Data_Engineering_Pretest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "import os\n",
    "import time\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>code</th>\n",
       "      <th>descrption</th>\n",
       "      <th>type</th>\n",
       "      <th>brand</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3000005040</td>\n",
       "      <td>AUNT JEM ORIGINAL PANCAKE MIX</td>\n",
       "      <td>Type 1</td>\n",
       "      <td>Aunt Jemima</td>\n",
       "      <td>2 LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000005070</td>\n",
       "      <td>A/JEM COMPLETE PANCAKE MI</td>\n",
       "      <td>Type 1</td>\n",
       "      <td>Aunt Jemima</td>\n",
       "      <td>32    OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000005300</td>\n",
       "      <td>AJ BUTTERMILK PANCAKE MIX</td>\n",
       "      <td>Type 1</td>\n",
       "      <td>Aunt Jemima</td>\n",
       "      <td>32 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3000005350</td>\n",
       "      <td>A J BTRMLK COMP PNCK MIX</td>\n",
       "      <td>Type 1</td>\n",
       "      <td>Aunt Jemima</td>\n",
       "      <td>1 LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1600015760</td>\n",
       "      <td>BC PANCAKE MIX BUTTERMILK</td>\n",
       "      <td>Type 1</td>\n",
       "      <td>Bisquick</td>\n",
       "      <td>6.75 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>3905955112</td>\n",
       "      <td>SPRNG TREE SF MAPLE SYRUP</td>\n",
       "      <td>Type 4</td>\n",
       "      <td>Spring Tree</td>\n",
       "      <td>P    24 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>3905972884</td>\n",
       "      <td>SPRING TREE MAPLE SYRUP</td>\n",
       "      <td>Type 4</td>\n",
       "      <td>Spring Tree</td>\n",
       "      <td>8.5 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>3068434050</td>\n",
       "      <td>TREE OF LIFE REGULAR BLACKSTRA</td>\n",
       "      <td>Type 4</td>\n",
       "      <td>Tree of Life</td>\n",
       "      <td>##########</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>3068434052</td>\n",
       "      <td>TREE OF LIFE REGULAR BLACKSTRA</td>\n",
       "      <td>Type 4</td>\n",
       "      <td>Tree of Life</td>\n",
       "      <td>##########</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>6138914020</td>\n",
       "      <td>VRMT GLD PURE MPL SYRP</td>\n",
       "      <td>Type 4</td>\n",
       "      <td>Vermont Gold</td>\n",
       "      <td>8.5 OZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>927 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           code                      descrption    type         brand  \\\n",
       "0    3000005040   AUNT JEM ORIGINAL PANCAKE MIX  Type 1   Aunt Jemima   \n",
       "1    3000005070       A/JEM COMPLETE PANCAKE MI  Type 1   Aunt Jemima   \n",
       "2    3000005300       AJ BUTTERMILK PANCAKE MIX  Type 1   Aunt Jemima   \n",
       "3    3000005350        A J BTRMLK COMP PNCK MIX  Type 1   Aunt Jemima   \n",
       "4    1600015760       BC PANCAKE MIX BUTTERMILK  Type 1      Bisquick   \n",
       "..          ...                             ...     ...           ...   \n",
       "922  3905955112       SPRNG TREE SF MAPLE SYRUP  Type 4   Spring Tree   \n",
       "923  3905972884         SPRING TREE MAPLE SYRUP  Type 4   Spring Tree   \n",
       "924  3068434050  TREE OF LIFE REGULAR BLACKSTRA  Type 4  Tree of Life   \n",
       "925  3068434052  TREE OF LIFE REGULAR BLACKSTRA  Type 4  Tree of Life   \n",
       "926  6138914020          VRMT GLD PURE MPL SYRP  Type 4  Vermont Gold   \n",
       "\n",
       "           size  \n",
       "0          2 LB  \n",
       "1      32    OZ  \n",
       "2         32 OZ  \n",
       "3          1 LB  \n",
       "4       6.75 OZ  \n",
       "..          ...  \n",
       "922  P    24 OZ  \n",
       "923      8.5 OZ  \n",
       "924  ##########  \n",
       "925  ##########  \n",
       "926      8.5 OZ  \n",
       "\n",
       "[927 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting data from csv file\n",
    "item_df = pd.read_csv(r'Raw_datasets/item.csv')\n",
    "\n",
    "item_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>code</th>\n",
       "      <th>descrption</th>\n",
       "      <th>type</th>\n",
       "      <th>brand</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3000005040</td>\n",
       "      <td>AUNT JEM ORIGINAL PANCAKE MIX</td>\n",
       "      <td>Type 1</td>\n",
       "      <td>Aunt Jemima</td>\n",
       "      <td>2 LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000005070</td>\n",
       "      <td>A/JEM COMPLETE PANCAKE MI</td>\n",
       "      <td>Type 1</td>\n",
       "      <td>Aunt Jemima</td>\n",
       "      <td>32    OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000005300</td>\n",
       "      <td>AJ BUTTERMILK PANCAKE MIX</td>\n",
       "      <td>Type 1</td>\n",
       "      <td>Aunt Jemima</td>\n",
       "      <td>32 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3000005350</td>\n",
       "      <td>A J BTRMLK COMP PNCK MIX</td>\n",
       "      <td>Type 1</td>\n",
       "      <td>Aunt Jemima</td>\n",
       "      <td>1 LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1600015760</td>\n",
       "      <td>BC PANCAKE MIX BUTTERMILK</td>\n",
       "      <td>Type 1</td>\n",
       "      <td>Bisquick</td>\n",
       "      <td>6.75 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>3905955112</td>\n",
       "      <td>SPRNG TREE SF MAPLE SYRUP</td>\n",
       "      <td>Type 4</td>\n",
       "      <td>Spring Tree</td>\n",
       "      <td>P    24 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>3905972884</td>\n",
       "      <td>SPRING TREE MAPLE SYRUP</td>\n",
       "      <td>Type 4</td>\n",
       "      <td>Spring Tree</td>\n",
       "      <td>8.5 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>3068434050</td>\n",
       "      <td>TREE OF LIFE REGULAR BLACKSTRA</td>\n",
       "      <td>Type 4</td>\n",
       "      <td>Tree of Life</td>\n",
       "      <td>##########</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>3068434052</td>\n",
       "      <td>TREE OF LIFE REGULAR BLACKSTRA</td>\n",
       "      <td>Type 4</td>\n",
       "      <td>Tree of Life</td>\n",
       "      <td>##########</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>6138914020</td>\n",
       "      <td>VRMT GLD PURE MPL SYRP</td>\n",
       "      <td>Type 4</td>\n",
       "      <td>Vermont Gold</td>\n",
       "      <td>8.5 OZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>927 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           code                      descrption    type         brand  \\\n",
       "0    3000005040   AUNT JEM ORIGINAL PANCAKE MIX  Type 1   Aunt Jemima   \n",
       "1    3000005070       A/JEM COMPLETE PANCAKE MI  Type 1   Aunt Jemima   \n",
       "2    3000005300       AJ BUTTERMILK PANCAKE MIX  Type 1   Aunt Jemima   \n",
       "3    3000005350        A J BTRMLK COMP PNCK MIX  Type 1   Aunt Jemima   \n",
       "4    1600015760       BC PANCAKE MIX BUTTERMILK  Type 1      Bisquick   \n",
       "..          ...                             ...     ...           ...   \n",
       "922  3905955112       SPRNG TREE SF MAPLE SYRUP  Type 4   Spring Tree   \n",
       "923  3905972884         SPRING TREE MAPLE SYRUP  Type 4   Spring Tree   \n",
       "924  3068434050  TREE OF LIFE REGULAR BLACKSTRA  Type 4  Tree of Life   \n",
       "925  3068434052  TREE OF LIFE REGULAR BLACKSTRA  Type 4  Tree of Life   \n",
       "926  6138914020          VRMT GLD PURE MPL SYRP  Type 4  Vermont Gold   \n",
       "\n",
       "           size  \n",
       "0          2 LB  \n",
       "1      32    OZ  \n",
       "2         32 OZ  \n",
       "3          1 LB  \n",
       "4       6.75 OZ  \n",
       "..          ...  \n",
       "922  P    24 OZ  \n",
       "923      8.5 OZ  \n",
       "924  ##########  \n",
       "925  ##########  \n",
       "926      8.5 OZ  \n",
       "\n",
       "[927 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking and dropping duplicate rows\n",
    "item_df = item_df.drop_duplicates()\n",
    "\n",
    "item_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>code</th>\n",
       "      <th>supermarkets</th>\n",
       "      <th>week</th>\n",
       "      <th>feature</th>\n",
       "      <th>display</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2700042240</td>\n",
       "      <td>285</td>\n",
       "      <td>91</td>\n",
       "      <td>Not on Feature</td>\n",
       "      <td>Mid-Aisle End Cap</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2700042292</td>\n",
       "      <td>285</td>\n",
       "      <td>92</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2700042274</td>\n",
       "      <td>285</td>\n",
       "      <td>92</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2700042273</td>\n",
       "      <td>285</td>\n",
       "      <td>92</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2700042254</td>\n",
       "      <td>285</td>\n",
       "      <td>92</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351367</th>\n",
       "      <td>9999966721</td>\n",
       "      <td>387</td>\n",
       "      <td>101</td>\n",
       "      <td>Wrap Front Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351368</th>\n",
       "      <td>9999966720</td>\n",
       "      <td>387</td>\n",
       "      <td>101</td>\n",
       "      <td>Wrap Front Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351369</th>\n",
       "      <td>3620000507</td>\n",
       "      <td>387</td>\n",
       "      <td>104</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351370</th>\n",
       "      <td>3620000493</td>\n",
       "      <td>387</td>\n",
       "      <td>104</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351371</th>\n",
       "      <td>9999966715</td>\n",
       "      <td>387</td>\n",
       "      <td>101</td>\n",
       "      <td>Wrap Front Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351372 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              code  supermarkets  week                feature  \\\n",
       "0       2700042240           285    91         Not on Feature   \n",
       "1       2700042292           285    92  Interior Page Feature   \n",
       "2       2700042274           285    92  Interior Page Feature   \n",
       "3       2700042273           285    92  Interior Page Feature   \n",
       "4       2700042254           285    92  Interior Page Feature   \n",
       "...            ...           ...   ...                    ...   \n",
       "351367  9999966721           387   101     Wrap Front Feature   \n",
       "351368  9999966720           387   101     Wrap Front Feature   \n",
       "351369  3620000507           387   104  Interior Page Feature   \n",
       "351370  3620000493           387   104  Interior Page Feature   \n",
       "351371  9999966715           387   101     Wrap Front Feature   \n",
       "\n",
       "                  display  province  \n",
       "0       Mid-Aisle End Cap         2  \n",
       "1          Not on Display         2  \n",
       "2          Not on Display         2  \n",
       "3          Not on Display         2  \n",
       "4          Not on Display         2  \n",
       "...                   ...       ...  \n",
       "351367     Not on Display         1  \n",
       "351368     Not on Display         1  \n",
       "351369     Not on Display         1  \n",
       "351370     Not on Display         1  \n",
       "351371     Not on Display         1  \n",
       "\n",
       "[351372 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting data from csv file\n",
    "promotion_df = pd.read_csv(r'Raw_datasets\\promotion.csv')\n",
    "\n",
    "promotion_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>code</th>\n",
       "      <th>supermarkets</th>\n",
       "      <th>week</th>\n",
       "      <th>feature</th>\n",
       "      <th>display</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2700042240</td>\n",
       "      <td>285</td>\n",
       "      <td>91</td>\n",
       "      <td>Not on Feature</td>\n",
       "      <td>Mid-Aisle End Cap</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2700042292</td>\n",
       "      <td>285</td>\n",
       "      <td>92</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2700042274</td>\n",
       "      <td>285</td>\n",
       "      <td>92</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2700042273</td>\n",
       "      <td>285</td>\n",
       "      <td>92</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2700042254</td>\n",
       "      <td>285</td>\n",
       "      <td>92</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351367</th>\n",
       "      <td>9999966721</td>\n",
       "      <td>387</td>\n",
       "      <td>101</td>\n",
       "      <td>Wrap Front Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351368</th>\n",
       "      <td>9999966720</td>\n",
       "      <td>387</td>\n",
       "      <td>101</td>\n",
       "      <td>Wrap Front Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351369</th>\n",
       "      <td>3620000507</td>\n",
       "      <td>387</td>\n",
       "      <td>104</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351370</th>\n",
       "      <td>3620000493</td>\n",
       "      <td>387</td>\n",
       "      <td>104</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351371</th>\n",
       "      <td>9999966715</td>\n",
       "      <td>387</td>\n",
       "      <td>101</td>\n",
       "      <td>Wrap Front Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351372 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              code  supermarkets  week                feature  \\\n",
       "0       2700042240           285    91         Not on Feature   \n",
       "1       2700042292           285    92  Interior Page Feature   \n",
       "2       2700042274           285    92  Interior Page Feature   \n",
       "3       2700042273           285    92  Interior Page Feature   \n",
       "4       2700042254           285    92  Interior Page Feature   \n",
       "...            ...           ...   ...                    ...   \n",
       "351367  9999966721           387   101     Wrap Front Feature   \n",
       "351368  9999966720           387   101     Wrap Front Feature   \n",
       "351369  3620000507           387   104  Interior Page Feature   \n",
       "351370  3620000493           387   104  Interior Page Feature   \n",
       "351371  9999966715           387   101     Wrap Front Feature   \n",
       "\n",
       "                  display  province  \n",
       "0       Mid-Aisle End Cap         2  \n",
       "1          Not on Display         2  \n",
       "2          Not on Display         2  \n",
       "3          Not on Display         2  \n",
       "4          Not on Display         2  \n",
       "...                   ...       ...  \n",
       "351367     Not on Display         1  \n",
       "351368     Not on Display         1  \n",
       "351369     Not on Display         1  \n",
       "351370     Not on Display         1  \n",
       "351371     Not on Display         1  \n",
       "\n",
       "[351372 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking and dropping duplicate rows\n",
    "promotion_df = promotion_df.drop_duplicates()\n",
    "\n",
    "promotion_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>code</th>\n",
       "      <th>amount</th>\n",
       "      <th>units</th>\n",
       "      <th>time</th>\n",
       "      <th>province</th>\n",
       "      <th>week</th>\n",
       "      <th>customerId</th>\n",
       "      <th>supermarket</th>\n",
       "      <th>basket</th>\n",
       "      <th>day</th>\n",
       "      <th>voucher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7680850106</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1100</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>125434</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3620000470</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1</td>\n",
       "      <td>1100</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>125434</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1800028064</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1</td>\n",
       "      <td>1137</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>108320</td>\n",
       "      <td>244</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9999985067</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1</td>\n",
       "      <td>1148</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>162016</td>\n",
       "      <td>244</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9999985131</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1</td>\n",
       "      <td>1323</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>89437</td>\n",
       "      <td>244</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048570</th>\n",
       "      <td>1510000011</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>43825</td>\n",
       "      <td>346</td>\n",
       "      <td>665448</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048571</th>\n",
       "      <td>3620000489</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>43825</td>\n",
       "      <td>346</td>\n",
       "      <td>665448</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048572</th>\n",
       "      <td>9999971309</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1410</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>42140</td>\n",
       "      <td>346</td>\n",
       "      <td>665449</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048573</th>\n",
       "      <td>3620000250</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2</td>\n",
       "      <td>1415</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>53544</td>\n",
       "      <td>346</td>\n",
       "      <td>665450</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048574</th>\n",
       "      <td>3620000350</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2</td>\n",
       "      <td>1415</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>53544</td>\n",
       "      <td>346</td>\n",
       "      <td>665450</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1048575 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               code  amount  units  time  province  week  customerId  \\\n",
       "0        7680850106    0.80      1  1100         2     1      125434   \n",
       "1        3620000470    3.59      1  1100         2     1      125434   \n",
       "2        1800028064    2.25      1  1137         2     1      108320   \n",
       "3        9999985067    0.85      1  1148         2     1      162016   \n",
       "4        9999985131    2.19      1  1323         2     1       89437   \n",
       "...             ...     ...    ...   ...       ...   ...         ...   \n",
       "1048570  1510000011    2.35      1  1307         2    27       43825   \n",
       "1048571  3620000489    0.99      1  1307         2    27       43825   \n",
       "1048572  9999971309    2.00      2  1410         2    27       42140   \n",
       "1048573  3620000250    1.98      2  1415         2    27       53544   \n",
       "1048574  3620000350    1.98      2  1415         2    27       53544   \n",
       "\n",
       "         supermarket  basket  day  voucher  \n",
       "0                244       1    1        0  \n",
       "1                244       1    1        0  \n",
       "2                244       2    1        0  \n",
       "3                244       3    1        0  \n",
       "4                244       4    1        0  \n",
       "...              ...     ...  ...      ...  \n",
       "1048570          346  665448  189        0  \n",
       "1048571          346  665448  189        0  \n",
       "1048572          346  665449  189        0  \n",
       "1048573          346  665450  189        0  \n",
       "1048574          346  665450  189        0  \n",
       "\n",
       "[1048575 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting data from csv file\n",
    "sales_df = pd.read_csv(r'Raw_datasets/sales.csv')\n",
    "\n",
    "sales_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>code</th>\n",
       "      <th>amount</th>\n",
       "      <th>units</th>\n",
       "      <th>time</th>\n",
       "      <th>province</th>\n",
       "      <th>week</th>\n",
       "      <th>customerId</th>\n",
       "      <th>supermarket</th>\n",
       "      <th>basket</th>\n",
       "      <th>day</th>\n",
       "      <th>voucher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7680850106</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1100</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>125434</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3620000470</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1</td>\n",
       "      <td>1100</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>125434</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1800028064</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1</td>\n",
       "      <td>1137</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>108320</td>\n",
       "      <td>244</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9999985067</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1</td>\n",
       "      <td>1148</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>162016</td>\n",
       "      <td>244</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9999985131</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1</td>\n",
       "      <td>1323</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>89437</td>\n",
       "      <td>244</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048570</th>\n",
       "      <td>1510000011</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>43825</td>\n",
       "      <td>346</td>\n",
       "      <td>665448</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048571</th>\n",
       "      <td>3620000489</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>43825</td>\n",
       "      <td>346</td>\n",
       "      <td>665448</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048572</th>\n",
       "      <td>9999971309</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1410</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>42140</td>\n",
       "      <td>346</td>\n",
       "      <td>665449</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048573</th>\n",
       "      <td>3620000250</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2</td>\n",
       "      <td>1415</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>53544</td>\n",
       "      <td>346</td>\n",
       "      <td>665450</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048574</th>\n",
       "      <td>3620000350</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2</td>\n",
       "      <td>1415</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>53544</td>\n",
       "      <td>346</td>\n",
       "      <td>665450</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1048575 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               code  amount  units  time  province  week  customerId  \\\n",
       "0        7680850106    0.80      1  1100         2     1      125434   \n",
       "1        3620000470    3.59      1  1100         2     1      125434   \n",
       "2        1800028064    2.25      1  1137         2     1      108320   \n",
       "3        9999985067    0.85      1  1148         2     1      162016   \n",
       "4        9999985131    2.19      1  1323         2     1       89437   \n",
       "...             ...     ...    ...   ...       ...   ...         ...   \n",
       "1048570  1510000011    2.35      1  1307         2    27       43825   \n",
       "1048571  3620000489    0.99      1  1307         2    27       43825   \n",
       "1048572  9999971309    2.00      2  1410         2    27       42140   \n",
       "1048573  3620000250    1.98      2  1415         2    27       53544   \n",
       "1048574  3620000350    1.98      2  1415         2    27       53544   \n",
       "\n",
       "         supermarket  basket  day  voucher  \n",
       "0                244       1    1        0  \n",
       "1                244       1    1        0  \n",
       "2                244       2    1        0  \n",
       "3                244       3    1        0  \n",
       "4                244       4    1        0  \n",
       "...              ...     ...  ...      ...  \n",
       "1048570          346  665448  189        0  \n",
       "1048571          346  665448  189        0  \n",
       "1048572          346  665449  189        0  \n",
       "1048573          346  665450  189        0  \n",
       "1048574          346  665450  189        0  \n",
       "\n",
       "[1048575 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking and dropping duplicate rows\n",
    "sales_df = sales_df.drop_duplicates()\n",
    "\n",
    "sales_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>supermarket_No</th>\n",
       "      <th>postal-code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>199</td>\n",
       "      <td>30319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>30134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201</td>\n",
       "      <td>30066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>31093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203</td>\n",
       "      <td>30542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>383</td>\n",
       "      <td>40502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>384</td>\n",
       "      <td>42367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>385</td>\n",
       "      <td>37160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>386</td>\n",
       "      <td>29803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>387</td>\n",
       "      <td>37748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>387 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     supermarket_No  postal-code\n",
       "0               199        30319\n",
       "1               200        30134\n",
       "2               201        30066\n",
       "3               202        31093\n",
       "4               203        30542\n",
       "..              ...          ...\n",
       "382             383        40502\n",
       "383             384        42367\n",
       "384             385        37160\n",
       "385             386        29803\n",
       "386             387        37748\n",
       "\n",
       "[387 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting data from csv file\n",
    "supermarkets_df = pd.read_csv(r'Raw_datasets\\supermarkets.csv')\n",
    "\n",
    "supermarkets_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>supermarket_No</th>\n",
       "      <th>postal-code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>199</td>\n",
       "      <td>30319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>30134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201</td>\n",
       "      <td>30066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>31093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203</td>\n",
       "      <td>30542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>383</td>\n",
       "      <td>40502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>384</td>\n",
       "      <td>42367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>385</td>\n",
       "      <td>37160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>386</td>\n",
       "      <td>29803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>387</td>\n",
       "      <td>37748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>387 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     supermarket_No  postal-code\n",
       "0               199        30319\n",
       "1               200        30134\n",
       "2               201        30066\n",
       "3               202        31093\n",
       "4               203        30542\n",
       "..              ...          ...\n",
       "382             383        40502\n",
       "383             384        42367\n",
       "384             385        37160\n",
       "385             386        29803\n",
       "386             387        37748\n",
       "\n",
       "[387 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking and dropping duplicate rows\n",
    "supermarkets_df = supermarkets_df.drop_duplicates()\n",
    "\n",
    "supermarkets_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 927 entries, 0 to 926\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   code        927 non-null    int64 \n",
      " 1   descrption  927 non-null    object\n",
      " 2   type        927 non-null    object\n",
      " 3   brand       927 non-null    object\n",
      " 4   size        927 non-null    object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 36.3+ KB\n"
     ]
    }
   ],
   "source": [
    "item_df.info()\n",
    "\n",
    "# No missing data in item_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 351372 entries, 0 to 351371\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   code          351372 non-null  int64 \n",
      " 1   supermarkets  351372 non-null  int64 \n",
      " 2   week          351372 non-null  int64 \n",
      " 3   feature       351372 non-null  object\n",
      " 4   display       351372 non-null  object\n",
      " 5   province      351372 non-null  int64 \n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 16.1+ MB\n"
     ]
    }
   ],
   "source": [
    "promotion_df.info()\n",
    "\n",
    "# No missing data in promotion_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1048575 entries, 0 to 1048574\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count    Dtype  \n",
      "---  ------       --------------    -----  \n",
      " 0   code         1048575 non-null  int64  \n",
      " 1   amount       1048575 non-null  float64\n",
      " 2   units        1048575 non-null  int64  \n",
      " 3   time         1048575 non-null  int64  \n",
      " 4   province     1048575 non-null  int64  \n",
      " 5   week         1048575 non-null  int64  \n",
      " 6   customerId   1048575 non-null  int64  \n",
      " 7   supermarket  1048575 non-null  int64  \n",
      " 8   basket       1048575 non-null  int64  \n",
      " 9   day          1048575 non-null  int64  \n",
      " 10  voucher      1048575 non-null  int64  \n",
      "dtypes: float64(1), int64(10)\n",
      "memory usage: 88.0 MB\n"
     ]
    }
   ],
   "source": [
    "sales_df.info()\n",
    "\n",
    "# No missing values in sales_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 387 entries, 0 to 386\n",
      "Data columns (total 2 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   supermarket_No  387 non-null    int64\n",
      " 1   postal-code     387 non-null    int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 6.2 KB\n"
     ]
    }
   ],
   "source": [
    "supermarkets_df.info()\n",
    "\n",
    "# No missing values in supermarkets_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for duplicates in key columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [code, descrption, type, brand, size]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# Checking for duplicates in the item_df \n",
    "# Check for duplicates in 'code' column\n",
    "item_duplicates = item_df[item_df['code'].duplicated()]\n",
    "print(item_duplicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], Name: count, dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of each value\n",
    "item_value_counts = item_df['code'].value_counts()\n",
    "\n",
    "# Filter values that appear more than once\n",
    "item_duplicates_no = item_value_counts[item_value_counts > 1]\n",
    "print(item_duplicates_no)\n",
    "\n",
    "# No duplicate data in the code column of item_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              code  supermarkets  week                  feature  \\\n",
      "5       2700042240           285    92    Interior Page Feature   \n",
      "9       2700042240           285    93           Not on Feature   \n",
      "46      3620001375             1    56  Interior Page Line Item   \n",
      "49      3620001369             1    56  Interior Page Line Item   \n",
      "52      3620001365             1    56  Interior Page Line Item   \n",
      "...            ...           ...   ...                      ...   \n",
      "351367  9999966721           387   101       Wrap Front Feature   \n",
      "351368  9999966720           387   101       Wrap Front Feature   \n",
      "351369  3620000507           387   104    Interior Page Feature   \n",
      "351370  3620000493           387   104    Interior Page Feature   \n",
      "351371  9999966715           387   101       Wrap Front Feature   \n",
      "\n",
      "                  display  province  \n",
      "5       Mid-Aisle End Cap         2  \n",
      "9       Mid-Aisle End Cap         2  \n",
      "46         Not on Display         1  \n",
      "49         Not on Display         1  \n",
      "52         Not on Display         1  \n",
      "...                   ...       ...  \n",
      "351367     Not on Display         1  \n",
      "351368     Not on Display         1  \n",
      "351369     Not on Display         1  \n",
      "351370     Not on Display         1  \n",
      "351371     Not on Display         1  \n",
      "\n",
      "[350893 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# Checking for duplicates in the promotion_df \n",
    "# Check for duplicates in 'code' column\n",
    "promotion_duplicates = promotion_df[promotion_df['code'].duplicated()]\n",
    "print(promotion_duplicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code\n",
      "3620000300    6615\n",
      "3620000250    5842\n",
      "3620000350    5399\n",
      "9999985052    3654\n",
      "3620000440    3379\n",
      "              ... \n",
      "3340006508       2\n",
      "2472200005       2\n",
      "7130000081       2\n",
      "4119606063       2\n",
      "9999968192       2\n",
      "Name: count, Length: 446, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of each value\n",
    "promotion_value_counts = promotion_df['code'].value_counts()\n",
    "\n",
    "# Filter values that appear more than once\n",
    "promotion_duplicates_no = promotion_value_counts[promotion_value_counts > 1]\n",
    "print(promotion_duplicates_no)\n",
    "\n",
    "# There are a lot of duplicate data in the code column of promotion_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               code  amount  units  time  province  week  customerId  \\\n",
      "17       3620000446    2.19      1  1754         2     1      108013   \n",
      "20       9999985067    0.85      1  1815         2     1       53674   \n",
      "29       9999985048    0.75      1  2050         2     1       83172   \n",
      "37       2700042239    0.99      1  1102         2     1      118654   \n",
      "42       2700042239    0.99      1  1627         2     1      122564   \n",
      "...             ...     ...    ...   ...       ...   ...         ...   \n",
      "1048570  1510000011    2.35      1  1307         2    27       43825   \n",
      "1048571  3620000489    0.99      1  1307         2    27       43825   \n",
      "1048572  9999971309    2.00      2  1410         2    27       42140   \n",
      "1048573  3620000250    1.98      2  1415         2    27       53544   \n",
      "1048574  3620000350    1.98      2  1415         2    27       53544   \n",
      "\n",
      "         supermarket  basket  day  voucher  \n",
      "17               244      11    1        0  \n",
      "20               244      12    1        0  \n",
      "29               244      16    1        0  \n",
      "37               365      19    1        0  \n",
      "42               365      24    1        0  \n",
      "...              ...     ...  ...      ...  \n",
      "1048570          346  665448  189        0  \n",
      "1048571          346  665448  189        0  \n",
      "1048572          346  665449  189        0  \n",
      "1048573          346  665450  189        0  \n",
      "1048574          346  665450  189        0  \n",
      "\n",
      "[1047793 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "# Checking for duplicates in the sales_df \n",
    "# Check for duplicates in 'code' column\n",
    "sales_duplicates = sales_df[sales_df['code'].duplicated()]\n",
    "print(sales_duplicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code\n",
      "9999985020    18009\n",
      "9999985004    17703\n",
      "3620000250    17238\n",
      "9999985068    14756\n",
      "9999985021    11118\n",
      "              ...  \n",
      "8532801991        2\n",
      "7140303303        2\n",
      "8939710379        2\n",
      "3620000661        2\n",
      "7914730119        2\n",
      "Name: count, Length: 737, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of each value\n",
    "sales_value_counts = sales_df['code'].value_counts()\n",
    "\n",
    "# Filter values that appear more than once\n",
    "sales_duplicates_no = sales_value_counts[sales_value_counts > 1]\n",
    "print(sales_duplicates_no)\n",
    "\n",
    "# There are a lot of duplicate data in the code column of sales_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               code  amount  units  time  province  week  customerId  \\\n",
      "1        3620000470    3.59      1  1100         2     1      125434   \n",
      "5        5100002794    2.19      1  1323         2     1       89437   \n",
      "7        9999985051    1.29      1  1415         2     1      158549   \n",
      "11       3340060177    1.50      1  1624         2     1        8283   \n",
      "12       4112907712    3.65      1  1624         2     1        8283   \n",
      "...             ...     ...    ...   ...       ...   ...         ...   \n",
      "1048569  3620000300    1.98      2  1107         2    27       41469   \n",
      "1048570  1510000011    2.35      1  1307         2    27       43825   \n",
      "1048571  3620000489    0.99      1  1307         2    27       43825   \n",
      "1048572  9999971309    2.00      2  1410         2    27       42140   \n",
      "1048574  3620000350    1.98      2  1415         2    27       53544   \n",
      "\n",
      "         supermarket  basket  day  voucher  \n",
      "1                244       1    1        0  \n",
      "5                244       4    1        0  \n",
      "7                244       5    1        0  \n",
      "11               244       8    1        0  \n",
      "12               244       8    1        0  \n",
      "...              ...     ...  ...      ...  \n",
      "1048569          346  665447  189        0  \n",
      "1048570          346  665448  189        0  \n",
      "1048571          346  665448  189        0  \n",
      "1048572          346  665449  189        0  \n",
      "1048574          346  665450  189        0  \n",
      "\n",
      "[798965 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "# Check for duplicates in 'code' column\n",
    "sales_duplicates_customerId = sales_df[sales_df['customerId'].duplicated()]\n",
    "print(sales_duplicates_customerId)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customerId\n",
      "369388    88\n",
      "293395    83\n",
      "265319    77\n",
      "56869     74\n",
      "28955     72\n",
      "          ..\n",
      "75786      2\n",
      "62199      2\n",
      "137050     2\n",
      "94880      2\n",
      "229933     2\n",
      "Name: count, Length: 179711, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of each value\n",
    "sales_value_counts_customerId = sales_df['customerId'].value_counts()\n",
    "\n",
    "# Filter values that appear more than once\n",
    "sales_duplicates_no_customerId = sales_value_counts_customerId[sales_value_counts_customerId > 1]\n",
    "print(sales_duplicates_no_customerId)\n",
    "\n",
    "# There are a lot of duplicate data in the customerId column of sales_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [supermarket_No, postal-code]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# Checking for duplicates in the supermarkets_df \n",
    "# Check for duplicates in 'supermarket_No' column\n",
    "supermarkets_duplicates = supermarkets_df[supermarkets_df['supermarket_No'].duplicated()]\n",
    "print(supermarkets_duplicates)\n",
    "\n",
    "# No duplicate data in the supermarket_No column of sales_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Primary keys to Sales and Promotion tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>sales_id</th>\n",
       "      <th>code</th>\n",
       "      <th>customerId</th>\n",
       "      <th>supermarket</th>\n",
       "      <th>units</th>\n",
       "      <th>province</th>\n",
       "      <th>amount</th>\n",
       "      <th>basket</th>\n",
       "      <th>voucher</th>\n",
       "      <th>week</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>7680850106</td>\n",
       "      <td>125434</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>3620000470</td>\n",
       "      <td>125434</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1800028064</td>\n",
       "      <td>108320</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>9999985067</td>\n",
       "      <td>162016</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>9999985131</td>\n",
       "      <td>89437</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.19</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1323</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>1048570</th>\n",
       "      <td>1048575</td>\n",
       "      <td>1510000011</td>\n",
       "      <td>43825</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.35</td>\n",
       "      <td>665448</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>189</td>\n",
       "      <td>1307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048571</th>\n",
       "      <td>1048576</td>\n",
       "      <td>3620000489</td>\n",
       "      <td>43825</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.99</td>\n",
       "      <td>665448</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>189</td>\n",
       "      <td>1307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048572</th>\n",
       "      <td>1048577</td>\n",
       "      <td>9999971309</td>\n",
       "      <td>42140</td>\n",
       "      <td>346</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>665449</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>189</td>\n",
       "      <td>1410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048573</th>\n",
       "      <td>1048578</td>\n",
       "      <td>3620000250</td>\n",
       "      <td>53544</td>\n",
       "      <td>346</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.98</td>\n",
       "      <td>665450</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>189</td>\n",
       "      <td>1415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048574</th>\n",
       "      <td>1048579</td>\n",
       "      <td>3620000350</td>\n",
       "      <td>53544</td>\n",
       "      <td>346</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.98</td>\n",
       "      <td>665450</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>189</td>\n",
       "      <td>1415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1048575 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         sales_id        code  customerId  supermarket  units  province  \\\n",
       "0               5  7680850106      125434          244      1         2   \n",
       "1               6  3620000470      125434          244      1         2   \n",
       "2               7  1800028064      108320          244      1         2   \n",
       "3               8  9999985067      162016          244      1         2   \n",
       "4               9  9999985131       89437          244      1         2   \n",
       "...           ...         ...         ...          ...    ...       ...   \n",
       "1048570   1048575  1510000011       43825          346      1         2   \n",
       "1048571   1048576  3620000489       43825          346      1         2   \n",
       "1048572   1048577  9999971309       42140          346      2         2   \n",
       "1048573   1048578  3620000250       53544          346      2         2   \n",
       "1048574   1048579  3620000350       53544          346      2         2   \n",
       "\n",
       "         amount  basket  voucher  week  day  time  \n",
       "0          0.80       1        0     1    1  1100  \n",
       "1          3.59       1        0     1    1  1100  \n",
       "2          2.25       2        0     1    1  1137  \n",
       "3          0.85       3        0     1    1  1148  \n",
       "4          2.19       4        0     1    1  1323  \n",
       "...         ...     ...      ...   ...  ...   ...  \n",
       "1048570    2.35  665448        0    27  189  1307  \n",
       "1048571    0.99  665448        0    27  189  1307  \n",
       "1048572    2.00  665449        0    27  189  1410  \n",
       "1048573    1.98  665450        0    27  189  1415  \n",
       "1048574    1.98  665450        0    27  189  1415  \n",
       "\n",
       "[1048575 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Sales Table, assigning a unique primary key called sales_id using a dedicated numbering system\n",
    "sales_df['sales_id'] = range(5, len(sales_df) + 5)  # Sequential numbering starting at 5\n",
    "\n",
    "# # Correcting the supermarket column name\n",
    "# sales_df['supermarket_no'] = sales_df['supermarket']\n",
    "\n",
    "sales_df = sales_df[['sales_id', 'code', 'customerId', 'supermarket', 'units', 'province', 'amount', 'basket', 'voucher', 'week', 'day', 'time']].copy().drop_duplicates().reset_index(drop = True)\n",
    "\n",
    "\n",
    "sales_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>promotion_id</th>\n",
       "      <th>code</th>\n",
       "      <th>supermarkets</th>\n",
       "      <th>feature</th>\n",
       "      <th>display</th>\n",
       "      <th>province</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>2700042240</td>\n",
       "      <td>285</td>\n",
       "      <td>Not on Feature</td>\n",
       "      <td>Mid-Aisle End Cap</td>\n",
       "      <td>2</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>2700042292</td>\n",
       "      <td>285</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>2700042274</td>\n",
       "      <td>285</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>2700042273</td>\n",
       "      <td>285</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>2700042254</td>\n",
       "      <td>285</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>2</td>\n",
       "      <td>92</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351367</th>\n",
       "      <td>351400</td>\n",
       "      <td>9999966721</td>\n",
       "      <td>387</td>\n",
       "      <td>Wrap Front Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351368</th>\n",
       "      <td>351401</td>\n",
       "      <td>9999966720</td>\n",
       "      <td>387</td>\n",
       "      <td>Wrap Front Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351369</th>\n",
       "      <td>351402</td>\n",
       "      <td>3620000507</td>\n",
       "      <td>387</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351370</th>\n",
       "      <td>351403</td>\n",
       "      <td>3620000493</td>\n",
       "      <td>387</td>\n",
       "      <td>Interior Page Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351371</th>\n",
       "      <td>351404</td>\n",
       "      <td>9999966715</td>\n",
       "      <td>387</td>\n",
       "      <td>Wrap Front Feature</td>\n",
       "      <td>Not on Display</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351372 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        promotion_id        code  supermarkets                feature  \\\n",
       "0                 33  2700042240           285         Not on Feature   \n",
       "1                 34  2700042292           285  Interior Page Feature   \n",
       "2                 35  2700042274           285  Interior Page Feature   \n",
       "3                 36  2700042273           285  Interior Page Feature   \n",
       "4                 37  2700042254           285  Interior Page Feature   \n",
       "...              ...         ...           ...                    ...   \n",
       "351367        351400  9999966721           387     Wrap Front Feature   \n",
       "351368        351401  9999966720           387     Wrap Front Feature   \n",
       "351369        351402  3620000507           387  Interior Page Feature   \n",
       "351370        351403  3620000493           387  Interior Page Feature   \n",
       "351371        351404  9999966715           387     Wrap Front Feature   \n",
       "\n",
       "                  display  province  week  \n",
       "0       Mid-Aisle End Cap         2    91  \n",
       "1          Not on Display         2    92  \n",
       "2          Not on Display         2    92  \n",
       "3          Not on Display         2    92  \n",
       "4          Not on Display         2    92  \n",
       "...                   ...       ...   ...  \n",
       "351367     Not on Display         1   101  \n",
       "351368     Not on Display         1   101  \n",
       "351369     Not on Display         1   104  \n",
       "351370     Not on Display         1   104  \n",
       "351371     Not on Display         1   101  \n",
       "\n",
       "[351372 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Promotion Table, assigning a unique primary key called promotion_id using a dedicated numbering system\n",
    "promotion_df['promotion_id'] = range(33, len(promotion_df) + 33)  # Sequential numbering starting at 33\n",
    "\n",
    "\n",
    "# # Correcting the supermarket column name\n",
    "# promotion_df['supermarket_no'] = promotion_df['supermarkets']\n",
    "\n",
    "promotion_df = promotion_df[['promotion_id', 'code', 'supermarkets', 'feature', 'display', 'province', 'week']].copy().drop_duplicates().reset_index(drop = True)\n",
    "\n",
    "\n",
    "promotion_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the tables to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_df.to_csv('Item_table.csv', index=False)\n",
    "promotion_df.to_csv('Promotion_table.csv', index=False)\n",
    "sales_df.to_csv('Sales_table.csv', index=False)\n",
    "supermarkets_df.to_csv('Supermarkets_table.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data into a database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load environment variables from the .env file\n",
    "load_dotenv()\n",
    "\n",
    "db_params = {\n",
    "    'username' : os.getenv('DB_USER'),\n",
    "    'password' : os.getenv('PASSWORD'),\n",
    "    'host' : os.getenv('HOST'),\n",
    "    'port': int(os.getenv('PORT')),  # Ensure PORT is an integer\n",
    "    'database' : os.getenv('DATABASE')\n",
    "}\n",
    "\n",
    "# define the database connection url\n",
    "db_url = f\"postgresql://{db_params['username']}:{db_params['password']}@{db_params['host']}:{db_params['port']}/{db_params['database']}\"\n",
    "\n",
    "\n",
    "# create the database engine with the db url specified\n",
    "engine = create_engine(db_url)\n",
    "\n",
    "# connect to postgreSQL server\n",
    "with engine.connect() as connection:\n",
    "    # Create table and load the data\n",
    "    item_df.to_sql('Item_table', connection, index = False, if_exists = 'replace')\n",
    "    promotion_df.to_sql('Promotion_table', connection, index = False, if_exists = 'replace')\n",
    "    sales_df.to_sql('Sales_table', connection, index = False, if_exists = 'replace')\n",
    "    supermarkets_df.to_sql('Supermarkets_table', connection, index = False, if_exists = 'replace')\n",
    "\n",
    "print('Database, table and data loaded successfully')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Conducting Business Analysis and Deriving Effective Business Solutions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Business-Valued Solutions use **Data Analytics** and **Machine Learning** to turn raw data into actionable insights, helping businesses optimize operations, improve customer experience, and boost profits. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Business analysis 1:** \n",
    "\n",
    "In this section, we analyze supermarket transaction data to uncover branch-level sales patterns, regional trends, and performance benchmarks. These insights guide strategic decisions for growth and efficiency.\n",
    "\n",
    "This code analyzes **branch-based sales patterns** by grouping data at the branch (`supermarket`) and regional (`province`) levels. It calculates:\n",
    "\n",
    "1. **`Total_Sales`**: Sum of `amount` (revenue).\n",
    "2. **`Total_Units`**: Sum of `units` (items sold).\n",
    "\n",
    " **Insights:**\n",
    "1. **High Performers**: Identifies supermarkets (e.g., supermarket `2` in Province `1`) generating the most revenue or sales volume.\n",
    "2. **Underperformers**: Highlights supermarkets with low sales (e.g., supermarket `384` in Province `2`) for targeted improvements.\n",
    "3. **Regional Trends**: Reveals provinces with higher sales, guiding resource allocation and strategic focus.\n",
    "4. **Resource Allocation**: Helps distribute inventory or staff efficiently across supermarkets based on demand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch Sales Patterns:\n",
      "                       Total_Sales  Total_Units\n",
      "supermarket province                          \n",
      "1           1             4989.59         3506\n",
      "2           1             5024.30         3476\n",
      "3           1             5513.87         3777\n",
      "4           1             3678.02         2531\n",
      "5           1             7550.35         5207\n",
      "...                           ...          ...\n",
      "381         2             6514.70         4562\n",
      "382         2             2633.90         1927\n",
      "383         2             5982.47         3732\n",
      "384         2             2039.36         1356\n",
      "385         2             2163.84         1551\n",
      "\n",
      "[376 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Branch-Based Sales Patterns\n",
    "# Analyze sales per supermarket and province\n",
    "def branch_sales_patterns(sales_df):\n",
    "    branch_sales = sales_df.groupby(['supermarket', 'province']).agg(\n",
    "        Total_Sales=('amount', 'sum'),\n",
    "        Total_Units=('units', 'sum')\n",
    "    )\n",
    "    print(\"Branch Sales Patterns:\\n\", branch_sales)\n",
    "    return branch_sales\n",
    "\n",
    "\n",
    "branch_sales = branch_sales_patterns(sales_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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></th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Total_Units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>supermarket</th>\n",
       "      <th>province</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <th>1</th>\n",
       "      <td>687.06</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <th>2</th>\n",
       "      <td>950.01</td>\n",
       "      <td>648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <th>1</th>\n",
       "      <td>935.37</td>\n",
       "      <td>659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <th>2</th>\n",
       "      <td>1175.25</td>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <th>1</th>\n",
       "      <td>1040.59</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <th>2</th>\n",
       "      <td>12259.84</td>\n",
       "      <td>7752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <th>2</th>\n",
       "      <td>11787.82</td>\n",
       "      <td>7797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <th>2</th>\n",
       "      <td>13012.65</td>\n",
       "      <td>8020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <th>2</th>\n",
       "      <td>13132.53</td>\n",
       "      <td>8098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <th>1</th>\n",
       "      <td>12111.45</td>\n",
       "      <td>8234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>376 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Total_Sales  Total_Units\n",
       "supermarket province                          \n",
       "13          1              687.06          553\n",
       "299         2              950.01          648\n",
       "120         1              935.37          659\n",
       "370         2             1175.25          715\n",
       "178         1             1040.59          803\n",
       "...                           ...          ...\n",
       "326         2            12259.84         7752\n",
       "371         2            11787.82         7797\n",
       "359         2            13012.65         8020\n",
       "270         2            13132.53         8098\n",
       "71          1            12111.45         8234\n",
       "\n",
       "[376 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting the DataFrames in ascending order \n",
    "branch_sales = branch_sales.sort_values(by='Total_Sales', ascending=True)\n",
    "branch_sales = branch_sales.sort_values(by='Total_Units', ascending=True)\n",
    "\n",
    "branch_sales\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Actionable insights**\n",
    "**1. Top-Performing Branches:**\n",
    "Identify branches with the highest Total_Sales and Total_Units.\n",
    "supermarket 71 in Province 1 stands out with the highest sales and units sold (12111.45 sales, 8234 units).\n",
    "\n",
    "**Actionable Insight:** Invest in expanding high-performing branches or replicating their strategies across other locations.\n",
    "\n",
    "**2. Underperforming Branches:**\n",
    "Pinpoint branches with low sales and units.\n",
    "supermarket 13 in Province 1 has one of the lowest performances (687.06\tsales, 553 units).\n",
    "\n",
    "**Actionable Insight:** Investigate causes (e.g., location, promotions, or competition) and develop strategies to improve performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the result\n",
    "\n",
    "# Set plot style\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# using Bar Plot: Total Sales by Branch and Province\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(data=branch_sales, x=\"supermarket\", y=\"Total_Sales\", hue=\"province\", palette=\"viridis\")\n",
    "plt.title(\"Total Sales by Supermarket and Province\", fontsize=14)\n",
    "plt.xlabel(\"Supermarket\")\n",
    "plt.ylabel(\"Total Sales\")\n",
    "plt.legend(title=\"Province\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Business analysis 2:** \n",
    "This next section analyzes **promotion effectiveness** by evaluating the impact of features and displays on promotions at the provincial level.\n",
    "\n",
    " **What the Code Does:**\n",
    "1. **Binary Conversion**: Converts `feature` and `display` columns to `1` if present and `0` otherwise.\n",
    "2. **Grouping**: Groups data by `feature` and `province` to aggregate metrics.\n",
    "3. **Calculates Metrics**:\n",
    "   - **`Display_Count`**: Total displays in each group.\n",
    "   - **`Week_Count`**: Total weeks with promotions.\n",
    "   - **`Effectiveness`**: Ratio of displays to weeks (`Display_Count / Week_Count`).\n",
    "\n",
    " **Business Insights:**\n",
    "1. **Consistent Usage**: Provinces `1` and `2` use displays consistently for promotions (`Effectiveness = 1.0`).\n",
    "2. **Regional Strategies**: Highlights how each province utilizes promotions, guiding improvements where needed.\n",
    "3. **Resource Allocation**: Insights help distribute marketing budgets and resources effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promotion Effectiveness:\n",
      "                   Display_Count  Week_Count  Effectiveness\n",
      "feature province                                          \n",
      "1       1                207193      207193            1.0\n",
      "        2                144179      144179            1.0\n"
     ]
    }
   ],
   "source": [
    "# Promotion Effectiveness\n",
    "# Convert 'feature' column to binary: 1 if there's a feature, 0 otherwise\n",
    "promotion_df['feature'] = promotion_df['feature'].apply(lambda x: 1 if pd.notnull(x) and x != 0 else 0)\n",
    "\n",
    "# Convert 'display' column to binary: 1 if there's a display, 0 otherwise\n",
    "promotion_df['display'] = promotion_df['display'].apply(lambda x: 1 if pd.notnull(x) and x != 0 else 0)\n",
    "\n",
    "promotion_df\n",
    "\n",
    "\n",
    "\n",
    "# Evaluate which promotions are most effective\n",
    "def promotion_effectiveness(promotion_df):\n",
    "    promotion_analysis = promotion_df.groupby(['feature', 'province']).agg(\n",
    "        Display_Count=('display', 'sum'),\n",
    "        Week_Count=('week', 'count')\n",
    "    )\n",
    "    promotion_analysis['Effectiveness'] = promotion_analysis['Display_Count'] / promotion_analysis['Week_Count']\n",
    "    print(\"Promotion Effectiveness:\\n\", promotion_analysis)\n",
    "    return promotion_analysis\n",
    "\n",
    "promotion_analysis = promotion_effectiveness(promotion_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the result\n",
    "\n",
    "# Reset the index for easy plotting\n",
    "promotion_analysis = promotion_analysis.reset_index()\n",
    "\n",
    "# Heatmap: Visualizing Display Count Across Features and Provinces\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# Use pivot with keyword arguments\n",
    "heatmap_data = promotion_analysis.pivot(index='feature', columns='province', values='Display_Count')\n",
    "\n",
    "sns.heatmap(\n",
    "    heatmap_data,\n",
    "    annot=True,\n",
    "    fmt='d',\n",
    "    cmap='coolwarm',\n",
    "    cbar_kws={'label': 'Display Count'}\n",
    ")\n",
    "plt.title('Display Count Heatmap by Province and Feature')\n",
    "plt.xlabel('Province')\n",
    "plt.ylabel('Feature')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Actionable insights**\n",
    "\n",
    "From the given promotion effectiveness data, the following insights can be derived:\n",
    "\n",
    "1. **Consistent Display Utilization**:\n",
    "   - For **feature = 1**, both provinces (1 and 2) used displays consistently every week (`Effectiveness = 1.0`).\n",
    "   - This suggests that displays were well-coordinated with promotional efforts and fully utilized during the measured period.\n",
    "\n",
    "2. **High Display Count**:\n",
    "   - **Province 1** had a total of **207,193 displays**, while **Province 2** had **144,179 displays** for promotions with features.\n",
    "   - Province 1 used significantly more displays, indicating either a larger market size or higher promotional activity compared to Province 2.\n",
    "\n",
    "3. **Potential Resource Allocation Differences**:\n",
    "   - The difference in the display count suggests that Province 1 might have greater marketing resources or higher demand for promotional campaigns than Province 2.\n",
    "   - It could also point to differences in customer behavior or regional marketing strategies.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".myenv_Data_Engineering_Pretest",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}