sales_predictor algo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:\\\\Users\\\\banga\\\\Downloads\\\\train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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_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>302</th>\n",
       "      <td>NCQ54</td>\n",
       "      <td>17.700</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.012540</td>\n",
       "      <td>Household</td>\n",
       "      <td>167.0474</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>2004</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>5895.6590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>NCD06</td>\n",
       "      <td>13.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.099730</td>\n",
       "      <td>Household</td>\n",
       "      <td>46.3060</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>559.2720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>NCZ18</td>\n",
       "      <td>7.825</td>\n",
       "      <td>low fat</td>\n",
       "      <td>0.186357</td>\n",
       "      <td>Household</td>\n",
       "      <td>254.3698</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>5580.7356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4602</th>\n",
       "      <td>FDV08</td>\n",
       "      <td>7.350</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.028711</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>39.9454</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>377.5086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7873</th>\n",
       "      <td>NCZ05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.101782</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>104.6990</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>103.1990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "302            NCQ54       17.700          Low Fat         0.012540   \n",
       "287            NCD06       13.000          Low Fat         0.099730   \n",
       "74             NCZ18        7.825          low fat         0.186357   \n",
       "4602           FDV08        7.350          Low Fat         0.028711   \n",
       "7873           NCZ05          NaN          Low Fat         0.101782   \n",
       "\n",
       "                  Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "302               Household  167.0474            OUT035   \n",
       "287               Household   46.3060            OUT018   \n",
       "74                Household  254.3698            OUT049   \n",
       "4602  Fruits and Vegetables   39.9454            OUT018   \n",
       "7873     Health and Hygiene  104.6990            OUT019   \n",
       "\n",
       "      Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "302                        2004       Small               Tier 2   \n",
       "287                        2009      Medium               Tier 3   \n",
       "74                         1999      Medium               Tier 1   \n",
       "4602                       2009      Medium               Tier 3   \n",
       "7873                       1985       Small               Tier 1   \n",
       "\n",
       "            Outlet_Type  Item_Outlet_Sales  \n",
       "302   Supermarket Type1          5895.6590  \n",
       "287   Supermarket Type2           559.2720  \n",
       "74    Supermarket Type1          5580.7356  \n",
       "4602  Supermarket Type2           377.5086  \n",
       "7873      Grocery Store           103.1990  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Shape of Our Dataset (Number of Rows And Number of Columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Information About Our Dataset Like Total Number Rows, Total Number of Columns, Datatypes of Each Column And Memory Requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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_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>7060.000000</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>4.643456</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>8.773750</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>12.600000</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>16.850000</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  7060.000000      8523.000000  8523.000000                8523.000000   \n",
       "mean     12.857645         0.066132   140.992782                1997.831867   \n",
       "std       4.643456         0.051598    62.275067                   8.371760   \n",
       "min       4.555000         0.000000    31.290000                1985.000000   \n",
       "25%       8.773750         0.026989    93.826500                1987.000000   \n",
       "50%      12.600000         0.053931   143.012800                1999.000000   \n",
       "75%      16.850000         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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Null Values In The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Identifier               0.000000\n",
      "Item_Weight                  17.165317\n",
      "Item_Fat_Content              0.000000\n",
      "Item_Visibility               0.000000\n",
      "Item_Type                     0.000000\n",
      "Item_MRP                      0.000000\n",
      "Outlet_Identifier             0.000000\n",
      "Outlet_Establishment_Year     0.000000\n",
      "Outlet_Size                  28.276428\n",
      "Outlet_Location_Type          0.000000\n",
      "Outlet_Type                   0.000000\n",
      "Item_Outlet_Sales             0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "per = data.isnull().sum() * 100 / len(data)\n",
    "print(per)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taking Care of Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling The missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        9.300\n",
       "1        5.920\n",
       "2       17.500\n",
       "3       19.200\n",
       "4        8.930\n",
       "         ...  \n",
       "8518     6.865\n",
       "8519     8.380\n",
       "8520    10.600\n",
       "8521     7.210\n",
       "8522    14.800\n",
       "Name: Item_Weight, Length: 8523, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Medium\n",
       "1       Medium\n",
       "2       Medium\n",
       "3          NaN\n",
       "4         High\n",
       "         ...  \n",
       "8518      High\n",
       "8519       NaN\n",
       "8520     Small\n",
       "8521    Medium\n",
       "8522     Small\n",
       "Name: Outlet_Size, Length: 8523, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Outlet_Size']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_weight = data['Item_Weight'].mean()\n",
    "median_weight = data['Item_Weight'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.857645184135976 12.6\n"
     ]
    }
   ],
   "source": [
    "print(mean_weight,median_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Item_Weight_mean']=data['Item_Weight'].fillna(mean_weight)\n",
    "data['Item_Weight_median']=data['Item_Weight'].fillna(median_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Weight_mean</th>\n",
       "      <th>Item_Weight_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.3</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>3735.138</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility Item_Type  \\\n",
       "0           FDA15          9.3          Low Fat         0.016047     Dairy   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  249.8092            OUT049                       1999      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \\\n",
       "0               Tier 1  Supermarket Type1           3735.138   \n",
       "\n",
       "   Item_Weight_mean  Item_Weight_median  \n",
       "0               9.3                 9.3  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Weight variable variance 21.561688259836558\n",
      "Item Weight variance after mean imputation 17.86012173506058\n",
      "Item Weight variance after median imputation 17.869561454073647\n"
     ]
    }
   ],
   "source": [
    "print(\"Original Weight variable variance\",data['Item_Weight'].var())\n",
    "print(\"Item Weight variance after mean imputation\",data['Item_Weight_mean'].var())\n",
    "print(\"Item Weight variance after median imputation\",data['Item_Weight_median'].var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Item_Weight'].plot(kind = \"kde\",label=\"Original\")\n",
    "\n",
    "data['Item_Weight_mean'].plot(kind = \"kde\",label = \"Mean\")\n",
    "\n",
    "data['Item_Weight_median'].plot(kind = \"kde\",label = \"Median\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Item_Weight','Item_Weight_mean','Item_Weight_median']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Item_Weight_interploate']=data['Item_Weight'].interpolate(method=\"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Item_Weight'].plot(kind = \"kde\",label=\"Original\")\n",
    "\n",
    "data['Item_Weight_interploate'].plot(kind = \"kde\",label = \"interploate\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Imputaion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNNImputer(n_neighbors=10,weights=\"distance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['knn_imputer']= knn.fit_transform(data[['Item_Weight']]).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Item_Weight'].plot(kind = \"kde\",label=\"Original\")\n",
    "\n",
    "data['knn_imputer'].plot(kind = \"kde\",label = \"KNN imputer\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['Item_Weight','Item_Weight_mean','Item_Weight_median','knn_imputer'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Weight_interploate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</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>3735.138</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Item_Fat_Content  Item_Visibility Item_Type  Item_MRP  \\\n",
       "0           FDA15          Low Fat         0.016047     Dairy  249.8092   \n",
       "\n",
       "  Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0            OUT049                       1999      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \\\n",
       "0               Tier 1  Supermarket Type1           3735.138   \n",
       "\n",
       "   Item_Weight_interploate  \n",
       "0                      9.3  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Fat_Content                0\n",
       "Item_Visibility                 0\n",
       "Item_Type                       0\n",
       "Item_MRP                        0\n",
       "Outlet_Identifier               0\n",
       "Outlet_Establishment_Year       0\n",
       "Outlet_Size                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "Item_Weight_interploate         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlet_Size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Size\n",
       "Medium    2793\n",
       "Small     2388\n",
       "High       932\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Outlet_Size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Type\n",
       "Supermarket Type1    5577\n",
       "Grocery Store        1083\n",
       "Supermarket Type3     935\n",
       "Supermarket Type2     928\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Outlet_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_outlet = data.pivot_table(values='Outlet_Size',columns='Outlet_Type',aggfunc=(lambda x:x.mode()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_outlet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_values = data['Outlet_Size'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[missing_values,'Outlet_Size'] = data.loc[missing_values,'Outlet_Type'].apply(lambda x :mode_outlet[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              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",
       "Item_Weight_interploate      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item_Fat_Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Identifier', 'Item_Fat_Content', 'Item_Visibility', 'Item_Type',\n",
       "       'Item_MRP', 'Outlet_Identifier', 'Outlet_Establishment_Year',\n",
       "       'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type',\n",
       "       'Item_Outlet_Sales', 'Item_Weight_interploate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content\n",
       "Low Fat    5089\n",
       "Regular    2889\n",
       "LF          316\n",
       "reg         117\n",
       "low fat     112\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace({'Item_Fat_Content':{'Low Fat':'LF','low fat':'LF','reg':'Regular'}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content\n",
       "LF         5517\n",
       "Regular    3006\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item_Visibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Identifier', 'Item_Fat_Content', 'Item_Visibility', 'Item_Type',\n",
       "       'Item_MRP', 'Outlet_Identifier', 'Outlet_Establishment_Year',\n",
       "       'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type',\n",
       "       'Item_Outlet_Sales', 'Item_Weight_interploate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Visibility\n",
       "0.000000    526\n",
       "0.076975      3\n",
       "0.162462      2\n",
       "0.076841      2\n",
       "0.073562      2\n",
       "           ... \n",
       "0.013957      1\n",
       "0.110460      1\n",
       "0.124646      1\n",
       "0.054142      1\n",
       "0.044878      1\n",
       "Name: count, Length: 7880, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Visibility'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Item_Visibility_interpolate']=data['Item_Visibility'].replace(0,np.nan).interpolate(method='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Weight_interploate</th>\n",
       "      <th>Item_Visibility_interpolate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>LF</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>3735.138</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.016047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Item_Fat_Content  Item_Visibility Item_Type  Item_MRP  \\\n",
       "0           FDA15               LF         0.016047     Dairy  249.8092   \n",
       "\n",
       "  Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0            OUT049                       1999      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \\\n",
       "0               Tier 1  Supermarket Type1           3735.138   \n",
       "\n",
       "   Item_Weight_interploate  Item_Visibility_interpolate  \n",
       "0                      9.3                     0.016047  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Visibility_interpolate\n",
       "0.076975    3\n",
       "0.044024    2\n",
       "0.040912    2\n",
       "0.076856    2\n",
       "0.078759    2\n",
       "           ..\n",
       "0.021011    1\n",
       "0.099189    1\n",
       "0.076866    1\n",
       "0.014116    1\n",
       "0.044878    1\n",
       "Name: count, Length: 8405, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Visibility_interpolate'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Item_Visibility'].plot(kind=\"kde\",label=\"Original\")\n",
    "\n",
    "data['Item_Visibility_interpolate'].plot(kind=\"kde\",color='red',label=\"Interpolate\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop('Item_Visibility',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Weight_interploate</th>\n",
       "      <th>Item_Visibility_interpolate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>LF</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>3735.138</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.016047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Item_Fat_Content Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0           FDA15               LF     Dairy  249.8092            OUT049   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  Item_Weight_interploate  \\\n",
       "0  Supermarket Type1           3735.138                      9.3   \n",
       "\n",
       "   Item_Visibility_interpolate  \n",
       "0                     0.016047  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Identifier', 'Item_Fat_Content', 'Item_Type', 'Item_MRP',\n",
       "       'Outlet_Identifier', 'Outlet_Establishment_Year', 'Outlet_Size',\n",
       "       'Outlet_Location_Type', 'Outlet_Type', 'Item_Outlet_Sales',\n",
       "       'Item_Weight_interploate', 'Item_Visibility_interpolate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Type\n",
       "Fruits and Vegetables    1232\n",
       "Snack Foods              1200\n",
       "Household                 910\n",
       "Frozen Foods              856\n",
       "Dairy                     682\n",
       "Canned                    649\n",
       "Baking Goods              648\n",
       "Health and Hygiene        520\n",
       "Soft Drinks               445\n",
       "Meat                      425\n",
       "Breads                    251\n",
       "Hard Drinks               214\n",
       "Others                    169\n",
       "Starchy Foods             148\n",
       "Breakfast                 110\n",
       "Seafood                    64\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item_Identifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Identifier', 'Item_Fat_Content', 'Item_Type', 'Item_MRP',\n",
       "       'Outlet_Identifier', 'Outlet_Establishment_Year', 'Outlet_Size',\n",
       "       'Outlet_Location_Type', 'Outlet_Type', 'Item_Outlet_Sales',\n",
       "       'Item_Weight_interploate', 'Item_Visibility_interpolate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier\n",
       "DRM35    4\n",
       "NCW54    5\n",
       "FDW34    7\n",
       "FDB47    2\n",
       "NCM29    7\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Identifier'].value_counts().sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Item_Identifier'] =data['Item_Identifier'].apply(lambda x : x[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier\n",
       "FD    6125\n",
       "NC    1599\n",
       "DR     799\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Identifier'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlet_Establishment_Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Identifier', 'Item_Fat_Content', 'Item_Type', 'Item_MRP',\n",
       "       'Outlet_Identifier', 'Outlet_Establishment_Year', 'Outlet_Size',\n",
       "       'Outlet_Location_Type', 'Outlet_Type', 'Item_Outlet_Sales',\n",
       "       'Item_Weight_interploate', 'Item_Visibility_interpolate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1999\n",
       "1       2009\n",
       "2       1999\n",
       "3       1998\n",
       "4       1987\n",
       "        ... \n",
       "8518    1987\n",
       "8519    2002\n",
       "8520    2004\n",
       "8521    2009\n",
       "8522    1997\n",
       "Name: Outlet_Establishment_Year, Length: 8523, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Outlet_Establishment_Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_year = dt.datetime.today().year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2024"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Outlet_age']= current_year - data['Outlet_Establishment_Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Weight_interploate</th>\n",
       "      <th>Item_Visibility_interpolate</th>\n",
       "      <th>Outlet_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FD</td>\n",
       "      <td>LF</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>3735.138</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Item_Fat_Content Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0              FD               LF     Dairy  249.8092            OUT049   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  Item_Weight_interploate  \\\n",
       "0  Supermarket Type1           3735.138                      9.3   \n",
       "\n",
       "   Item_Visibility_interpolate  Outlet_age  \n",
       "0                     0.016047          25  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop('Outlet_Establishment_Year',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Weight_interploate</th>\n",
       "      <th>Item_Visibility_interpolate</th>\n",
       "      <th>Outlet_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FD</td>\n",
       "      <td>LF</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DR</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FD</td>\n",
       "      <td>LF</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FD</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>19.20</td>\n",
       "      <td>0.015755</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NC</td>\n",
       "      <td>LF</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0.014751</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Item_Fat_Content              Item_Type  Item_MRP  \\\n",
       "0              FD               LF                  Dairy  249.8092   \n",
       "1              DR          Regular            Soft Drinks   48.2692   \n",
       "2              FD               LF                   Meat  141.6180   \n",
       "3              FD          Regular  Fruits and Vegetables  182.0950   \n",
       "4              NC               LF              Household   53.8614   \n",
       "\n",
       "  Outlet_Identifier Outlet_Size Outlet_Location_Type        Outlet_Type  \\\n",
       "0            OUT049      Medium               Tier 1  Supermarket Type1   \n",
       "1            OUT018      Medium               Tier 3  Supermarket Type2   \n",
       "2            OUT049      Medium               Tier 1  Supermarket Type1   \n",
       "3            OUT010       Small               Tier 3      Grocery Store   \n",
       "4            OUT013        High               Tier 3  Supermarket Type1   \n",
       "\n",
       "   Item_Outlet_Sales  Item_Weight_interploate  Item_Visibility_interpolate  \\\n",
       "0          3735.1380                     9.30                     0.016047   \n",
       "1           443.4228                     5.92                     0.019278   \n",
       "2          2097.2700                    17.50                     0.016760   \n",
       "3           732.3800                    19.20                     0.015755   \n",
       "4           994.7052                     8.93                     0.014751   \n",
       "\n",
       "   Outlet_age  \n",
       "0          25  \n",
       "1          15  \n",
       "2          25  \n",
       "3          26  \n",
       "4          37  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array(['DR', 'FD', 'NC'], dtype=object)]\n",
      "[array(['LF', 'Regular'], dtype=object)]\n",
      "[array(['Baking Goods', 'Breads', 'Breakfast', 'Canned', 'Dairy',\n",
      "       'Frozen Foods', 'Fruits and Vegetables', 'Hard Drinks',\n",
      "       'Health and Hygiene', 'Household', 'Meat', 'Others', 'Seafood',\n",
      "       'Snack Foods', 'Soft Drinks', 'Starchy Foods'], dtype=object)]\n",
      "[array(['OUT010', 'OUT013', 'OUT017', 'OUT018', 'OUT019', 'OUT027',\n",
      "       'OUT035', 'OUT045', 'OUT046', 'OUT049'], dtype=object)]\n",
      "[array(['High', 'Medium', 'Small'], dtype=object)]\n",
      "[array(['Tier 1', 'Tier 2', 'Tier 3'], dtype=object)]\n",
      "[array(['Grocery Store', 'Supermarket Type1', 'Supermarket Type2',\n",
      "       'Supermarket Type3'], dtype=object)]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "data_encoded = data.copy()\n",
    "\n",
    "cat_cols = data.select_dtypes(include=['object']).columns\n",
    "\n",
    "for col in cat_cols:\n",
    "    oe = OrdinalEncoder()\n",
    "    data_encoded[col]=oe.fit_transform(data_encoded[[col]])\n",
    "    print(oe.categories_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Weight_interploate</th>\n",
       "      <th>Item_Visibility_interpolate</th>\n",
       "      <th>Outlet_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Fat_Content  Item_Type  Item_MRP  Outlet_Identifier  \\\n",
       "0              1.0               0.0        4.0  249.8092                9.0   \n",
       "1              0.0               1.0       14.0   48.2692                3.0   \n",
       "2              1.0               0.0       10.0  141.6180                9.0   \n",
       "\n",
       "   Outlet_Size  Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  \\\n",
       "0          1.0                   0.0          1.0          3735.1380   \n",
       "1          1.0                   2.0          2.0           443.4228   \n",
       "2          1.0                   0.0          1.0          2097.2700   \n",
       "\n",
       "   Item_Weight_interploate  Item_Visibility_interpolate  Outlet_age  \n",
       "0                     9.30                     0.016047          25  \n",
       "1                     5.92                     0.019278          15  \n",
       "2                    17.50                     0.016760          25  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_encoded.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_encoded.drop('Item_Outlet_Sales',axis=1)\n",
    "y = data_encoded['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5549992903957147\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=100,random_state=42)\n",
    "scores = cross_val_score(rf,X,y,cv=5,scoring='r2')\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in a:\\py\\lib\\site-packages (2.0.3)\n",
      "Requirement already satisfied: numpy in a:\\py\\lib\\site-packages (from xgboost) (1.24.3)\n",
      "Requirement already satisfied: scipy in a:\\py\\lib\\site-packages (from xgboost) (1.10.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 21.2.3; however, version 23.3.2 is available.\n",
      "You should consider upgrading via the 'A:\\py\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBRFRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5954067732342189\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRFRegressor\n",
    "\n",
    "xg = XGBRFRegressor(n_estimators=100,random_state=42)\n",
    "scores = cross_val_score(xg,X,y,cv=5,scoring='r2')\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>feature</th>\n",
       "      <th>XGBRF_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Outlet_Type</td>\n",
       "      <td>0.349864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Outlet_Size</td>\n",
       "      <td>0.192658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Outlet_age</td>\n",
       "      <td>0.175040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Item_MRP</td>\n",
       "      <td>0.131012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Outlet_Identifier</td>\n",
       "      <td>0.130735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Outlet_Location_Type</td>\n",
       "      <td>0.013184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Item_Visibility_interpolate</td>\n",
       "      <td>0.002493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Item_Weight_interploate</td>\n",
       "      <td>0.001770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Item_Type</td>\n",
       "      <td>0.001566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Item_Identifier</td>\n",
       "      <td>0.000999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Item_Fat_Content</td>\n",
       "      <td>0.000680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        feature  XGBRF_importance\n",
       "7                   Outlet_Type          0.349864\n",
       "5                   Outlet_Size          0.192658\n",
       "10                   Outlet_age          0.175040\n",
       "3                      Item_MRP          0.131012\n",
       "4             Outlet_Identifier          0.130735\n",
       "6          Outlet_Location_Type          0.013184\n",
       "9   Item_Visibility_interpolate          0.002493\n",
       "8       Item_Weight_interploate          0.001770\n",
       "2                     Item_Type          0.001566\n",
       "0               Item_Identifier          0.000999\n",
       "1              Item_Fat_Content          0.000680"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg = XGBRFRegressor(n_estimators=100,random_state=42)\n",
    "\n",
    "xg1 = xg.fit(X,y)\n",
    "pd.DataFrame({\n",
    "    'feature':X.columns,\n",
    "    'XGBRF_importance':xg1.feature_importances_\n",
    "    \n",
    "}).sort_values(by='XGBRF_importance',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Visibility_interpolate',\n",
       " 'Item_Weight_interploate',\n",
       " 'Item_Type',\n",
       " 'Outlet_Location_Type',\n",
       " 'Item_Identifier',\n",
       " 'Item_Fat_Content']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['Item_Visibility_interpolate','Item_Weight_interploate',\n",
    "'Item_Type','Outlet_Location_Type','Item_Identifier','Item_Fat_Content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5966037632320667\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRFRegressor\n",
    "\n",
    "xg = XGBRFRegressor(n_estimators=100,random_state=42)\n",
    "scores = cross_val_score(xg1,X.drop(['Item_Visibility_interpolate','Item_Weight_interploate',\n",
    "'Item_Type','Outlet_Location_Type','Item_Identifier','Item_Fat_Content'],axis=1),y,cv=5,scoring='r2')\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = X.drop(columns=['Item_Visibility_interpolate','Item_Weight_interploate',\n",
    "'Item_Type','Outlet_Location_Type','Item_Identifier','Item_Fat_Content'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Outlet_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>249.8092</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.2692</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141.6180</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>182.0950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.8614</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>214.5218</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8519</th>\n",
       "      <td>108.1570</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8520</th>\n",
       "      <td>85.1224</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8521</th>\n",
       "      <td>103.1332</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>75.4670</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8523 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_MRP  Outlet_Identifier  Outlet_Size  Outlet_Type  Outlet_age\n",
       "0     249.8092                9.0          1.0          1.0          25\n",
       "1      48.2692                3.0          1.0          2.0          15\n",
       "2     141.6180                9.0          1.0          1.0          25\n",
       "3     182.0950                0.0          2.0          0.0          26\n",
       "4      53.8614                1.0          0.0          1.0          37\n",
       "...        ...                ...          ...          ...         ...\n",
       "8518  214.5218                1.0          0.0          1.0          37\n",
       "8519  108.1570                7.0          2.0          1.0          22\n",
       "8520   85.1224                6.0          2.0          1.0          20\n",
       "8521  103.1332                3.0          1.0          2.0          15\n",
       "8522   75.4670                8.0          2.0          1.0          27\n",
       "\n",
       "[8523 rows x 5 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRFRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_final = XGBRFRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRFRegressor(base_score=None, booster=None, callbacks=None,\n",
       "               colsample_bylevel=None, colsample_bytree=None, device=None,\n",
       "               early_stopping_rounds=None, enable_categorical=False,\n",
       "               eval_metric=None, feature_types=None, gamma=None,\n",
       "               grow_policy=None, importance_type=None,\n",
       "               interaction_constraints=None, max_bin=None,\n",
       "               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "               max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "               multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "               num_parallel_tree=None, objective=&#x27;reg:squarederror&#x27;,\n",
       "               random_state=None, reg_alpha=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRFRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRFRegressor(base_score=None, booster=None, callbacks=None,\n",
       "               colsample_bylevel=None, colsample_bytree=None, device=None,\n",
       "               early_stopping_rounds=None, enable_categorical=False,\n",
       "               eval_metric=None, feature_types=None, gamma=None,\n",
       "               grow_policy=None, importance_type=None,\n",
       "               interaction_constraints=None, max_bin=None,\n",
       "               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "               max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "               multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "               num_parallel_tree=None, objective=&#x27;reg:squarederror&#x27;,\n",
       "               random_state=None, reg_alpha=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRFRegressor(base_score=None, booster=None, callbacks=None,\n",
       "               colsample_bylevel=None, colsample_bytree=None, device=None,\n",
       "               early_stopping_rounds=None, enable_categorical=False,\n",
       "               eval_metric=None, feature_types=None, gamma=None,\n",
       "               grow_policy=None, importance_type=None,\n",
       "               interaction_constraints=None, max_bin=None,\n",
       "               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "               max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "               multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "               num_parallel_tree=None, objective='reg:squarederror',\n",
       "               random_state=None, reg_alpha=None, ...)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_final.fit(final_data,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(final_data,y,\n",
    "                                                 test_size=0.20,\n",
    "                                                 random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRFRegressor(base_score=None, booster=None, callbacks=None,\n",
       "               colsample_bylevel=None, colsample_bytree=None, device=None,\n",
       "               early_stopping_rounds=None, enable_categorical=False,\n",
       "               eval_metric=None, feature_types=None, gamma=None,\n",
       "               grow_policy=None, importance_type=None,\n",
       "               interaction_constraints=None, max_bin=None,\n",
       "               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "               max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "               multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "               num_parallel_tree=None, objective=&#x27;reg:squarederror&#x27;,\n",
       "               random_state=None, reg_alpha=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRFRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRFRegressor(base_score=None, booster=None, callbacks=None,\n",
       "               colsample_bylevel=None, colsample_bytree=None, device=None,\n",
       "               early_stopping_rounds=None, enable_categorical=False,\n",
       "               eval_metric=None, feature_types=None, gamma=None,\n",
       "               grow_policy=None, importance_type=None,\n",
       "               interaction_constraints=None, max_bin=None,\n",
       "               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "               max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "               multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "               num_parallel_tree=None, objective=&#x27;reg:squarederror&#x27;,\n",
       "               random_state=None, reg_alpha=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRFRegressor(base_score=None, booster=None, callbacks=None,\n",
       "               colsample_bylevel=None, colsample_bytree=None, device=None,\n",
       "               early_stopping_rounds=None, enable_categorical=False,\n",
       "               eval_metric=None, feature_types=None, gamma=None,\n",
       "               grow_policy=None, importance_type=None,\n",
       "               interaction_constraints=None, max_bin=None,\n",
       "               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "               max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "               multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "               num_parallel_tree=None, objective='reg:squarederror',\n",
       "               random_state=None, reg_alpha=None, ...)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_final.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xg_final.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "713.9516489619299"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction on Unseen Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2067.0864\n"
     ]
    }
   ],
   "source": [
    "pred = xg_final.predict(np.array([[141.6180,9.0,1.0,1.0,24]]))[0]\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sales Value is between 1352.66642578125 and 2781.50642578125\n"
     ]
    }
   ],
   "source": [
    "print(f\"Sales Value is between {pred-714.42} and {pred+714.42}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Model Using Joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bigmart_model']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(xg_final,'bigmart_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load('bigmart_model')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2067.0864]\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(np.array([[141.6180,9.0,1.0,1.0,24]]))\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sales Value is between [1352.6665] and [2781.5063]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Sales Value is between {pred-714.42} and {pred+714.42}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dash\n",
    "from dash import html, dcc, Input, Output, State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "app = dash.Dash(__name__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"650\"\n",
       "            src=\"http://127.0.0.1:8050/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1de4e070df0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import dash\n",
    "from dash import html, dcc, Input, Output, State\n",
    "import pandas as pd\n",
    "\n",
    "# Initialize the Dash app\n",
    "app = dash.Dash(__name__)\n",
    "\n",
    "# Define the layout of the app\n",
    "app.layout = html.Div([\n",
    "    html.Div([\n",
    "        html.H1(\"SALES PREDICTOR\", style={'text-align': 'center'}),\n",
    "        \n",
    "        html.Div([\n",
    "            html.Label(\"ITEM_MRP:\"),\n",
    "            dcc.Input(id='ITEM_MRP', type='number', placeholder='Enter item MRP',\n",
    "                      style={'margin': '10px', 'padding': '10px'}),\n",
    "            html.Br(),\n",
    "            html.Label(\"OUTLET_INDENTIFIER:\"),\n",
    "            dcc.Input(id='OUTLET_INDENTIFIER', type='number', placeholder='Enter value 0-9',\n",
    "                      style={'margin': '10px', 'padding': '10px'}),\n",
    "            \n",
    "            html.Br(),\n",
    "            html.Label(\"OUTLET_SIZE:\"),\n",
    "            dcc.Input(id='OUTLET_SIZE', type='number', placeholder='Enter value 0-2',\n",
    "                      style={'margin': '10px', 'padding': '10px'}),\n",
    "            html.Br(),\n",
    "            html.Label(\"OUTLET_TYPE:\"),\n",
    "            dcc.Input(id='OUTLET_TYPE', type='number', placeholder='Enter value 0-3',\n",
    "                      style={'margin': '10px', 'padding': '10px'}),\n",
    "            html.Br(),\n",
    "            html.Label(\"OUTLET_AGE:\"),\n",
    "            dcc.Input(id='OUTLET_AGE', type='number', placeholder='Enter Outlet age',\n",
    "                      style={'margin': '10px', 'padding': '10px'}),\n",
    "            html.Br(),\n",
    "            html.Button('Predict Sales Price', id='predict_button', n_clicks=0,\n",
    "                        style={'margin': '10px', 'padding': '10px', 'background-color': '#007BFF', 'color': 'white'}),\n",
    "        ], style={'text-align': 'center'}),\n",
    "        \n",
    "        html.Div(id='prediction_output', style={'text-align': 'center', 'font-size': '20px', 'margin-top': '20px'})\n",
    "    ], style={'width': '50%', 'margin': '0 auto', 'border': '2px solid #007BFF', 'padding': '20px', 'border-radius': '10px'})\n",
    "])\n",
    "\n",
    "# Define callback to update output\n",
    "@app.callback(\n",
    "    Output('prediction_output', 'children'),\n",
    "    [Input('predict_button', 'n_clicks')],\n",
    "    [State('ITEM_MRP', 'value'), \n",
    "     State('OUTLET_INDENTIFIER', 'value'),\n",
    "     State('OUTLET_SIZE', 'value'),\n",
    "     State('OUTLET_TYPE', 'value'),\n",
    "     State('OUTLET_AGE', 'value')]\n",
    ")\n",
    "def update_output(n_clicks,ITEM_MRP,OUTLET_INDENTIFIER,OUTLET_SIZE,OUTLET_TYPE,OUTLET_AGE):\n",
    "    if n_clicks > 0 and all(v is not None for v in [ITEM_MRP,OUTLET_INDENTIFIER,OUTLET_SIZE,OUTLET_TYPE,OUTLET_AGE]):\n",
    "        # Prepare the feature vector\n",
    "        features = pd.DataFrame([[ITEM_MRP,OUTLET_INDENTIFIER,OUTLET_SIZE,OUTLET_TYPE,OUTLET_AGE]], \n",
    "                                columns=['Item_MRP','Outlet_Identifier','Outlet_Size','Outlet_Type','Outlet_age'])\n",
    "        # Predict\n",
    "        prediction = model.predict(features)[0]\n",
    "        return 'Predicted Sales are between  '+str(prediction-714)+\"  and  \"+str(prediction+714)\n",
    "    elif n_clicks > 0:\n",
    "        return 'Please enter all values to get a prediction'\n",
    "    return ''\n",
    "\n",
    "# Run the app\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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