3.Feature_Engineering.ipynb

{
 "cells": [
  {
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    "ExecuteTime": {
     "end_time": "2025-01-14T20:36:29.896156Z",
     "start_time": "2025-01-14T20:36:29.893152Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "source": [
    "%matplotlib inline\n",
    "# warnings.filterwarnings('ignore')\n",
    "sns.set()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:36:31.823898Z",
     "start_time": "2025-01-14T20:36:31.808249Z"
    }
   },
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T20:36:32.387047Z",
     "start_time": "2025-01-14T20:36:32.355333Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df = pd.read_csv('data/preprocessed/cleaned.csv').drop(columns=['Unnamed: 0'])\n",
    "df.head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Item_Weight Item_Fat_Content  Item_Visibility              Item_Type  \\\n",
       "0         9.30          Low Fat         0.016047                  Dairy   \n",
       "1         5.92          Regular         0.019278            Soft Drinks   \n",
       "2        17.50          Low Fat         0.016760                   Meat   \n",
       "3        19.20          Regular         0.000000  Fruits and Vegetables   \n",
       "4         8.93       Non-Edible         0.000000              Household   \n",
       "\n",
       "   Item_MRP  Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0  249.8092                       1999      Medium               Tier 1   \n",
       "1   48.2692                       2009      Medium               Tier 3   \n",
       "2  141.6180                       1999      Medium               Tier 1   \n",
       "3  182.0950                       1998         NaN               Tier 3   \n",
       "4   53.8614                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales New_Item_Type  Outlet_Age  \n",
       "0  Supermarket Type1          3735.1380            FD          14  \n",
       "1  Supermarket Type2           443.4228            DR           4  \n",
       "2  Supermarket Type1          2097.2700            FD          14  \n",
       "3      Grocery Store           732.3800            FD          15  \n",
       "4  Supermarket Type1           994.7052            NC          26  "
      ],
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Outlet_Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>FD</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>DR</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>FD</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>FD</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>Non-Edible</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</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",
       "      <td>NC</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T20:36:33.388345Z",
     "start_time": "2025-01-14T20:36:33.382343Z"
    }
   },
   "cell_type": "code",
   "source": "df.columns",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Weight', 'Item_Fat_Content', 'Item_Visibility', 'Item_Type',\n",
       "       'Item_MRP', 'Outlet_Establishment_Year', 'Outlet_Size',\n",
       "       'Outlet_Location_Type', 'Outlet_Type', 'Item_Outlet_Sales',\n",
       "       'New_Item_Type', 'Outlet_Age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "source": [
    "X, y = df.drop(columns=['Item_Outlet_Sales', 'Item_Type', 'Outlet_Establishment_Year']), df['Item_Outlet_Sales']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:36:34.616009Z",
     "start_time": "2025-01-14T20:36:34.611099Z"
    }
   },
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "source": [
    "# X.to_csv(path_or_buf= \"data/features.csv\")\n",
    "# y.to_csv(path_or_buf=\"data/targets.csv\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:36:35.187985Z",
     "start_time": "2025-01-14T20:36:35.184488Z"
    }
   },
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.model_selection import train_test_split"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:36:58.521178Z",
     "start_time": "2025-01-14T20:36:58.366170Z"
    }
   },
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.impute import KNNImputer, SimpleImputer\n",
    "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder\n",
    "from utils.custom_transformers import CustomTransformer"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:36:59.320062Z",
     "start_time": "2025-01-14T20:36:59.157890Z"
    }
   },
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "source": [
    "X.columns"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:36:59.859554Z",
     "start_time": "2025-01-14T20:36:59.854249Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Weight', 'Item_Fat_Content', 'Item_Visibility', 'Item_MRP',\n",
       "       'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'New_Item_Type',\n",
       "       'Outlet_Age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "source": [
    "X.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:00.612110Z",
     "start_time": "2025-01-14T20:37:00.602792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Item_Weight Item_Fat_Content  Item_Visibility  Item_MRP Outlet_Size  \\\n",
       "0         9.30          Low Fat         0.016047  249.8092      Medium   \n",
       "1         5.92          Regular         0.019278   48.2692      Medium   \n",
       "2        17.50          Low Fat         0.016760  141.6180      Medium   \n",
       "3        19.20          Regular         0.000000  182.0950         NaN   \n",
       "4         8.93       Non-Edible         0.000000   53.8614        High   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type New_Item_Type  Outlet_Age  \n",
       "0               Tier 1  Supermarket Type1            FD          14  \n",
       "1               Tier 3  Supermarket Type2            DR           4  \n",
       "2               Tier 1  Supermarket Type1            FD          14  \n",
       "3               Tier 3      Grocery Store            FD          15  \n",
       "4               Tier 3  Supermarket Type1            NC          26  "
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Outlet_Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>FD</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>DR</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>FD</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>FD</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>Non-Edible</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>NC</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "source": [
    "df['Outlet_Location_Type'].unique()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:01.535637Z",
     "start_time": "2025-01-14T20:37:01.530287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Tier 1', 'Tier 3', 'Tier 2'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "X.columns"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:10.015660Z",
     "start_time": "2025-01-14T20:37:10.002216Z"
    }
   },
   "cell_type": "code",
   "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:10.566013Z",
     "start_time": "2025-01-14T20:37:10.562162Z"
    }
   },
   "cell_type": "code",
   "source": [
    "categorical_encoder = CustomTransformer(transformers=[\n",
    "    ('temp_impute',SimpleImputer(strategy='constant', fill_value='Missing'),['Outlet_Size']),\n",
    "    ('numerical_encoder', OrdinalEncoder(categories=[\n",
    "        ['Non-Edible','Low Fat', 'Regular'],\n",
    "        ['Small', 'Medium', 'High', 'Missing'],\n",
    "        ['Tier 1', 'Tier 2', 'Tier 3']\n",
    "    ]),['Item_Fat_Content', 'Outlet_Size', 'Outlet_Location_Type']),\n",
    "    ('ohe', OneHotEncoder(sparse_output=False, drop='first'), ['New_Item_Type','Outlet_Type']),\n",
    "    ('temp_impute_restore', SimpleImputer(strategy='constant', fill_value=np.NAN, missing_values=3.),['Outlet_Size'])\n",
    "])"
   ],
   "outputs": [],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "source": [
    "X_train = categorical_encoder.fit_transform(X_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:11.487934Z",
     "start_time": "2025-01-14T20:37:11.466185Z"
    }
   },
   "outputs": [],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "source": [
    "X_train"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:12.479652Z",
     "start_time": "2025-01-14T20:37:12.464526Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Item_Weight  Item_Fat_Content  Item_Visibility  Item_MRP  Outlet_Size  \\\n",
       "549         9.500               2.0         0.035206  171.3448          1.0   \n",
       "7757       18.000               0.0         0.047473  170.5422          NaN   \n",
       "764        17.600               2.0         0.076122  111.7202          0.0   \n",
       "6867        8.325               1.0         0.029845   41.6138          NaN   \n",
       "2716       12.850               1.0         0.137228  155.5630          0.0   \n",
       "...           ...               ...              ...       ...          ...   \n",
       "5734        9.395               2.0         0.286345  139.1838          NaN   \n",
       "5191       15.600               1.0         0.117575   75.6670          NaN   \n",
       "5390       17.600               0.0         0.018944  237.3590          NaN   \n",
       "860        20.350               1.0         0.054363  117.9466          NaN   \n",
       "7270       16.350               0.0         0.016993   95.7410          0.0   \n",
       "\n",
       "      Outlet_Location_Type  Outlet_Age  New_Item_Type_FD  New_Item_Type_NC  \\\n",
       "549                    0.0          14               1.0               0.0   \n",
       "7757                   1.0          11               0.0               1.0   \n",
       "764                    0.0          16               1.0               0.0   \n",
       "6867                   1.0          11               1.0               0.0   \n",
       "2716                   0.0          16               1.0               0.0   \n",
       "...                    ...         ...               ...               ...   \n",
       "5734                   2.0          15               1.0               0.0   \n",
       "5191                   1.0           6               1.0               0.0   \n",
       "5390                   1.0          11               0.0               1.0   \n",
       "860                    1.0           6               1.0               0.0   \n",
       "7270                   0.0          16               0.0               1.0   \n",
       "\n",
       "      Outlet_Type_Supermarket Type1  Outlet_Type_Supermarket Type2  \\\n",
       "549                             1.0                            0.0   \n",
       "7757                            1.0                            0.0   \n",
       "764                             1.0                            0.0   \n",
       "6867                            1.0                            0.0   \n",
       "2716                            1.0                            0.0   \n",
       "...                             ...                            ...   \n",
       "5734                            0.0                            0.0   \n",
       "5191                            1.0                            0.0   \n",
       "5390                            1.0                            0.0   \n",
       "860                             1.0                            0.0   \n",
       "7270                            1.0                            0.0   \n",
       "\n",
       "      Outlet_Type_Supermarket Type3  \n",
       "549                             0.0  \n",
       "7757                            0.0  \n",
       "764                             0.0  \n",
       "6867                            0.0  \n",
       "2716                            0.0  \n",
       "...                             ...  \n",
       "5734                            0.0  \n",
       "5191                            0.0  \n",
       "5390                            0.0  \n",
       "860                             0.0  \n",
       "7270                            0.0  \n",
       "\n",
       "[6818 rows x 12 columns]"
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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_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Age</th>\n",
       "      <th>New_Item_Type_FD</th>\n",
       "      <th>New_Item_Type_NC</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>9.500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.035206</td>\n",
       "      <td>171.3448</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7757</th>\n",
       "      <td>18.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.047473</td>\n",
       "      <td>170.5422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>17.600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.076122</td>\n",
       "      <td>111.7202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6867</th>\n",
       "      <td>8.325</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.029845</td>\n",
       "      <td>41.6138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2716</th>\n",
       "      <td>12.850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137228</td>\n",
       "      <td>155.5630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>9.395</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.286345</td>\n",
       "      <td>139.1838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>15.600</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.117575</td>\n",
       "      <td>75.6670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>17.600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018944</td>\n",
       "      <td>237.3590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>20.350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.054363</td>\n",
       "      <td>117.9466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>16.350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016993</td>\n",
       "      <td>95.7410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6818 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.preprocessing import StandardScaler, FunctionTransformer"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:13.372577Z",
     "start_time": "2025-01-14T20:37:13.368951Z"
    }
   },
   "outputs": [],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "source": [
    "X_train.columns"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:13.929543Z",
     "start_time": "2025-01-14T20:37:13.925209Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Weight', 'Item_Fat_Content', 'Item_Visibility', 'Item_MRP',\n",
       "       'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Age', 'New_Item_Type_FD',\n",
       "       'New_Item_Type_NC', 'Outlet_Type_Supermarket Type1',\n",
       "       'Outlet_Type_Supermarket Type2', 'Outlet_Type_Supermarket Type3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.preprocessing import KBinsDiscretizer"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:14.628849Z",
     "start_time": "2025-01-14T20:37:14.625397Z"
    }
   },
   "outputs": [],
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "source": [
    "descretizer = CustomTransformer(transformers=[\n",
    "    ('descrete_age' ,KBinsDiscretizer(encode='ordinal', strategy='quantile', subsample=200_000), ['Outlet_Age'])\n",
    "])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:15.436052Z",
     "start_time": "2025-01-14T20:37:15.433227Z"
    }
   },
   "outputs": [],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "source": [
    "X_train = descretizer.fit_transform(X_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:16.072580Z",
     "start_time": "2025-01-14T20:37:16.065651Z"
    }
   },
   "outputs": [],
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "source": [
    "descretizer.transformers[0][1].bin_edges_"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:17.333346Z",
     "start_time": "2025-01-14T20:37:17.327604Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([array([ 4.,  6., 11., 15., 26., 28.])], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 22
  },
  {
   "cell_type": "code",
   "source": [
    "imputer_and_normalizer = CustomTransformer(transformers=[\n",
    "    ('root_transform',FunctionTransformer(func=np.sqrt),['Item_Visibility']),\n",
    "    ('temp_impute', SimpleImputer(strategy='constant', fill_value=np.NAN, missing_values=0.),['Item_Visibility']),\n",
    "    ('knn_imputer' ,KNNImputer(n_neighbors=5, weights='distance'),list(X_train.columns)),\n",
    "    ('log_transform',FunctionTransformer(func=np.log1p),['Item_Weight']),\n",
    "    ('scaler',StandardScaler(),['Item_Weight', 'Item_MRP'])\n",
    "])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:18.172004Z",
     "start_time": "2025-01-14T20:37:18.167720Z"
    }
   },
   "outputs": [],
   "execution_count": 23
  },
  {
   "cell_type": "code",
   "source": [
    "X_train = imputer_and_normalizer.fit_transform(X_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:20.466992Z",
     "start_time": "2025-01-14T20:37:18.941607Z"
    }
   },
   "outputs": [],
   "execution_count": 24
  },
  {
   "cell_type": "code",
   "source": [
    "X_train"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:20.492551Z",
     "start_time": "2025-01-14T20:37:20.476358Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Item_Weight  Item_Fat_Content  Item_Visibility  Item_MRP  Outlet_Size  \\\n",
       "549     -0.674890               2.0         0.187632  0.470709     1.000000   \n",
       "7757     1.096958               0.0         0.217883  0.457877     0.360989   \n",
       "764      1.033389               2.0         0.275902 -0.482625     0.000000   \n",
       "6867    -1.029449               1.0         0.172758 -1.603553     0.199708   \n",
       "2716     0.152411               1.0         0.370443  0.218375     0.000000   \n",
       "...           ...               ...              ...       ...          ...   \n",
       "5734    -0.704916               2.0         0.535112 -0.043511     1.183311   \n",
       "5191     0.693521               1.0         0.342891 -1.059078     0.000000   \n",
       "5390     1.033389               0.0         0.137636  1.526207     1.029452   \n",
       "860      1.445352               1.0         0.233158 -0.383072     0.358409   \n",
       "7270     0.825543               0.0         0.130358 -0.738116     0.000000   \n",
       "\n",
       "      Outlet_Location_Type  Outlet_Age  New_Item_Type_FD  New_Item_Type_NC  \\\n",
       "549                    0.0         2.0               1.0               0.0   \n",
       "7757                   1.0         2.0               0.0               1.0   \n",
       "764                    0.0         3.0               1.0               0.0   \n",
       "6867                   1.0         2.0               1.0               0.0   \n",
       "2716                   0.0         3.0               1.0               0.0   \n",
       "...                    ...         ...               ...               ...   \n",
       "5734                   2.0         3.0               1.0               0.0   \n",
       "5191                   1.0         1.0               1.0               0.0   \n",
       "5390                   1.0         2.0               0.0               1.0   \n",
       "860                    1.0         1.0               1.0               0.0   \n",
       "7270                   0.0         3.0               0.0               1.0   \n",
       "\n",
       "      Outlet_Type_Supermarket Type1  Outlet_Type_Supermarket Type2  \\\n",
       "549                             1.0                            0.0   \n",
       "7757                            1.0                            0.0   \n",
       "764                             1.0                            0.0   \n",
       "6867                            1.0                            0.0   \n",
       "2716                            1.0                            0.0   \n",
       "...                             ...                            ...   \n",
       "5734                            0.0                            0.0   \n",
       "5191                            1.0                            0.0   \n",
       "5390                            1.0                            0.0   \n",
       "860                             1.0                            0.0   \n",
       "7270                            1.0                            0.0   \n",
       "\n",
       "      Outlet_Type_Supermarket Type3  \n",
       "549                             0.0  \n",
       "7757                            0.0  \n",
       "764                             0.0  \n",
       "6867                            0.0  \n",
       "2716                            0.0  \n",
       "...                             ...  \n",
       "5734                            0.0  \n",
       "5191                            0.0  \n",
       "5390                            0.0  \n",
       "860                             0.0  \n",
       "7270                            0.0  \n",
       "\n",
       "[6818 rows x 12 columns]"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Age</th>\n",
       "      <th>New_Item_Type_FD</th>\n",
       "      <th>New_Item_Type_NC</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>-0.674890</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.187632</td>\n",
       "      <td>0.470709</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7757</th>\n",
       "      <td>1.096958</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217883</td>\n",
       "      <td>0.457877</td>\n",
       "      <td>0.360989</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>1.033389</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.275902</td>\n",
       "      <td>-0.482625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6867</th>\n",
       "      <td>-1.029449</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.172758</td>\n",
       "      <td>-1.603553</td>\n",
       "      <td>0.199708</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2716</th>\n",
       "      <td>0.152411</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.370443</td>\n",
       "      <td>0.218375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>-0.704916</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.535112</td>\n",
       "      <td>-0.043511</td>\n",
       "      <td>1.183311</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>0.693521</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.342891</td>\n",
       "      <td>-1.059078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>1.033389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.137636</td>\n",
       "      <td>1.526207</td>\n",
       "      <td>1.029452</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>1.445352</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.233158</td>\n",
       "      <td>-0.383072</td>\n",
       "      <td>0.358409</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>0.825543</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.130358</td>\n",
       "      <td>-0.738116</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6818 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "source": [
    "sns.kdeplot(np.log1p(y_train))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:23.266703Z",
     "start_time": "2025-01-14T20:37:23.090473Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Item_Outlet_Sales', ylabel='Density'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 26
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.pipeline import Pipeline"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:24.182363Z",
     "start_time": "2025-01-14T20:37:24.166785Z"
    }
   },
   "outputs": [],
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "source": [
    "preprocessing_pipeline = Pipeline(steps=[\n",
    "    ('categorical_encoder',categorical_encoder),\n",
    "    ('descretizer',descretizer),\n",
    "    ('imputer_and_normalizer', imputer_and_normalizer)\n",
    "],verbose=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:25.568511Z",
     "start_time": "2025-01-14T20:37:25.565110Z"
    }
   },
   "outputs": [],
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "source": [
    "X_test = preprocessing_pipeline.transform(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:26.739162Z",
     "start_time": "2025-01-14T20:37:26.414905Z"
    }
   },
   "outputs": [],
   "execution_count": 29
  },
  {
   "cell_type": "code",
   "source": [
    "X_test"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:27.360904Z",
     "start_time": "2025-01-14T20:37:27.344512Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Item_Weight  Item_Fat_Content  Item_Visibility  Item_MRP  Outlet_Size  \\\n",
       "7503     0.449881               1.0         0.162172 -0.998908          2.0   \n",
       "2957    -1.158761               0.0         0.266713 -1.586048          0.0   \n",
       "7031     0.488682               2.0         0.203256 -1.596652          1.0   \n",
       "1084    -0.286352               2.0         0.211582  0.508453          1.0   \n",
       "856     -0.483407               2.0         0.111605  0.889079          0.0   \n",
       "...           ...               ...              ...       ...          ...   \n",
       "7205    -0.083133               2.0         0.306032 -0.235593          0.0   \n",
       "3257    -1.479864               1.0         0.304694  0.100223          1.0   \n",
       "6346     0.488682               2.0         0.203014 -1.596652          2.0   \n",
       "6318    -0.590726               2.0         0.375744 -1.461459          1.0   \n",
       "6339    -1.167137               1.0         0.234731 -0.522515          0.0   \n",
       "\n",
       "      Outlet_Location_Type  Outlet_Age  New_Item_Type_FD  New_Item_Type_NC  \\\n",
       "7503                   2.0         4.0               1.0               0.0   \n",
       "2957                   0.0         3.0               0.0               1.0   \n",
       "7031                   0.0         2.0               1.0               0.0   \n",
       "1084                   2.0         4.0               0.0               0.0   \n",
       "856                    1.0         1.0               1.0               0.0   \n",
       "...                    ...         ...               ...               ...   \n",
       "7205                   0.0         3.0               1.0               0.0   \n",
       "3257                   2.0         0.0               1.0               0.0   \n",
       "6346                   2.0         4.0               1.0               0.0   \n",
       "6318                   2.0         0.0               1.0               0.0   \n",
       "6339                   1.0         2.0               1.0               0.0   \n",
       "\n",
       "      Outlet_Type_Supermarket Type1  Outlet_Type_Supermarket Type2  \\\n",
       "7503                            1.0                            0.0   \n",
       "2957                            1.0                            0.0   \n",
       "7031                            1.0                            0.0   \n",
       "1084                            0.0                            0.0   \n",
       "856                             1.0                            0.0   \n",
       "...                             ...                            ...   \n",
       "7205                            1.0                            0.0   \n",
       "3257                            0.0                            1.0   \n",
       "6346                            1.0                            0.0   \n",
       "6318                            0.0                            1.0   \n",
       "6339                            1.0                            0.0   \n",
       "\n",
       "      Outlet_Type_Supermarket Type3  \n",
       "7503                            0.0  \n",
       "2957                            0.0  \n",
       "7031                            0.0  \n",
       "1084                            1.0  \n",
       "856                             0.0  \n",
       "...                             ...  \n",
       "7205                            0.0  \n",
       "3257                            0.0  \n",
       "6346                            0.0  \n",
       "6318                            0.0  \n",
       "6339                            0.0  \n",
       "\n",
       "[1705 rows x 12 columns]"
      ],
      "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_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Age</th>\n",
       "      <th>New_Item_Type_FD</th>\n",
       "      <th>New_Item_Type_NC</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7503</th>\n",
       "      <td>0.449881</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.162172</td>\n",
       "      <td>-0.998908</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>-1.158761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.266713</td>\n",
       "      <td>-1.586048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7031</th>\n",
       "      <td>0.488682</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.203256</td>\n",
       "      <td>-1.596652</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>-0.286352</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.211582</td>\n",
       "      <td>0.508453</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>-0.483407</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.111605</td>\n",
       "      <td>0.889079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7205</th>\n",
       "      <td>-0.083133</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.306032</td>\n",
       "      <td>-0.235593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3257</th>\n",
       "      <td>-1.479864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.304694</td>\n",
       "      <td>0.100223</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6346</th>\n",
       "      <td>0.488682</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.203014</td>\n",
       "      <td>-1.596652</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6318</th>\n",
       "      <td>-0.590726</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>-1.461459</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6339</th>\n",
       "      <td>-1.167137</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.234731</td>\n",
       "      <td>-0.522515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1705 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 30
  },
  {
   "cell_type": "code",
   "source": [
    "import dill as pickle"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:33.816575Z",
     "start_time": "2025-01-14T20:37:33.788094Z"
    }
   },
   "outputs": [],
   "execution_count": 31
  },
  {
   "cell_type": "code",
   "source": [
    "with open('artifacts/preprocessing.pkl', 'wb') as file:\n",
    "    pickle.dump(preprocessing_pipeline,file)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:55.170031Z",
     "start_time": "2025-01-14T20:37:55.138744Z"
    }
   },
   "outputs": [],
   "execution_count": 32
  },
  {
   "cell_type": "code",
   "source": [
    "X_train"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:55.892140Z",
     "start_time": "2025-01-14T20:37:55.875791Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Item_Weight  Item_Fat_Content  Item_Visibility  Item_MRP  Outlet_Size  \\\n",
       "549     -0.674890               2.0         0.187632  0.470709     1.000000   \n",
       "7757     1.096958               0.0         0.217883  0.457877     0.360989   \n",
       "764      1.033389               2.0         0.275902 -0.482625     0.000000   \n",
       "6867    -1.029449               1.0         0.172758 -1.603553     0.199708   \n",
       "2716     0.152411               1.0         0.370443  0.218375     0.000000   \n",
       "...           ...               ...              ...       ...          ...   \n",
       "5734    -0.704916               2.0         0.535112 -0.043511     1.183311   \n",
       "5191     0.693521               1.0         0.342891 -1.059078     0.000000   \n",
       "5390     1.033389               0.0         0.137636  1.526207     1.029452   \n",
       "860      1.445352               1.0         0.233158 -0.383072     0.358409   \n",
       "7270     0.825543               0.0         0.130358 -0.738116     0.000000   \n",
       "\n",
       "      Outlet_Location_Type  Outlet_Age  New_Item_Type_FD  New_Item_Type_NC  \\\n",
       "549                    0.0         2.0               1.0               0.0   \n",
       "7757                   1.0         2.0               0.0               1.0   \n",
       "764                    0.0         3.0               1.0               0.0   \n",
       "6867                   1.0         2.0               1.0               0.0   \n",
       "2716                   0.0         3.0               1.0               0.0   \n",
       "...                    ...         ...               ...               ...   \n",
       "5734                   2.0         3.0               1.0               0.0   \n",
       "5191                   1.0         1.0               1.0               0.0   \n",
       "5390                   1.0         2.0               0.0               1.0   \n",
       "860                    1.0         1.0               1.0               0.0   \n",
       "7270                   0.0         3.0               0.0               1.0   \n",
       "\n",
       "      Outlet_Type_Supermarket Type1  Outlet_Type_Supermarket Type2  \\\n",
       "549                             1.0                            0.0   \n",
       "7757                            1.0                            0.0   \n",
       "764                             1.0                            0.0   \n",
       "6867                            1.0                            0.0   \n",
       "2716                            1.0                            0.0   \n",
       "...                             ...                            ...   \n",
       "5734                            0.0                            0.0   \n",
       "5191                            1.0                            0.0   \n",
       "5390                            1.0                            0.0   \n",
       "860                             1.0                            0.0   \n",
       "7270                            1.0                            0.0   \n",
       "\n",
       "      Outlet_Type_Supermarket Type3  \n",
       "549                             0.0  \n",
       "7757                            0.0  \n",
       "764                             0.0  \n",
       "6867                            0.0  \n",
       "2716                            0.0  \n",
       "...                             ...  \n",
       "5734                            0.0  \n",
       "5191                            0.0  \n",
       "5390                            0.0  \n",
       "860                             0.0  \n",
       "7270                            0.0  \n",
       "\n",
       "[6818 rows x 12 columns]"
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       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Size</th>\n",
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       "      <th>549</th>\n",
       "      <td>-0.674890</td>\n",
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       "      <td>0.187632</td>\n",
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       "      <td>0.457877</td>\n",
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       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>1.033389</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.275902</td>\n",
       "      <td>-0.482625</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6867</th>\n",
       "      <td>-1.029449</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.172758</td>\n",
       "      <td>-1.603553</td>\n",
       "      <td>0.199708</td>\n",
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       "    <tr>\n",
       "      <th>2716</th>\n",
       "      <td>0.152411</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.370443</td>\n",
       "      <td>0.218375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>-0.704916</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.535112</td>\n",
       "      <td>-0.043511</td>\n",
       "      <td>1.183311</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>0.693521</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.342891</td>\n",
       "      <td>-1.059078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>1.033389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.137636</td>\n",
       "      <td>1.526207</td>\n",
       "      <td>1.029452</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>1.445352</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.233158</td>\n",
       "      <td>-0.383072</td>\n",
       "      <td>0.358409</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>0.825543</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.130358</td>\n",
       "      <td>-0.738116</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6818 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "cell_type": "code",
   "source": [
    "y_trf = FunctionTransformer(func=np.log1p, inverse_func=lambda x: np.exp(x)-1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:37:59.530323Z",
     "start_time": "2025-01-14T20:37:59.526751Z"
    }
   },
   "outputs": [],
   "execution_count": 34
  },
  {
   "cell_type": "code",
   "source": [
    "y_train = y_trf.transform(y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:38:03.131920Z",
     "start_time": "2025-01-14T20:38:03.128574Z"
    }
   },
   "outputs": [],
   "execution_count": 35
  },
  {
   "cell_type": "code",
   "source": [
    "y_test = y_trf.transform(y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:38:03.707973Z",
     "start_time": "2025-01-14T20:38:03.704047Z"
    }
   },
   "outputs": [],
   "execution_count": 36
  },
  {
   "cell_type": "code",
   "source": [
    "with open('artifacts/preprocessing_y.pkl', 'wb') as file:\n",
    "    pickle.dump(y_trf,file)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:38:04.330621Z",
     "start_time": "2025-01-14T20:38:04.320755Z"
    }
   },
   "outputs": [],
   "execution_count": 37
  },
  {
   "cell_type": "code",
   "source": [
    "X_train.to_csv(path_or_buf='data/X_train.csv')\n",
    "X_test.to_csv(path_or_buf='data/X_test.csv')\n",
    "y_train.to_csv(path_or_buf='data/y_train.csv')\n",
    "y_test.to_csv(path_or_buf='data/y_test.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T20:38:29.080854Z",
     "start_time": "2025-01-14T20:38:28.930601Z"
    }
   },
   "outputs": [],
   "execution_count": 38
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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