Prediction_Of_BigMarket_Sales2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "282a902b",
   "metadata": {},
   "source": [
    "# Prediction of BigMarket Sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "992cabf1",
   "metadata": {},
   "source": [
    "Sales Prediction for Big Mart Outlets\n",
    "The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet.\n",
    "\n",
    "Using this model, BigMart will try to understand the properties of products and outlets which play a key role in increasing sales."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a187b2",
   "metadata": {},
   "source": [
    "# Data Dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a747470",
   "metadata": {},
   "source": [
    "We have train (8523) and test (5681) data set, train data set has both input and output variable(s). You need to predict the sales for test data set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778bec45",
   "metadata": {},
   "source": [
    "# Metadata"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6734983f",
   "metadata": {},
   "source": [
    "Item_Identifier\t:- Unique product ID\n",
    "Item_Weight:- \tWeight of product\n",
    "Item_Fat_Content:-\tWhether the product is low fat or not\n",
    "Item_Visibility\t:- The % of total display area of all products in a store allocated to the particular product\n",
    "Item_Type:- \tThe category to which the product belongs\n",
    "Item_MRP\t:- Maximum Retail Price (list price) of the product\n",
    "Outlet_Identifier\t:- Unique store ID\n",
    "Outlet_Establishment_Year\t:- The year in which store was established\n",
    "Outlet_Size\t:- The size of the store in terms of ground area covered\n",
    "Outlet_Location_Type\t:- The type of city in which the store is located\n",
    "Outlet_Type\t:- Whether the outlet is just a grocery store or some sort of supermarket\n",
    "Item_Outlet_Sales\t:- Sales of the product in the particular store. This is the outcome variable to be predicted.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc28644",
   "metadata": {},
   "source": [
    "# Import The Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "2c86269e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077da5c2",
   "metadata": {},
   "source": [
    "# Lets Import the Data set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7c410dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"F:\\Predict BigMarket Sales\\Train.csv\")\n",
    "test_data  = pd.read_csv(\"F:\\Predict BigMarket Sales\\Test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "30a00066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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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",
       "    </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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4595e0c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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_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>FDW58</td>\n",
       "      <td>20.750</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDW14</td>\n",
       "      <td>8.300</td>\n",
       "      <td>reg</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN55</td>\n",
       "      <td>14.600</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>Others</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDQ58</td>\n",
       "      <td>7.315</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
       "0           FDW58       20.750          Low Fat         0.007565  Snack Foods   \n",
       "1           FDW14        8.300              reg         0.038428        Dairy   \n",
       "2           NCN55       14.600          Low Fat         0.099575       Others   \n",
       "3           FDQ58        7.315          Low Fat         0.015388  Snack Foods   \n",
       "4           FDY38          NaN          Regular         0.118599        Dairy   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  107.8622            OUT049                       1999      Medium   \n",
       "1   87.3198            OUT017                       2007         NaN   \n",
       "2  241.7538            OUT010                       1998         NaN   \n",
       "3  155.0340            OUT017                       2007         NaN   \n",
       "4  234.2300            OUT027                       1985      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  \n",
       "0               Tier 1  Supermarket Type1  \n",
       "1               Tier 2  Supermarket Type1  \n",
       "2               Tier 3      Grocery Store  \n",
       "3               Tier 2  Supermarket Type1  \n",
       "4               Tier 3  Supermarket Type3  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9d8fc60c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rows, column: (8523, 12)\n",
      "rows, column: (5681, 11)\n"
     ]
    }
   ],
   "source": [
    "print(\"rows, column:\",  train_data.shape)\n",
    "print(\"rows, column:\",  test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "82c3801b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier               object\n",
       "Item_Weight                  float64\n",
       "Item_Fat_Content              object\n",
       "Item_Visibility              float64\n",
       "Item_Type                     object\n",
       "Item_MRP                     float64\n",
       "Outlet_Identifier             object\n",
       "Outlet_Establishment_Year      int64\n",
       "Outlet_Size                   object\n",
       "Outlet_Location_Type          object\n",
       "Outlet_Type                   object\n",
       "Item_Outlet_Sales            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "881d38e6",
   "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": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "6bac0dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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_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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6808aa",
   "metadata": {},
   "source": [
    "# Handling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "94f69bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Size                  2410\n",
       "Item_Weight                  1463\n",
       "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_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a7761e2",
   "metadata": {},
   "source": [
    "# Handling missing values in Outlet_size column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a24c4753",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by Outlet_Type and calculate mode Outlet_Size for each category\n",
    "category_mode = train_data.groupby('Outlet_Type')['Outlet_Size'].apply(lambda x: x.mode().iloc[0])\n",
    "\n",
    "\n",
    "# Function to impute missing Outlet_Size based on category\n",
    "def impute_outlet_size(row):\n",
    "    if pd.isnull(row['Outlet_Size']):\n",
    "        return category_mode.get(row['Outlet_Type'], None)\n",
    "    return row['Outlet_Size']\n",
    "\n",
    "# Apply the imputation function to the DataFrame\n",
    "train_data['Outlet_Size'] = train_data.apply(impute_outlet_size, axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd1b322",
   "metadata": {},
   "source": [
    " Now only item_weight column has null values for that I will check the dependency of otem_weight column and Item_Outlet_Sales by correlation of their is no corrleation I will simply drop that whole column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "190f1ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_5968\\2987843140.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  correlation_matrix = train_data.corr()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "correlation_matrix = train_data.corr()\n",
    "\n",
    "# Create a heatmap using seaborn\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0f8778",
   "metadata": {},
   "source": [
    "As we can see that their is weak correlation between Item_weight and Item_Outlet_Sales. we can drop that Item_weight Column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "2ef43f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 'Item_Weight ' column\n",
    "train_data.drop('Item_Weight', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038a0f74",
   "metadata": {},
   "source": [
    "Now our train dataset does not have any null values. We will now apply same procedure for the test dataset to remove null values\n",
    "from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c21f884a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Size                  1606\n",
       "Item_Weight                   976\n",
       "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_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "5ecdfd1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by Outlet_Type and calculate mode Outlet_Size for each category\n",
    "category_mode = test_data.groupby('Outlet_Type')['Outlet_Size'].apply(lambda x: x.mode().iloc[0])\n",
    "\n",
    "\n",
    "# Function to impute missing Outlet_Size based on category\n",
    "def impute_outlet_size(row):\n",
    "    if pd.isnull(row['Outlet_Size']):\n",
    "        return category_mode.get(row['Outlet_Type'], None)\n",
    "    return row['Outlet_Size']\n",
    "\n",
    "# Apply the imputation function to the DataFrame\n",
    "test_data['Outlet_Size'] = test_data.apply(impute_outlet_size, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "63a147ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 'Item_Weight ' column\n",
    "test_data.drop('Item_Weight', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3549c4",
   "metadata": {},
   "source": [
    "Now both the dataset 'train_data' and 'test_data' does not have missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c27eb954",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rows, column: (8523, 11)\n",
      "rows, column: (5681, 10)\n"
     ]
    }
   ],
   "source": [
    "print(\"rows, column:\",  train_data.shape)\n",
    "print(\"rows, column:\",  test_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "683d8e2c",
   "metadata": {},
   "source": [
    "# Now we will do some Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "a9a13b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box plot using seaborn\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(data=train_data, x='Outlet_Identifier', y='Item_Outlet_Sales')\n",
    "plt.title('Outlet_Identifier by Item_Outlet_Sales')\n",
    "plt.xlabel('Outlet_Identifier')\n",
    "plt.ylabel('Item_Outlet_Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7175a0b",
   "metadata": {},
   "source": [
    "From the above boxplots we can see that Outlet_10 and Outlet_19 has less contributed to the Total sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97400ef0",
   "metadata": {},
   "source": [
    "# Handling the Outliers on train as well as test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "803f2e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Visibility', 'Item_MRP', 'Item_Outlet_Sales']"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_col = list(train_data.select_dtypes(['float64']).columns)\n",
    "float_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2145cbe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in train_data.columns:\n",
    "    if train_data[column].dtype == 'object':\n",
    "        try:\n",
    "            train_data[column] = train_data[column].astype('int64')\n",
    "        except ValueError:\n",
    "            pass "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "57e6e27f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Outlet_Establishment_Year']"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_col = list(train_data.select_dtypes(['int64']).columns)\n",
    "int_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "9bb40487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Identifier',\n",
       " 'Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Identifier',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_col = list(train_data.select_dtypes(['object']).columns)\n",
    "obj_col"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c99ed40",
   "metadata": {},
   "source": [
    "# Checking Outliers in float columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "1d70e087",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Visibility has ===>   144 outliers\n",
      "Item_MRP has ===>   0 outliers\n",
      "Item_Outlet_Sales has ===>   186 outliers\n"
     ]
    }
   ],
   "source": [
    "for i in float_col:\n",
    "    Q3, Q1 = np.percentile(train_data[i], [75 ,25])\n",
    "    iqr= Q3 - Q1\n",
    "    low = Q1 - (1.5*iqr)\n",
    "    high = Q3 + (1.5*iqr)\n",
    "    x=train_data[i][train_data[i]>high]\n",
    "    y=train_data[i][train_data[i]<low]\n",
    "    print(i,'has ===>  ',x.shape[0]+y.shape[0],'outliers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "fd0d41d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.07440424500000001 0.195979015\n",
      "0.067595815\n",
      "-43.8993 323.3695\n",
      "91.8172\n",
      "-2566.3261 6501.8699\n",
      "2267.049\n"
     ]
    }
   ],
   "source": [
    "for i in float_col:\n",
    "    Q3, Q1 = np.percentile(train_data[i], [75 ,25])\n",
    "    iqr= Q3 - Q1\n",
    "    low = Q1 - (1.5*iqr)\n",
    "    high = Q3 + (1.5*iqr)\n",
    "    print(low,high)\n",
    "    print(iqr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "7693fc18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0945852925"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Item_Visibility'].quantile(.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "7a95c744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3101.2964"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Item_Outlet_Sales'].quantile(.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "485c9d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_1=['Item_Visibility','Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "ebf50e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "p0_ = []\n",
    "p100_ = []\n",
    "iqr_ = []\n",
    "high_ = []\n",
    "low_ = []\n",
    "\n",
    "for i in outlier_1:\n",
    "    p0 =train_data[i].min()\n",
    "    p100=train_data[i].max()\n",
    "    \n",
    "    p0_.append(p0)\n",
    "    p100_.append(p100)\n",
    "\n",
    "    q1=train_data[i].quantile(0.25)\n",
    "    q2=train_data[i].quantile(0.5)\n",
    "    q3=train_data[i].quantile(0.75)\n",
    "    \n",
    "    iqr=q3-q1\n",
    "    iqr_.append(iqr)\n",
    "    \n",
    "    low: int = q1 - (1.5 * iqr)\n",
    "    high: int = q3 + (1.5 * iqr)\n",
    "    \n",
    "    low_.append(low)\n",
    "    high_.append(high)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e7f54f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "count = 0\n",
    "\n",
    "for i in outlier_1:\n",
    "    train_data[i] = train_data[i].clip(upper = high_[count], lower = low_[count])\n",
    "    count+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "a370b3ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Visibility has ===>   0 outliers\n",
      "Item_Outlet_Sales has ===>   0 outliers\n"
     ]
    }
   ],
   "source": [
    "for i in outlier_1:\n",
    "    Q3, Q1 = np.percentile(train_data[i], [75 ,25])\n",
    "    iqr= Q3 - Q1\n",
    "    low = Q1 - (1.5*iqr)\n",
    "    high = Q3 + (1.5*iqr)\n",
    "    x=train_data[i][train_data[i]>high]\n",
    "    y=train_data[i][train_data[i]<low]\n",
    "    print(i,'has ===>  ',x.shape[0]+y.shape[0],'outliers')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cadd59a",
   "metadata": {},
   "source": [
    "So we Have succefully removed ouliers from float datatype columns now we do same for object and int64 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "ed827b9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlet_Establishment_Year has ===>   0 outliers\n"
     ]
    }
   ],
   "source": [
    "for i in int_col:\n",
    "    Q3, Q1 = np.percentile(train_data[i], [75 ,25])\n",
    "    iqr= Q3 - Q1\n",
    "    low = Q1 - (1.5*iqr)\n",
    "    high = Q3 + (1.5*iqr)\n",
    "    x=train_data[i][train_data[i]>high]\n",
    "    y=train_data[i][train_data[i]<low]\n",
    "    print(i,'has ===>  ',x.shape[0]+y.shape[0],'outliers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "56574f22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8523.000000\n",
       "mean        0.065307\n",
       "std         0.048841\n",
       "min         0.000000\n",
       "25%         0.026989\n",
       "50%         0.053931\n",
       "75%         0.094585\n",
       "max         0.195979\n",
       "Name: Item_Visibility, dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Item_Visibility'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99623563",
   "metadata": {},
   "source": [
    "As we can see  that the Fat_content name are occured as 'LF and Reg' which we need to replace by LF:- Low Fat and Reg:- Regular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "42f4982f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Categories:\n",
      "Low Fat    5089\n",
      "Regular    2889\n",
      "LF          316\n",
      "reg         117\n",
      "low fat     112\n",
      "Name: Item_Fat_Content, dtype: int64\n",
      "\n",
      "Modified Categories:\n",
      "Low Fat    5517\n",
      "Regular    3006\n",
      "Name: Item_Fat_Content, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Change categories of low fat:\n",
    "print('Original Categories:')\n",
    "print(train_data['Item_Fat_Content'].value_counts())\n",
    "\n",
    "train_data['Item_Fat_Content'] = train_data['Item_Fat_Content'].replace({'LF':'Low Fat',\n",
    "                                                             'reg':'Regular',\n",
    "                                                             'low fat':'Low Fat'})\n",
    "print('\\nModified Categories:')\n",
    "print(train_data['Item_Fat_Content'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "8b5a4277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Count the occurrences of each fat content\n",
    "fat_content_counts = train_data['Item_Fat_Content'].value_counts()\n",
    "\n",
    "# Create a pie chart\n",
    "plt.figure(figsize=(3, 4))  # Set the figure size\n",
    "plt.pie(fat_content_counts, labels=fat_content_counts.index, autopct='%1.1f%%', startangle=140)\n",
    "\n",
    "# Add a title\n",
    "plt.title('Distribution of Items by Fat Content')\n",
    "\n",
    "# Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.axis('equal')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2acf21",
   "metadata": {},
   "source": [
    "Item_by_fatContent is diveide in  t parts Low fat having 64.7% and Regular having 35.3%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "259ffaee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box plot\n",
    "plt.figure(figsize=(10, 3))\n",
    "sns.barplot(data=train_data, x='Outlet_Size', y='Item_Outlet_Sales')\n",
    "plt.xlabel('Outlet_Size')\n",
    "plt.ylabel('Target Item_Outlet_Sales')\n",
    "plt.title('BarGraph of Oulet_size vs item_Outlet_Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "980a0429",
   "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_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.065307</td>\n",
       "      <td>140.992782</td>\n",
       "      <td>1997.831867</td>\n",
       "      <td>2156.313016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.048841</td>\n",
       "      <td>62.275067</td>\n",
       "      <td>8.371760</td>\n",
       "      <td>1624.863069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>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>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>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>0.195979</td>\n",
       "      <td>266.888400</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>6501.869900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
       "count      8523.000000  8523.000000                8523.000000   \n",
       "mean          0.065307   140.992782                1997.831867   \n",
       "std           0.048841    62.275067                   8.371760   \n",
       "min           0.000000    31.290000                1985.000000   \n",
       "25%           0.026989    93.826500                1987.000000   \n",
       "50%           0.053931   143.012800                1999.000000   \n",
       "75%           0.094585   185.643700                2004.000000   \n",
       "max           0.195979   266.888400                2009.000000   \n",
       "\n",
       "       Item_Outlet_Sales  \n",
       "count        8523.000000  \n",
       "mean         2156.313016  \n",
       "std          1624.863069  \n",
       "min            33.290000  \n",
       "25%           834.247400  \n",
       "50%          1794.331000  \n",
       "75%          3101.296400  \n",
       "max          6501.869900  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c78f30f",
   "metadata": {},
   "source": [
    "As we can see the min value of the Item_visibility is zero which is not possible or\n",
    "due to some error so we need to impute this value by mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f5381897",
   "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_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.069603</td>\n",
       "      <td>140.992782</td>\n",
       "      <td>1997.831867</td>\n",
       "      <td>2156.313016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.045879</td>\n",
       "      <td>62.275067</td>\n",
       "      <td>8.371760</td>\n",
       "      <td>1624.863069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.003575</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>0.033085</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>0.062517</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>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>0.195979</td>\n",
       "      <td>266.888400</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>6501.869900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
       "count      8523.000000  8523.000000                8523.000000   \n",
       "mean          0.069603   140.992782                1997.831867   \n",
       "std           0.045879    62.275067                   8.371760   \n",
       "min           0.003575    31.290000                1985.000000   \n",
       "25%           0.033085    93.826500                1987.000000   \n",
       "50%           0.062517   143.012800                1999.000000   \n",
       "75%           0.094585   185.643700                2004.000000   \n",
       "max           0.195979   266.888400                2009.000000   \n",
       "\n",
       "       Item_Outlet_Sales  \n",
       "count        8523.000000  \n",
       "mean         2156.313016  \n",
       "std          1624.863069  \n",
       "min            33.290000  \n",
       "25%           834.247400  \n",
       "50%          1794.331000  \n",
       "75%          3101.296400  \n",
       "max          6501.869900  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean excluding zero values\n",
    "mean_value = train_data[train_data['Item_Visibility'] != 0]['Item_Visibility'].mean()\n",
    "\n",
    "# Replace zero values with the mean\n",
    "train_data['Item_Visibility'] = train_data['Item_Visibility'].replace(0, mean_value)\n",
    "\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e467354",
   "metadata": {},
   "source": [
    "we will do this also on test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "63cab968",
   "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_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5681.000000</td>\n",
       "      <td>5681.000000</td>\n",
       "      <td>5681.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.070036</td>\n",
       "      <td>141.023273</td>\n",
       "      <td>1997.828903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.048383</td>\n",
       "      <td>61.809091</td>\n",
       "      <td>8.372256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.003591</td>\n",
       "      <td>31.990000</td>\n",
       "      <td>1985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.033208</td>\n",
       "      <td>94.412000</td>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.062137</td>\n",
       "      <td>141.415400</td>\n",
       "      <td>1999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.093463</td>\n",
       "      <td>186.026600</td>\n",
       "      <td>2004.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.323637</td>\n",
       "      <td>266.588400</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Visibility     Item_MRP  Outlet_Establishment_Year\n",
       "count      5681.000000  5681.000000                5681.000000\n",
       "mean          0.070036   141.023273                1997.828903\n",
       "std           0.048383    61.809091                   8.372256\n",
       "min           0.003591    31.990000                1985.000000\n",
       "25%           0.033208    94.412000                1987.000000\n",
       "50%           0.062137   141.415400                1999.000000\n",
       "75%           0.093463   186.026600                2004.000000\n",
       "max           0.323637   266.588400                2009.000000"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean excluding zero values\n",
    "mean_value = test_data[test_data['Item_Visibility'] != 0]['Item_Visibility'].mean()\n",
    "\n",
    "# Replace zero values with the mean\n",
    "test_data['Item_Visibility'] = test_data['Item_Visibility'].replace(0, mean_value)\n",
    "\n",
    "test_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "473c049f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group data by 'Item_Type' and calculate the mean of 'Item_Outlet_Sales'\n",
    "item_type_sales_mean = train_data.groupby('Item_Type')['Item_Outlet_Sales'].mean()\n",
    "\n",
    "# Create a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "item_type_sales_mean.plot(kind='bar', color='green')\n",
    "plt.xlabel('Item_Type')\n",
    "plt.ylabel('Mean Item Outlet Sales')\n",
    "plt.title('Mean Item Outlet Sales by Item_Type')\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "4af01717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(train_data['Item_Type'], train_data['Item_Outlet_Sales'], color='blue')\n",
    "plt.xlabel(' Item_Type')\n",
    "plt.ylabel('Item Outlet Sales')\n",
    "plt.title('Scatter Plot of Item Outlet Sales vs Item_Type')\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054c64ce",
   "metadata": {},
   "source": [
    "Among all the Item_Type 'Fruits and Vegetables' and 'Household' items are being most sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5a70cfda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical columns: ['Item_Identifier', 'Item_Fat_Content', 'Item_Type', 'Outlet_Identifier', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type']\n"
     ]
    }
   ],
   "source": [
    "categorical_columns = train_data.select_dtypes(include=['object', 'category']).columns.tolist()\n",
    "\n",
    "print(\"Categorical columns:\", categorical_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c69d27da",
   "metadata": {},
   "source": [
    "# Label Encoding for categorical variables on train data and then test data as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "81a1f1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "# Initialize LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# Encode categorical variables\n",
    "for column in train_data.select_dtypes(include=['object', 'category']).columns:\n",
    "    train_data[column] = label_encoder.fit_transform(train_data[column])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "a68f5e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "# Initialize LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# Encode categorical variables\n",
    "for column in test_data.select_dtypes(include=['object', 'category']).columns:\n",
    "    test_data[column] = label_encoder.fit_transform(test_data[column])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "5dc4aa41",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>156</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>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>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>1</td>\n",
       "      <td>0.069603</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069603</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_Fat_Content  Item_Visibility  Item_Type  Item_MRP  \\\n",
       "0              156                 0         0.016047          4  249.8092   \n",
       "1                8                 1         0.019278         14   48.2692   \n",
       "2              662                 0         0.016760         10  141.6180   \n",
       "3             1121                 1         0.069603          6  182.0950   \n",
       "4             1297                 0         0.069603          9   53.8614   \n",
       "\n",
       "   Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0                  9                       1999            1   \n",
       "1                  3                       2009            1   \n",
       "2                  9                       1999            1   \n",
       "3                  0                       1998            2   \n",
       "4                  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": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "b3aa6185",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1103</td>\n",
       "      <td>1</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>4</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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1406</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>809</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1184</td>\n",
       "      <td>2</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_Fat_Content  Item_Visibility  Item_Type  Item_MRP  \\\n",
       "0             1103                 1         0.007565         13  107.8622   \n",
       "1             1067                 4         0.038428          4   87.3198   \n",
       "2             1406                 1         0.099575         11  241.7538   \n",
       "3              809                 1         0.015388         13  155.0340   \n",
       "4             1184                 2         0.118599          4  234.2300   \n",
       "\n",
       "   Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0                  9                       1999            1   \n",
       "1                  2                       2007            2   \n",
       "2                  0                       1998            2   \n",
       "3                  2                       2007            2   \n",
       "4                  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": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "3d91306a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "correlation_matrix = train_data.corr()\n",
    "\n",
    "# Create a heatmap using seaborn\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5cbb4409",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_MRP                     0.574554\n",
      "Outlet_Type                  0.399640\n",
      "Outlet_Identifier            0.169321\n",
      "Outlet_Location_Type         0.083727\n",
      "Item_Fat_Content             0.019675\n",
      "Item_Type                    0.017388\n",
      "Outlet_Establishment_Year   -0.037133\n",
      "Item_Visibility             -0.125984\n",
      "Outlet_Size                 -0.164868\n",
      "Name: Item_Outlet_Sales, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "numerical_corr = train_data.iloc[:, 1:].select_dtypes(exclude=['object']).corr()\n",
    "print (numerical_corr['Item_Outlet_Sales'].sort_values(axis=0,ascending=False).iloc[1:11])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43cb57f",
   "metadata": {},
   "source": [
    "# Split the data into all the independent variables as x and dependent variable as y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "20b5be44",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_data[['Item_MRP','Outlet_Type','Outlet_Identifier','Item_Visibility','Outlet_Size']].values\n",
    "y_train = train_data.iloc[ : , -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "d3dc4c48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.50e+02 1.00e+00 9.00e+00 1.60e-02 1.00e+00]\n",
      " [4.83e+01 2.00e+00 3.00e+00 1.93e-02 1.00e+00]\n",
      " [1.42e+02 1.00e+00 9.00e+00 1.68e-02 1.00e+00]\n",
      " ...\n",
      " [8.51e+01 1.00e+00 6.00e+00 3.52e-02 2.00e+00]\n",
      " [1.03e+02 2.00e+00 3.00e+00 1.45e-01 1.00e+00]\n",
      " [7.55e+01 1.00e+00 8.00e+00 4.49e-02 2.00e+00]]\n"
     ]
    }
   ],
   "source": [
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "d9133efe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3735.14  443.42 2097.27 ... 1193.11 1845.6   765.67]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537ee7b7",
   "metadata": {},
   "source": [
    "# Split the training dataset again for prediction and model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "95237b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "x1_train, x1_test, y1_train, y1_test = train_test_split (x_train, y_train, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057584a0",
   "metadata": {},
   "source": [
    "# Now we perform some regression models "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce726fd",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "45f7e468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 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-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 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-18 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-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 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-18 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-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 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-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 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-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 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-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(x1_train, y1_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43509e30",
   "metadata": {},
   "source": [
    "Predicting the test Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "9e918575",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 571.32 1859.2  3034.44 ... 2362.02 2187.98 2038.18]\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test set\n",
    "y1_pred = regressor.predict(x1_test)\n",
    "np.set_printoptions(precision=2)\n",
    "print(y1_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e044c72",
   "metadata": {},
   "source": [
    "# Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "8579b3fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the features into polynomial features\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "degree = 3  # Change this to the degree of polynomial you want\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "x1_train_poly = poly.fit_transform(x1_train)\n",
    "x1_test_poly = poly.transform(x1_test)\n",
    "\n",
    "# Fit a linear regression model to the polynomial features\n",
    "model = LinearRegression()\n",
    "model.fit(x1_train_poly, y1_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y1_pred_poly = model.predict(x1_test_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "4740cac4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 535.39 1213.48 3125.46 ... 2760.21 1497.73 2461.61]\n"
     ]
    }
   ],
   "source": [
    "print(y1_pred_poly)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63744183",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "b2213fd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</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-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor_d = DecisionTreeRegressor()\n",
    "regressor_d.fit(x1_train ,y1_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "548e6588",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 493.36 1406.17 3870.96 ... 2137.88  717.73  288.96]\n"
     ]
    }
   ],
   "source": [
    "y1_pred_decision = regressor_d.predict(x1_test)\n",
    "np.set_printoptions(precision=2)\n",
    "print(y1_pred_decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f7dff33",
   "metadata": {},
   "source": [
    "# Random Forest (hyperparameter tunning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "f25d5800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 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-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 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-17 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-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 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-17 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-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 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-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 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-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 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-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 150]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 150]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid={'max_depth': [None, 10, 20],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'n_estimators': [50, 100, 150]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor_R = RandomForestRegressor()\n",
    "# Define a grid of hyperparameters to search\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 150],\n",
    "    'max_depth': [None, 10, 20],\n",
    "    'min_samples_split': [2, 5, 10]\n",
    "}\n",
    "\n",
    "# Initialize GridSearchCV\n",
    "grid_search = GridSearchCV(regressor_R, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
    "\n",
    "grid_search.fit( x1_train, y1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "cacaa2b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the best hyperparameters and the corresponding model\n",
    "best_params = grid_search.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "07c5781a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "RMSE on test set: 1048.7087753880971\n"
     ]
    }
   ],
   "source": [
    "print(\"Best hyperparameters:\", best_params)\n",
    "print(\"RMSE on test set:\", rmse_ran)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47cdd008",
   "metadata": {},
   "source": [
    "# Show the predictions of every model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "54b534a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_df = pd.DataFrame({\n",
    "    'MultipleLinearRegression': y1_pred.flatten(),\n",
    "    'PolynomialFeatures' :y1_pred_poly.flatten(),\n",
    "    'DecisionTree': y1_pred_decision.flatten(),\n",
    "    'RandomForest': y1_pred_random_forest1.flatten()\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "82f26f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      MultipleLinearRegression  PolynomialFeatures  DecisionTree  RandomForest\n",
      "0                   571.319086          535.386523      493.3578    575.283406\n",
      "1                  1859.198698         1213.481591     1406.1696   1299.411698\n",
      "2                  3034.443837         3125.461356     3870.9612   3368.059336\n",
      "3                  1374.462433         1467.668443     1298.3100   1402.577010\n",
      "4                   492.640661          601.272490      639.1680    717.124234\n",
      "...                        ...                 ...           ...           ...\n",
      "1700               3032.644593         3082.872761     3874.9560   2956.279352\n",
      "1701               4389.444266         3448.506752     4857.6768   3622.886954\n",
      "1702               2362.019011         2760.210445     2137.8838   2645.187950\n",
      "1703               2187.983610         1497.733388      717.7324   1363.200365\n",
      "1704               2038.183504         2461.605251      288.9572   2626.264363\n",
      "\n",
      "[1705 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(predictions_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8decea03",
   "metadata": {},
   "source": [
    "# Calculate R-sqaured value for each regression model for evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "4d03d7db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared for Multiple Linear Regression: 0.5069049877481504\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_multiple = r2_score( y1_test , y1_pred)\n",
    "print(\"R-squared for Multiple Linear Regression:\",r2_multiple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "8f1ec4ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared Polynomial Regression: 0.6119925358837941\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_poly = r2_score( y1_test , y1_pred_poly)\n",
    "print(\"R-squared Polynomial Regression:\",r2_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "e1dfd929",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared Decision Tree Regession: 0.2353424954941412\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_decision = r2_score( y1_test , y1_pred_decision)\n",
    "print(\"R-squared Decision Tree Regession:\",r2_decision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "02772a6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared Random Forest Regression: 0.5964187657051223\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_random_forest2 = r2_score( y1_test , y1_pred_random_forest1)\n",
    "print(\"R-squared Random Forest Regression:\",r2_random_forest2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb35f90",
   "metadata": {},
   "source": [
    "# Show the R-sqaured values for each regresssion model in dscending order for evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "d04ecae1",
   "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>Models</th>\n",
       "      <th>R-Sqaured value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PolynomialFeatures</td>\n",
       "      <td>0.611993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.596419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultipleLinearRegression</td>\n",
       "      <td>0.506905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>0.235342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Models  R-Sqaured value\n",
       "1        PolynomialFeatures         0.611993\n",
       "3              RandomForest         0.596419\n",
       "0  MultipleLinearRegression         0.506905\n",
       "2              DecisionTree         0.235342"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Models': [ 'MultipleLinearRegression','PolynomialFeatures', 'DecisionTree','RandomForest' ],\n",
    "    'R-Sqaured value': [r2_multiple,r2_poly,r2_decision,r2_random_forest2],\n",
    "      })\n",
    "models.sort_values(by='R-Sqaured value', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79546e03",
   "metadata": {},
   "source": [
    "# Calculate Adjusted R-sqaured for each regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "315210b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted R2 Score for Multiple Regression: 0.5055\n"
     ]
    }
   ],
   "source": [
    "# Number of observations\n",
    "n = len(y1_test)\n",
    "\n",
    "# Number of predictor variables (independent variables)\n",
    "k = 5  # Adjust this value based on your model\n",
    "\n",
    "# Calculate adjusted R2 using statsmodels\n",
    "adjusted_r2_multiple = 1 - (1 - r2_multiple) * (n - 1) / (n - k - 1)\n",
    "\n",
    "print(f'Adjusted R2 Score for Multiple Regression: {adjusted_r2_multiple:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "3b30fdd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted R2 Score for Polynomial Regression: 0.6109\n"
     ]
    }
   ],
   "source": [
    "# Number of observations\n",
    "n = len(y1_test)\n",
    "\n",
    "# Number of predictor variables (independent variables)\n",
    "k = 5  # Adjust this value based on your model\n",
    "\n",
    "# Calculate adjusted R2 using statsmodels\n",
    "adjusted_r2_poly = 1 - (1 - r2_poly) * (n - 1) / (n - k - 1)\n",
    "\n",
    "print(f'Adjusted R2 Score for Polynomial Regression: {adjusted_r2_poly:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "0a70d543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted R2 Score for Decision Tree Regression: 0.2331\n"
     ]
    }
   ],
   "source": [
    "# Number of observations\n",
    "n = len(y1_test)\n",
    "\n",
    "# Number of predictor variables (independent variables)\n",
    "k = 5 # Adjust this value based on your model\n",
    "\n",
    "# Calculate adjusted R2 using statsmodels\n",
    "adjusted_r2_decision = 1 - (1 - r2_decision) * (n - 1) / (n - k - 1)\n",
    "\n",
    "print(f'Adjusted R2 Score for Decision Tree Regression: {adjusted_r2_decision:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "7ee56b3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted R2 Score for Random Forest Regression: 0.5952\n"
     ]
    }
   ],
   "source": [
    "# Number of observations\n",
    "n = len(y1_test)\n",
    "\n",
    "# Number of predictor variables (independent variables)\n",
    "k = 5  # Adjust this value based on your model\n",
    "\n",
    "# Calculate adjusted R2 using statsmodels\n",
    "adjusted_r2_random_forest2 = 1 - (1 - r2_random_forest2) * (n - 1) / (n - k - 1)\n",
    "\n",
    "print(f'Adjusted R2 Score for Random Forest Regression: {adjusted_r2_random_forest2:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f683301d",
   "metadata": {},
   "source": [
    "# Show the Adj-R-Sqaured value for each regression model in dscending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "88f3af6d",
   "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>Models</th>\n",
       "      <th>Adjusted R-Sqaured value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PolynomialFeatures</td>\n",
       "      <td>0.610851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.595231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultipleLinearRegression</td>\n",
       "      <td>0.505454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>0.233092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Models  Adjusted R-Sqaured value\n",
       "1        PolynomialFeatures                  0.610851\n",
       "3              RandomForest                  0.595231\n",
       "0  MultipleLinearRegression                  0.505454\n",
       "2              DecisionTree                  0.233092"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_2 = pd.DataFrame({\n",
    "    'Models': [ 'MultipleLinearRegression','PolynomialFeatures', 'DecisionTree','RandomForest' ],\n",
    "    'Adjusted R-Sqaured value': [adjusted_r2_multiple,adjusted_r2_poly,adjusted_r2_decision,adjusted_r2_random_forest2],\n",
    "      })\n",
    "models_2.sort_values(by='Adjusted R-Sqaured value', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a95e5281",
   "metadata": {},
   "source": [
    "# Calculate RMSE value for each regression model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "610f17c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for Multiple Regression: 1159.1902638271847\n"
     ]
    }
   ],
   "source": [
    "mse_value_multiple = mean_squared_error(y1_test , y1_pred)\n",
    "rmse_value_multiple = np.sqrt(mse_value_multiple)\n",
    "print(\"RMSE for Multiple Regression:\", rmse_value_multiple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "a87b7fba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for Polynomial Regression: 1028.275433249655\n"
     ]
    }
   ],
   "source": [
    "mse_value_poly = mean_squared_error(y1_test , y1_pred_poly)\n",
    "rmse_value_poly = np.sqrt(mse_value_poly)\n",
    "print(\"RMSE for Polynomial Regression:\", rmse_value_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "99ac06c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for DecisionTree Regression: 1443.5203062903608\n"
     ]
    }
   ],
   "source": [
    "mse_value_decision = mean_squared_error(y1_test , y1_pred_decision)\n",
    "rmse_value_decision = np.sqrt(mse_value_decision)\n",
    "print(\"RMSE for DecisionTree Regression:\", rmse_value_decision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "6f823b34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for Random Forest: 1048.7087753880971\n"
     ]
    }
   ],
   "source": [
    "mse_value_random_forest = mean_squared_error(y1_test , y1_pred_random_forest1)\n",
    "rmse_value_random_forest = np.sqrt(mse_value_random_forest)\n",
    "print(\"RMSE for Random Forest:\", rmse_value_random_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b17e1221",
   "metadata": {},
   "source": [
    "# Show RMSE values for each regression model in dscending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "a2a743ea",
   "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>Models</th>\n",
       "      <th>RMSE value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>1443.520306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultipleLinearRegression</td>\n",
       "      <td>1159.190264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>1048.708775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PolynomialFeatures</td>\n",
       "      <td>1028.275433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Models   RMSE value\n",
       "2              DecisionTree  1443.520306\n",
       "0  MultipleLinearRegression  1159.190264\n",
       "3              RandomForest  1048.708775\n",
       "1        PolynomialFeatures  1028.275433"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_3 = pd.DataFrame({\n",
    "    'Models': [ 'MultipleLinearRegression','PolynomialFeatures', 'DecisionTree','RandomForest' ],\n",
    "    'RMSE value': [rmse_value_multiple,rmse_value_poly,rmse_value_decision,rmse_value_random_forest],\n",
    "      })\n",
    "models_3.sort_values(by='RMSE value', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7541e83",
   "metadata": {},
   "source": [
    "# So My conclusion is among all the regression model performed so far Polynomial Regression gives the best model So we predict the test data on Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "12612cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Fat_Content</th>\n",
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       "      <th>0</th>\n",
       "      <td>1103</td>\n",
       "      <td>1</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1067</td>\n",
       "      <td>4</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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1406</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>809</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1184</td>\n",
       "      <td>2</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",
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       "2             1406                 1         0.099575         11  241.7538   \n",
       "3              809                 1         0.015388         13  155.0340   \n",
       "4             1184                 2         0.118599          4  234.2300   \n",
       "\n",
       "   Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0                  9                       1999            1   \n",
       "1                  2                       2007            2   \n",
       "2                  0                       1998            2   \n",
       "3                  2                       2007            2   \n",
       "4                  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": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28f36fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the features into polynomial features\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "degree = 3  # Change this to the degree of polynomial you want\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "x1_train_poly = poly.fit_transform(x1_train)\n",
    "x1_test_poly = poly.transform(x1_test)\n",
    "\n",
    "# Fit a linear regression model to the polynomial features\n",
    "model = LinearRegression()\n",
    "model.fit(x1_train_poly, y1_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y1_pred_poly = model.predict(x1_test_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "6930fa2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_pred = test_data[['Item_MRP','Outlet_Type','Outlet_Identifier','Item_Visibility','Outlet_Size']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "4373d5d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted values on test data: [1756.87 1435.59  656.78 ... 1840.56 3567.13 1188.51]\n"
     ]
    }
   ],
   "source": [
    "test_data_poly=poly.transform(test_data_pred)\n",
    "# Make predictions on the test set\n",
    "y1_pred_poly_test_data = model.predict(test_data_poly)\n",
    "print('predicted values on test data:',y1_pred_poly_test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "497b5655",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "f2d4522d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined predictions saved to combined_predictions.xlsx\n"
     ]
    }
   ],
   "source": [
    "excel_filename = 'combined_predictions.xlsx'\n",
    "predictions_df.to_excel(excel_filename, index=False)\n",
    "\n",
    "print(f\"Combined predictions saved to {excel_filename}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c401417",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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