6. BigMart Sales Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "723ccd2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9959407",
   "metadata": {},
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f18a669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df = pd.read_csv(\"data/proj6/Train.csv\")\n",
    "bigMart_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb94d33",
   "metadata": {},
   "source": [
    "## Inspecting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e240479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   object \n",
      " 1   Item_Weight                7060 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   object \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   object \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   object \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                6113 non-null   object \n",
      " 9   Outlet_Location_Type       8523 non-null   object \n",
      " 10  Outlet_Type                8523 non-null   object \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 799.2+ KB\n"
     ]
    }
   ],
   "source": [
    "bigMart_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ec36c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b9a8f21",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>DRC01</td>\n",
       "      <td>FDN15</td>\n",
       "      <td>FDX07</td>\n",
       "      <td>NCD19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Weight</th>\n",
       "      <td>9.3</td>\n",
       "      <td>5.92</td>\n",
       "      <td>17.5</td>\n",
       "      <td>19.2</td>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Low Fat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Visibility</th>\n",
       "      <td>0.016047</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>0.01676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Type</th>\n",
       "      <td>Dairy</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>Meat</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>Household</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_MRP</th>\n",
       "      <td>249.8092</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>141.618</td>\n",
       "      <td>182.095</td>\n",
       "      <td>53.8614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <td>OUT049</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>OUT013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <td>1999</td>\n",
       "      <td>2009</td>\n",
       "      <td>1999</td>\n",
       "      <td>1998</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Tier 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <td>3735.138</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>2097.27</td>\n",
       "      <td>732.38</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           0                  1  \\\n",
       "Item_Identifier                        FDA15              DRC01   \n",
       "Item_Weight                              9.3               5.92   \n",
       "Item_Fat_Content                     Low Fat            Regular   \n",
       "Item_Visibility                     0.016047           0.019278   \n",
       "Item_Type                              Dairy        Soft Drinks   \n",
       "Item_MRP                            249.8092            48.2692   \n",
       "Outlet_Identifier                     OUT049             OUT018   \n",
       "Outlet_Establishment_Year               1999               2009   \n",
       "Outlet_Size                           Medium             Medium   \n",
       "Outlet_Location_Type                  Tier 1             Tier 3   \n",
       "Outlet_Type                Supermarket Type1  Supermarket Type2   \n",
       "Item_Outlet_Sales                   3735.138           443.4228   \n",
       "\n",
       "                                           2                      3  \\\n",
       "Item_Identifier                        FDN15                  FDX07   \n",
       "Item_Weight                             17.5                   19.2   \n",
       "Item_Fat_Content                     Low Fat                Regular   \n",
       "Item_Visibility                      0.01676                    0.0   \n",
       "Item_Type                               Meat  Fruits and Vegetables   \n",
       "Item_MRP                             141.618                182.095   \n",
       "Outlet_Identifier                     OUT049                 OUT010   \n",
       "Outlet_Establishment_Year               1999                   1998   \n",
       "Outlet_Size                           Medium                    NaN   \n",
       "Outlet_Location_Type                  Tier 1                 Tier 3   \n",
       "Outlet_Type                Supermarket Type1          Grocery Store   \n",
       "Item_Outlet_Sales                    2097.27                 732.38   \n",
       "\n",
       "                                           4  \n",
       "Item_Identifier                        NCD19  \n",
       "Item_Weight                             8.93  \n",
       "Item_Fat_Content                     Low Fat  \n",
       "Item_Visibility                          0.0  \n",
       "Item_Type                          Household  \n",
       "Item_MRP                             53.8614  \n",
       "Outlet_Identifier                     OUT013  \n",
       "Outlet_Establishment_Year               1987  \n",
       "Outlet_Size                             High  \n",
       "Outlet_Location_Type                  Tier 3  \n",
       "Outlet_Type                Supermarket Type1  \n",
       "Item_Outlet_Sales                   994.7052  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c13f8200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FDA15', 'DRC01', 'FDN15', ..., 'NCF55', 'NCW30', 'NCW05'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df[\"Item_Identifier\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a21d7668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5089\n",
       "Regular    2889\n",
       "LF          316\n",
       "reg         117\n",
       "low fat     112\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df[\"Item_Fat_Content\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fbd119c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medium    2793\n",
       "Small     2388\n",
       "High       932\n",
       "Name: Outlet_Size, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df[\"Outlet_Size\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "373f8bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Supermarket Type1    5577\n",
       "Grocery Store        1083\n",
       "Supermarket Type3     935\n",
       "Supermarket Type2     928\n",
       "Name: Outlet_Type, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df[\"Outlet_Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1a8846",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d12ba458",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8bc1acb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Series name: Item_Weight\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "7060 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 66.7 KB\n"
     ]
    }
   ],
   "source": [
    "bigMart_df[\"Item_Weight\"].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8e22a19c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Series name: Outlet_Size\n",
      "Non-Null Count  Dtype \n",
      "--------------  ----- \n",
      "6113 non-null   object\n",
      "dtypes: object(1)\n",
      "memory usage: 66.7+ KB\n"
     ]
    }
   ],
   "source": [
    "bigMart_df[\"Outlet_Size\"].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b31fea55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Medium\n",
       "1    Medium\n",
       "2    Medium\n",
       "3       NaN\n",
       "4      High\n",
       "5    Medium\n",
       "6      High\n",
       "7    Medium\n",
       "8       NaN\n",
       "9       NaN\n",
       "Name: Outlet_Size, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df[\"Outlet_Size\"][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "395eda7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "bigMart_df[\"Item_Weight\"] = bigMart_df[\"Item_Weight\"].fillna(bigMart_df[\"Item_Weight\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6de3cc62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df[\"Item_Weight\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "53aeee9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Medium'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df[\"Outlet_Size\"].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d68edcb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "bigMart_df[\"Outlet_Size\"] = bigMart_df[\"Outlet_Size\"].fillna(bigMart_df[\"Outlet_Size\"].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c755123c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bigMart_df[\"Item_Fat_Content\"].replace(['LF', 'reg', 'low fat'], ['Low Fat', 'Regular', 'Low Fat'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "df26bf03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5517\n",
       "Regular    3006\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df[\"Item_Fat_Content\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9590cd5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              0\n",
       "Item_Weight                  0\n",
       "Item_Fat_Content             0\n",
       "Item_Visibility              0\n",
       "Item_Type                    0\n",
       "Item_MRP                     0\n",
       "Outlet_Identifier            0\n",
       "Outlet_Establishment_Year    0\n",
       "Outlet_Size                  0\n",
       "Outlet_Location_Type         0\n",
       "Outlet_Type                  0\n",
       "Item_Outlet_Sales            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587e5997",
   "metadata": {},
   "source": [
    "## Visualizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bc0f38f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/djoe/Documents/myprogramming/hackpython/dataScience/udemy_proj/env/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.4\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7dc23753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Item_Fat_Content', ylabel='count'>"
      ]
     },
     "execution_count": 28,
     "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": [
    "sns.countplot(data=bigMart_df, x=\"Item_Fat_Content\", palette=\"mako\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cfbf41f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Item_Type', ylabel='count'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 10))\n",
    "sns.countplot(data=bigMart_df, x=\"Item_Type\", palette=\"flag\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "26cf18b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Item_Outlet_Sales', ylabel='Item_Type'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 9))\n",
    "sns.barplot(data=bigMart_df, y=\"Item_Type\", x=\"Item_Outlet_Sales\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eadbf1b6",
   "metadata": {},
   "source": [
    "## Converting Categorical Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b5e4b0c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6e706e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for stuff in bigMart_df.keys():\n",
    "    if types.is_object_dtype(bigMart_df[stuff]) == True:\n",
    "        bigMart_df[stuff] = le.fit_transform(bigMart_df[stuff])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f505256d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>156</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>5.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>662</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1121</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
       "0              156         9.30                 0         0.016047          4   \n",
       "1                8         5.92                 1         0.019278         14   \n",
       "2              662        17.50                 0         0.016760         10   \n",
       "3             1121        19.20                 1         0.000000          6   \n",
       "4             1297         8.93                 0         0.000000          9   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0  249.8092                  9                       1999            1   \n",
       "1   48.2692                  3                       2009            1   \n",
       "2  141.6180                  9                       1999            1   \n",
       "3  182.0950                  0                       1998            1   \n",
       "4   53.8614                  1                       1987            0   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  \n",
       "0                     0            1          3735.1380  \n",
       "1                     2            2           443.4228  \n",
       "2                     0            1          2097.2700  \n",
       "3                     2            0           732.3800  \n",
       "4                     2            1           994.7052  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "81d494b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "bigMart_df_test = pd.read_csv(\"data/proj6/Test.csv\")\n",
    "bigMart_df_test[\"Item_Weight\"] = bigMart_df_test[\"Item_Weight\"].fillna(bigMart_df_test[\"Item_Weight\"].mean())\n",
    "bigMart_df_test[\"Outlet_Size\"] = bigMart_df_test[\"Outlet_Size\"].fillna(bigMart_df_test[\"Outlet_Size\"].mode()[0])\n",
    "bigMart_df_test[\"Item_Fat_Content\"].replace(['LF', 'reg', 'low fat'], ['Low Fat', 'Regular', 'Low Fat'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2f897c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              0\n",
       "Item_Weight                  0\n",
       "Item_Fat_Content             0\n",
       "Item_Visibility              0\n",
       "Item_Type                    0\n",
       "Item_MRP                     0\n",
       "Outlet_Identifier            0\n",
       "Outlet_Establishment_Year    0\n",
       "Outlet_Size                  0\n",
       "Outlet_Location_Type         0\n",
       "Outlet_Type                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df_test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0b2cca2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for stuff in bigMart_df_test.keys():\n",
    "    if types.is_object_dtype(bigMart_df_test[stuff]) == True:\n",
    "        bigMart_df_test[stuff] = le.fit_transform(bigMart_df_test[stuff])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3ad23e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1103</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>13</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1067</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>4</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1406</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>11</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>809</td>\n",
       "      <td>7.315000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>13</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1184</td>\n",
       "      <td>12.695633</td>\n",
       "      <td>1</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>4</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>5</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>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             1103    20.750000                 0         0.007565         13   \n",
       "1             1067     8.300000                 1         0.038428          4   \n",
       "2             1406    14.600000                 0         0.099575         11   \n",
       "3              809     7.315000                 0         0.015388         13   \n",
       "4             1184    12.695633                 1         0.118599          4   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0  107.8622                  9                       1999            1   \n",
       "1   87.3198                  2                       2007            1   \n",
       "2  241.7538                  0                       1998            1   \n",
       "3  155.0340                  2                       2007            1   \n",
       "4  234.2300                  5                       1985            1   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  \n",
       "0                     0            1  \n",
       "1                     1            1  \n",
       "2                     2            0  \n",
       "3                     1            1  \n",
       "4                     2            3  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMart_df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3fe951d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Supermarket Type2'], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.inverse_transform([[2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692f6b57",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "dfffad59",
   "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>RandomForestRegressor()</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\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "X = bigMart_df.drop(\"Item_Outlet_Sales\", axis=1)\n",
    "y = bigMart_df[\"Item_Outlet_Sales\"]\n",
    "\n",
    "rfr = RandomForestRegressor()\n",
    "rfr.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e163f193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1638.83341 , 1136.766946,  749.504376, ..., 1881.770514,\n",
       "       4827.28303 , 1491.052442])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = bigMart_df_test\n",
    "rfr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "35fdd3e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5587090910507939"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "rfr.fit(X_train, y_train)\n",
    "\n",
    "rfr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "847dd80a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "name": "python",
   "nbconvert_exporter": "python",
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