRoxMTHKzs5Wnz59FBERoaFDh2rPnj0aMmSIBg8eLLvdLpfLJbfbrbq6OrW1tWn06NGSpLS0NLndbnV2dqqqqkrJycknLQcAhEbQovGNb3wjEIE9e/botddek81mU0xMTGAdh8Mhj8ejhoaGk5bHxMTI4/GoqalJUVFRstvtJy0HAISGPdhf4OOPP9YDDzygrKwshYeHa8+ePYHrLMuSzWaT3++XzWY7ZfmJP7/oy5fPZsCAqAua/1ITE9Mv1CP0GDwWwMUX1Gjs3LlTS5YsUU5OjlJTU7Vjxw55vd7A9V6vVw6HQ7GxsSctb2xslMPhUHR0tJqbm+Xz+RQeHh5Y/1wcPNgiv986ZXlv/Ybi9Taf8214LACcEBZm6/aH7aAdnjpw4IAeeughFRUVKTU1VZI0atQo1dbWau/evfL5fCotLZXT6VRcXJwiIyO1c+dOSdLmzZvldDoVERGhcePGqaysTJK0adMmOZ3OYI0MADiLoO1prF69Wu3t7SooKAgsu/vuu1VQUKDFixervb1diYmJSklJkSQVFRUpNzdXLS0tGjlypDIyMiRJeXl5ys7OVklJiQYNGqQVK1YEa2QAwFnYLMs69dhNL9Ld4an0rHUhmCh4flM457wPT+0sXBCEiUJnbNZzHJ4CzkPIDk8BAHofogEAMEY0AADGiAYAwBjRAAAYIxoAAGNEAwBgjGgAAIwRDQCAMaIBADBGNAAAxogGAMAY0QAAGCMaAABjRAMAYIxoAACMEQ0AgDGiAQAwRjQAAMaIBgDAGNEAABgjGgAAY0QDAGCMaAAAjBENAIAxogEAMEY0AADGiAYAwBjRAAAYIxoAAGNEAwBgjGgAAIwRDQCAMaIBADBGNAAAxogGAMAY0QAAGCMaAABjRAMAYIxoAACMEQ0AgDGiAQAwRjQAAMaIBgDAGNEAABgjGgAAY0QDAGCMaAAAjBENAIAxogEAMEY0AADGiAYAwFhQo9HS0qIpU6Zo//79kqSKigq5XC4lJSVp5cqVgfV2796ttLQ0JScna+nSperq6pIk1dfXa86cOUpJSdHChQvV2toazHEBAGcRtGi8//77+qd/+ift2bNHktTW1qacnBwVFxerrKxMNTU1Ki8vlyRlZmZq2bJl2rJliyzL0vr16yVJ+fn5Sk9Pl9vtVnx8vIqLi4M1LgDAQNCisX79euXl5cnhcEiSqqurNWTIEA0ePFh2u10ul0tut1t1dXVqa2vT6NGjJUlpaWlyu93q7OxUVVWVkpOTT1oOAAgde7A2/JOf/OSkyw0NDYqJiQlcdjgc8ng8pyyPiYmRx+NRU1OToqKiZLfbT1oOAAidoEXjy/x+v2w2W+CyZVmy2WxnXH7izy/68mUTAwZEnf/Ql6CYmH6hHqHH4LEALr6vLBqxsbHyer2By16vVw6H45TljY2Ncjgcio6OVnNzs3w+n8LDwwPrn6uDB1vk91unLO+t31C83uZzvg2PBYATwsJs3f6w/ZX9yu2oUaNUW1urvXv3yufzqbS0VE6nU3FxcYqMjNTOnTslSZs3b5bT6VRERITGjRunsrIySdKmTZvkdDq/qnEBAKfxle1pREZGqqCgQIsXL1Z7e7sSExOVkpIiSSoqKlJubq5aWlo0cuRIZWRkSJLy8vKUnZ2tkpISDRo0SCtWrPiqxgUAnEbQo7Ft27bA3xMSEvTqq6+ess7w4cP18ssvn7I8Li5Oa9euDep8AABzvCMcAGCMaAAAjBENAIAxogEAMEY0AADGiAYAwBjRAAAYIxoAAGNEAwBgjGgAAIwRDQCAMaIBADBGNAAAxogGAMAY0QAAGCMaAABjRAMAYIxoAACMEQ0AgDGiAQAwRjQAAMaIBgDAGNEAABgjGgAAY0QDAGCMaAAAjBENAIAxogEAMEY0AADGiAYAwBjRAAAYIxoAAGNEAwBgjGgAAIwRDQCAMaIBADBGNAAAxuyhHgBAz3Rtvz6K6BsZ6jEuqs62dh1u7gj1GJc0ogHgtCL6Rqos495Qj3FR3fnCGoloXBAOTwEAjBENAIAxogEAMEY0AADGiAYAwBjRAAAYIxoAAGNEAwBgjGgAAIwRDQCAMU4jAnzB1ddEKrJPn1CPcVG1d3To6JH2UI9xSbvm6ivUJ7J3fbvsaO/SkaOfn/PtetejAFygyD59NH/N90M9xkX1/L1PSSIaF6JPpF0/XfpyqMe4qHJ+MvO8bsfhKQCAsUsiGr///e915513KikpSevWrQv1OABw2erxh6c8Ho9WrlypjRs3qk+fPrr77rt166236qabbgr1aABw2enxexoVFRUaP368rr32Wl155ZVKTk6W2+0O9VgAcFnq8XsaDQ0NiomJCVx2OByqrq42vn1YmO2M113X/6oLmq0n6u7+dqfP1QMu8iShd76PxXVR0Rd5ktA738fiiut4XpxwzbVXXuRJQu90j8XZHh+bZVlWsAa6GEpKStTe3q5HHnlEkrR+/XrV1NRo+fLloR0MAC5DPf7wVGxsrLxeb+Cy1+uVw+EI4UQAcPnq8dGYMGGC3n77bR06dEiff/65tm7dKqfTGeqxAOCy1ONf0xg4cKAeffRRZWRkqLOzUzNnztS3vvWtUI8FAJelHv+aBgCg5+jxh6cAAD0H0QAAGCMaAABjRAMAYIxoAACMEY3T2L59u+bOnRu07Q8bNkzTpk076b/333//jOvv27dPOTk5QZvnbPbv36/4+PjArC6XS5MnT9aqVasu6teZPHmy9u/ff1G3GUwX+3lSX1+v5ORkTZs2TS0tLaddJ9TPhQt1usfsy8+vE/8dOHAgRFOiOz3+fRq91ebNm43Xra+v1759+4I4zdk5HI6TZvZ4PEpOTlZqaqqGDh0awsl6jx07dig+Pl4///nPz7hOT3guBMOXn1/oudjTOEe/+MUvdOedd8rlcqmgoEA+n08PPvigysvLJUkrVqzQggULJB0/2eKUKVOMt+3xeHTfffdp1qxZmjhxop566ilJ0o9//GPV1NQoPz//4t+h8+T1emVZlq666io9++yzmjFjhqZOnarCwkKdeOvPCy+8oKSkJN11113KzMzU008/Len4ntYJGzduVHZ29knbbmlp0ZIlSzR79mxNmjRJOTk5sixL27dv18yZM5WWlqYf/vCHX92dNVBbW6u5c+fK5XJp9uzZqq6uVk1Njb73ve9Jko4dO6b4+PjAHuWyZcv02muvBW6/e/duPfnkk/rLX/6iZcuWXVLPBZybLz6PFy9erB/+8IdKS0vTtGnTVFpaKknq7OxUTk6OkpOTlZGRoXnz5mn79u0hnvw49jTOQXl5ubZt26YNGzYoIiJCixcv1osvvqjExERVVlYqMTFR77zzjj777DP5fD795S9/UWJi4mm3NW3atMDfb731VuXk5Ki0tFRTpkzRjBkz1NzcrMTERM2dO1e5ubl65plnlJeX91Xd1VM0NDRo2rRpam9vV1NTk775zW/qmWee0UcffaSamhq9/PLLstlsyszM1Kuvvqphw4Zp3bp12rhxoyIiIjR37lzdcMMNRl/rjTfe0IgRI7Rq1Sp1dHQoNTVVu3btkiTt2bNHr7/+uvr16xfMu3vOMjMz9S//8i9KSkrSe++9p+9///vasmWLGhoa1NzcrL/+9a+6+uqrtWPHDo0aNUqVlZXKysoK3H7EiBFasmSJduzYoeXLl2v16tU99rkQDCeeXye4XK7AD1+90Ynn8S9/+Us5HA797Gc/U0tLi+6++26NGjVKb7zxhj7//HO53W7V19fL5XKFeuQAonEOKisrlZqaqiuuuEKSdNddd2nTpk1aunSpFi5cGDgOPWzYMO3atUt//vOfz3jM+3S74vfdd58qKyu1evVqffzxx+rs7NTnn5/7B78Hw4nDB36/XwUFBfrkk09022236YknnlB1dbXS0tIkSW1tbfra176mQ4cOadKkSYqKipIkpaam6ujRo0Zfa8qUKaqurtbzzz+vTz/9VIcPH9axY8ckSTfeeGOPC0Zra6v+93//V0lJSZKk0aNH65prrtGnn36qCRMmaPv27Xr33Xc1b948VVVVadKkSRo0aFDgsTmdnvxcCIbL7fDUiedxRUWF2tratGHDBknH90g//vhjvfXWW5o1a5ZsNpvi4uKUkJAQ4on/H9E4B36//5RlXV1dGjRokPx+v7Zu3apvf/vbuu6661RZWaldu3ZpzJgxxtsvKCjQvn37NGXKFP3DP/yDKioq1NPO8hIWFqasrCxNnz5dq1evls/n07x583TvvfdKko4eParw8HC9/PLLp328TrAsSzabTV1dXadct3btWm3ZskWzZs3ShAkT9NFHHwUeh759+wbnjl2A0/0/sixLPp9PEydO1Ntvv62amho999xzeumll/T6669r0qRJ3W7zUngu4PydeB77/X498cQTGjlypCSpsbFR11xzjTZs2NDtv59Q4jWNczB+/Hj94Q9/UFtbm7q6urRhwwaNHz9ekuR0OlVSUqLvfOc7Gj9+vNauXatRo0YpPDzcePtvvfWW7rvvPt1xxx2qra2Vx+OR3+9XeHj4ab+5hordbldWVpaKi4t1yy23aPPmzWptbVVXV5ceeughbdmyRQkJCSovL1dLS4s6Ojq0detW2WzHP9ylf//++vjjj2VZlrZt23bK9t966y3Nnj1bU6dOVXt7uz788MMe+w9IkqKionT99ddr69atkqT33ntPjY2N+sY3vqHbbrtNb775psLCwtSvXz+NGDFCL7zwgiZOnNjtNi+V5wIuzPjx4/Xb3/5W0vFDdFOnTtWBAwc0YcIElZWVybIseTwe7dixI/DvJ9TY0ziDd95556S9BJfLpeXLl2v37t2666671NXVpdtvv1333HOPJGnixIlas2aNxo4dqyuvvFKdnZ1n/Wnyyx544AFlZWWpb9++io2NVXx8vPbv368RI0aoublZmZmZeuKJJy7q/TxfTqdTY8aM0TvvvKOkpCTNmjVLPp9P3/3udzVjxgzZbDZlZGRo9uzZuvLKK9W/f39FRkZKkn7wgx/owQcf1HXXXaexY8eqqanppG3PmzdPjz32mJ599llFRUVpzJgx2r9/v/FrIqHwxBNP6LHHHtPTTz+tiIgIPf300+rTp4/69Omj2NhYffOb35R0/JvE3/72N/3d3/1dt9u7lJ4L5+rL/7a+/e1vh3Ca0Hr44Yf12GOPacqUKfL5fMrMzNQNN9ygWbNm6cMPP5TL5VJMTIy+9rWv9Zi9bM5yi6Cora1VeXm55s+fL0lauHChvve972ny5MmhHQy4BLzxxhuyLEuTJk1Sc3Ozpk+frg0bNujaa68N9WjsaSA44uLi9MEHH2jKlCmy2Wy6/fbbz3nPC7hcDR06VFlZWXryySclSUuWLOkRwZDY0wAAnANeCAcAGCMaAABjRAMAYIxoAACMEQ1c0iZPnqwPPvhAzzzzjP77v//7K/maw4YNk8vlOuk03kuXLu32Ns3NzcrIyDDafnt7u5588klNnz49cCr6Z5999oLeEb5v3z4tXrz4vG8vHf810BMnTsTli1+5Ra+wfft23XTTTV/Z1/v1r3+t6Oho4/WPHDmiDz744KzrWZalRYsW6cYbb9RLL72kyMhINTU16YEHHtCxY8f0yCOPnNe89fX1qq2tPa/bnvDBBx/oyJEjF7QNXPqIBi555eXlqqmpUWFhocLDw5WYmKiioiJVVVXJ5/PplltuUW5urqKiojR58mRNmTJFlZWVOnLkiBYsWKB3331Xu3btkt1uV0lJiQYOHHjes7z88st66aWX1NnZqSNHjuj+++9Xenq6fvSjH6mtrU3Tpk3Txo0bz3h6maqqKn366ad69tlnA+v0799fhYWFqqurkyR99tlneuyxx1RXVyfLsjR9+nQtWLBA+/fv1/z585WYmKj3339fR48eVWZmpiZPnqzc3NzA6dZXr16td999V0VFRfr8888VFhamhx9+WJMmTdLGjRv1xz/+UWFhYdq7d6/69u0bOAPriy++KJ/Pp379+unRRx8978cIlzgLuIRNmjTJqq6utu655x7rtddesyzLsp5++mmroKDA8vv9lmVZ1s9//nMrLy8vsP5Pf/pTy7Is6w9/+IM1fPhwa/fu3ZZlWdaiRYuskpKSs37Nm2++2ZoyZYo1derUwH+NjY1WS0uLNWvWLOvQoUOWZVnWX//6V2v06NGWZVnWvn37An/vzurVq60lS5Z0u86cOXOsX/3qV5ZlWdbRo0ctl8tllZaWWvv27bNuvvlma9u2bZZlWZbb7bYmTpxoWZZlVVZWWqmpqZZlWdbhw4etpKQka9++fZZlWdZnn31mOZ1Oq66uztqwYYM1duxY68CBA5ZlWdby5cutrKwsy7Isa9WqVVZ+fv5Z7wN6N/Y00Ou88cYbam5uVkVFhaTjH2gzYMCAwPUnTmE+ePBgXXfddRo+fLgk6YYbbjA+/HKmw1O/+MUvVF5erj179ujDDz8MnNLdVFhYWLevXRw7dkzvvvuufvWrX0mS+vXrp7S0NP35z3/WqFGjFBEREfgMl1tuuUWHDx8+ZRvvvfeevF6vHnroocAym82m//mf/5EkjRw5UrGxsYFt/PGPfzyn+4DejWig1/H7/crJyQl882xtbVV7e3vg+j59+gT+HhERcdG+7meffabZs2dr1qxZGjt2rFJSUvT666+f0zZGjRqlX//61/L5fCcdwqqurtbatWuVl5d3SlT8fn/gzLcREREKCzv++y1nOiuqz+fT0KFD9bvf/S6wzOPxKDo6Wr///e9POjGezWbjlOw4Cb89hV7hi6cMv/3227Vu3Tp1dHTI7/fr3/7t37RixYqgz1BTU6Po6GgtWrRIt99+eyAYPp9PdrtdPp/vrN+Ax4wZo69//et6/PHHA6FrbGzUj3/8Y11//fWKiorSqFGjtG7dOknHfytr06ZNmjBhQrfbDQ8PV2dnp6TjHxK1d+9eVVVVSTr+UbPJycnyeDxn3QanZQfRQK8wefJkrVixQq+88ooWLVqkuLg4zZgxQ3feeacsyzrlc8iD4bbbbtPAgQOVkpKiO+64QwcOHFB0dLT27t2rmJgYfetb31Jqauopp4L/slWrVkmS0tLSNHXqVM2fP19JSUlasmSJJKmoqEhvv/22XC6XZs6cqaSkpMAnJ57JTTfdpMjISM2cOVP9+/fXqlWrVFhYqKlTpyorK0uFhYW6/vrru93G+PHj9eabb+rf//3fz+FRQW/DCQsBAMZ4TQP4kkceeeSM72lYuXKlvv71r1/Q9tPT09Xa2nra69atW9ftZ4cDocaeBgDAGK9pAACMEQ0AgDGiAQAwRjQAAMaIBgDA2P8BjF1GdK7u6BwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Fat_Content column\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.countplot(x='Item_Fat_Content', data=big_mart_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "id": "RHm0Sh8vuyKM",
    "outputId": "9610a8eb-b002-45d9-e385-cbabb3c816c7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Type column\n",
    "plt.figure(figsize=(30,6))\n",
    "sns.countplot(x='Item_Type', data=big_mart_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 395
    },
    "id": "nLkJIy3NF2Gd",
    "outputId": "0d924369-3dbf-4a1b-a4e6-d30ec95449cb"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlet_Size column\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.countplot(x='Outlet_Size', data=big_mart_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "adBk67n8GjAH"
   },
   "source": [
    "Data Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_mart_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i2N8zbcdGnUy",
    "outputId": "008291ee-060f-4597-e8e7-3e485e073455"
   },
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_mart_data['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "29VZp_RaG2W-"
   },
   "outputs": [],
   "source": [
    "big_mart_data.replace({'Item_Fat_Content': {'low fat':'Low Fat','LF':'Low Fat', 'reg':'Regular'}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-Ejg24CTHRXJ",
    "outputId": "fe1504ee-9fdc-48bc-ed87-7dd816309aa9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5517\n",
       "Regular    3006\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_mart_data['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xTN9xWEnHhhQ"
   },
   "source": [
    "Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "SxHOpkH0Hc75"
   },
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "qAwLCxCWHsl_"
   },
   "outputs": [],
   "source": [
    "big_mart_data['Item_Identifier'] = encoder.fit_transform(big_mart_data['Item_Identifier'])\n",
    "\n",
    "big_mart_data['Item_Fat_Content'] = encoder.fit_transform(big_mart_data['Item_Fat_Content'])\n",
    "\n",
    "big_mart_data['Item_Type'] = encoder.fit_transform(big_mart_data['Item_Type'])\n",
    "\n",
    "big_mart_data['Outlet_Identifier'] = encoder.fit_transform(big_mart_data['Outlet_Identifier'])\n",
    "\n",
    "big_mart_data['Outlet_Size'] = encoder.fit_transform(big_mart_data['Outlet_Size'])\n",
    "\n",
    "big_mart_data['Outlet_Location_Type'] = encoder.fit_transform(big_mart_data['Outlet_Location_Type'])\n",
    "\n",
    "big_mart_data['Outlet_Type'] = encoder.fit_transform(big_mart_data['Outlet_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "id": "SzL_IgO1IVUv",
    "outputId": "95cc83a0-723f-48a0-a47a-47244dd22cf6"
   },
   "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>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>1</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>1</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>1</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>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>0</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  Outlet_Size  \\\n",
       "0  249.8092                  9                       1999            1   \n",
       "1   48.2692                  3                       2009            1   \n",
       "2  141.6180                  9                       1999            1   \n",
       "3  182.0950                  0                       1998            2   \n",
       "4   53.8614                  1                       1987            0   \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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_mart_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jr-0iqKOIdUD"
   },
   "source": [
    "Splitting features and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "uwe-H2o2IXzJ"
   },
   "outputs": [],
   "source": [
    "X = big_mart_data.drop(columns='Item_Outlet_Sales', axis=1)\n",
    "Y = big_mart_data['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TKDLyvImIuTo",
    "outputId": "ed8d8508-d091-41ba-a298-c7ed5a8ccd38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      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_Size  Outlet_Location_Type  Outlet_Type  \n",
      "0               1                     0            1  \n",
      "1               1                     2            2  \n",
      "2               1                     0            1  \n",
      "3               2                     2            0  \n",
      "4               0                     2            1  \n",
      "...           ...                   ...          ...  \n",
      "8518            0                     2            1  \n",
      "8519            2                     1            1  \n",
      "8520            2                     1            1  \n",
      "8521            1                     2            2  \n",
      "8522            2                     0            1  \n",
      "\n",
      "[8523 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WgbKCgaNIvd9",
    "outputId": "f9074dfe-2e97-49f5-a73e-fea2960dc7a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "quPO-6hlIz3Z"
   },
   "source": [
    "Splitting the data into Training data & Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "ahDPS_EYIxRX"
   },
   "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": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Re4BSHovJBB-",
    "outputId": "a870362e-eb4d-4378-9678-f7f4feca6bfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523, 11) (6818, 11) (1705, 11)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xl3IeS5nJaW3"
   },
   "source": [
    "Machine Learning Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m0xSe9ZcJe75"
   },
   "source": [
    "XGBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "OM2K1_zxJVq7"
   },
   "outputs": [],
   "source": [
    "regressor = XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dKuVHDn2JmiW",
    "outputId": "4d751e24-b4fa-4b14-8bc8-284a8aee75b6"
   },
   "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=12,\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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GKD3Ip4KJ4lS"
   },
   "source": [
    "Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "IZIMhuxJJ2m0"
   },
   "outputs": [],
   "source": [
    "# prediction on training data\n",
    "training_data_prediction = regressor.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "ebkXEag7KHsI"
   },
   "outputs": [],
   "source": [
    "# R squared Value\n",
    "r2_train = metrics.r2_score(Y_train, training_data_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VnIOWZEdKTet",
    "outputId": "235bc76e-5336-484c-c2ea-ef1b166f89ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared value =  0.8639680373364909\n"
     ]
    }
   ],
   "source": [
    "print('R Squared value = ', r2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "nLrj2_8SKsHM"
   },
   "outputs": [],
   "source": [
    "# prediction on test data\n",
    "test_data_prediction = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "E_R3MjSOK2-x"
   },
   "outputs": [],
   "source": [
    "# R squared Value\n",
    "r2_test = metrics.r2_score(Y_test, test_data_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zQxuP6v1K-Hh",
    "outputId": "c7f2a4a1-afc6-4783-ce8c-cfa3d1cd57f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared value =  0.5233136709735687\n"
     ]
    }
   ],
   "source": [
    "print('R Squared value = ', r2_test)"
   ]
  }
 ],
 "metadata": {
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  "language_info": {
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