Data Preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "import re\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading purchase behaviour and transaction datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "purchase = pd.read_csv('Purchase_Behaviour.csv')\n",
    "trans = pd.read_excel('Transaction_data.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Transaction Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43390</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43599</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>CCs Nacho Cheese    175g</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43605</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43329</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43330</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>43604</td>\n",
       "      <td>4</td>\n",
       "      <td>4074</td>\n",
       "      <td>2982</td>\n",
       "      <td>57</td>\n",
       "      <td>Old El Paso Salsa   Dip Tomato Mild 300g</td>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>43601</td>\n",
       "      <td>4</td>\n",
       "      <td>4149</td>\n",
       "      <td>3333</td>\n",
       "      <td>16</td>\n",
       "      <td>Smiths Crinkle Chips Salt &amp; Vinegar 330g</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43601</td>\n",
       "      <td>4</td>\n",
       "      <td>4196</td>\n",
       "      <td>3539</td>\n",
       "      <td>24</td>\n",
       "      <td>Grain Waves         Sweet Chilli 210g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>43332</td>\n",
       "      <td>5</td>\n",
       "      <td>5026</td>\n",
       "      <td>4525</td>\n",
       "      <td>42</td>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno 150g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43330</td>\n",
       "      <td>7</td>\n",
       "      <td>7150</td>\n",
       "      <td>6900</td>\n",
       "      <td>52</td>\n",
       "      <td>Grain Waves Sour    Cream&amp;Chives 210G</td>\n",
       "      <td>2</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0  43390          1            1000       1         5   \n",
       "1  43599          1            1307     348        66   \n",
       "2  43605          1            1343     383        61   \n",
       "3  43329          2            2373     974        69   \n",
       "4  43330          2            2426    1038       108   \n",
       "5  43604          4            4074    2982        57   \n",
       "6  43601          4            4149    3333        16   \n",
       "7  43601          4            4196    3539        24   \n",
       "8  43332          5            5026    4525        42   \n",
       "9  43330          7            7150    6900        52   \n",
       "\n",
       "                                  PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "0    Natural Chip        Compny SeaSalt175g         2        6.0  \n",
       "1                  CCs Nacho Cheese    175g         3        6.3  \n",
       "2    Smiths Crinkle Cut  Chips Chicken 170g         2        2.9  \n",
       "3    Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0  \n",
       "4  Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8  \n",
       "5  Old El Paso Salsa   Dip Tomato Mild 300g         1        5.1  \n",
       "6  Smiths Crinkle Chips Salt & Vinegar 330g         1        5.7  \n",
       "7     Grain Waves         Sweet Chilli 210g         1        3.6  \n",
       "8   Doritos Corn Chip Mexican Jalapeno 150g         1        3.9  \n",
       "9     Grain Waves Sour    Cream&Chives 210G         2        7.2  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 264836 entries, 0 to 264835\n",
      "Data columns (total 8 columns):\n",
      "DATE              264836 non-null int64\n",
      "STORE_NBR         264836 non-null int64\n",
      "LYLTY_CARD_NBR    264836 non-null int64\n",
      "TXN_ID            264836 non-null int64\n",
      "PROD_NBR          264836 non-null int64\n",
      "PROD_NAME         264836 non-null object\n",
      "PROD_QTY          264836 non-null int64\n",
      "TOT_SALES         264836 non-null float64\n",
      "dtypes: float64(1), int64(6), object(1)\n",
      "memory usage: 16.2+ MB\n"
     ]
    }
   ],
   "source": [
    "trans.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>The date column is in an integer format. We need to change this to a date format.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a datetime column from DATE column\n",
    "\n",
    "trans['date_modified'] = pd.to_datetime(trans['DATE'] , origin='1899-12-30', unit='d').dt.to_pydatetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_id_dedupped = trans['TXN_ID'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total TXN_IDs before dropping duplicates : 264836\n",
      "Total TXN_IDs after dropping duplicates : 263127\n"
     ]
    }
   ],
   "source": [
    "print('Total TXN_IDs before dropping duplicates : {}'.format(trans.shape[0]))\n",
    "print('Total TXN_IDs after dropping duplicates : {}'.format(trans_id_dedupped.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quantity of products sold at the chain of stores\n",
    "\n",
    "products = trans['PROD_NAME'].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "products.rename(columns={'index' : 'PROD_NAME', 'PROD_NAME' : 'Qty'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kettle Mozzarella   Basil &amp; Pesto 175g</td>\n",
       "      <td>3304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
       "      <td>3296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cobs Popd Swt/Chlli &amp;Sr/Cream Chips 110g</td>\n",
       "      <td>3269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tyrrells Crisps     Ched &amp; Chives 165g</td>\n",
       "      <td>3268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cobs Popd Sea Salt  Chips 110g</td>\n",
       "      <td>3265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kettle 135g Swt Pot Sea Salt</td>\n",
       "      <td>3257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tostitos Splash Of  Lime 175g</td>\n",
       "      <td>3252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Infuzions Thai SweetChili PotatoMix 110g</td>\n",
       "      <td>3242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Smiths Crnkle Chip  Orgnl Big Bag 380g</td>\n",
       "      <td>3233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Thins Potato Chips  Hot &amp; Spicy 175g</td>\n",
       "      <td>3229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kettle Sensations   Camembert &amp; Fig 150g</td>\n",
       "      <td>3219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Doritos Corn Chips  Cheese Supreme 170g</td>\n",
       "      <td>3217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pringles Barbeque   134g</td>\n",
       "      <td>3210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno 150g</td>\n",
       "      <td>3204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kettle Sweet Chilli And Sour Cream 175g</td>\n",
       "      <td>3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Smiths Crinkle Chips Salt &amp; Vinegar 330g</td>\n",
       "      <td>3197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Thins Chips Light&amp;  Tangy 175g</td>\n",
       "      <td>3188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>3185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pringles Sweet&amp;Spcy BBQ 134g</td>\n",
       "      <td>3177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Tyrrells Crisps     Lightly Salted 165g</td>\n",
       "      <td>3174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Infuzions BBQ Rib   Prawn Crackers 110g</td>\n",
       "      <td>3174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kettle Sea Salt     And Vinegar 175g</td>\n",
       "      <td>3173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Doritos Corn Chip Southern Chicken 150g</td>\n",
       "      <td>3172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Twisties Chicken270g</td>\n",
       "      <td>3170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Twisties Cheese     Burger 250g</td>\n",
       "      <td>3169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Grain Waves         Sweet Chilli 210g</td>\n",
       "      <td>3167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Pringles SourCream  Onion 134g</td>\n",
       "      <td>3162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Doritos Corn Chips  Nacho Cheese 170g</td>\n",
       "      <td>3160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cobs Popd Sour Crm  &amp;Chives Chips 110g</td>\n",
       "      <td>3159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Kettle Original 175g</td>\n",
       "      <td>3159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Cheetos Chs &amp; Bacon Balls 190g</td>\n",
       "      <td>1479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>RRD Salt &amp; Vinegar  165g</td>\n",
       "      <td>1474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>RRD Lime &amp; Pepper   165g</td>\n",
       "      <td>1473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
       "      <td>1473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Doritos Salsa Mild  300g</td>\n",
       "      <td>1472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Smiths Crinkle Cut  Tomato Salsa 150g</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>WW D/Style Chip     Sea Salt 200g</td>\n",
       "      <td>1469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>1468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>GrnWves Plus Btroot &amp; Chilli Jam 180g</td>\n",
       "      <td>1468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>WW Crinkle Cut      Chicken 175g</td>\n",
       "      <td>1467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Smiths Crinkle Cut  Chips Original 170g</td>\n",
       "      <td>1461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Smiths Thinly       Swt Chli&amp;S/Cream175G</td>\n",
       "      <td>1461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Natural ChipCo      Hony Soy Chckn175g</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Red Rock Deli SR    Salsa &amp; Mzzrlla 150g</td>\n",
       "      <td>1458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Smiths Crinkle Cut  Salt &amp; Vinegar 170g</td>\n",
       "      <td>1455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>RRD Steak &amp;         Chimuchurri 150g</td>\n",
       "      <td>1455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Cheezels Cheese Box 125g</td>\n",
       "      <td>1454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Smith Crinkle Cut   Bolognese 150g</td>\n",
       "      <td>1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Doritos Salsa       Medium 300g</td>\n",
       "      <td>1449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Cheetos Puffs 165g</td>\n",
       "      <td>1448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Thins Chips         Originl saltd 175g</td>\n",
       "      <td>1441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Smiths Chip Thinly  CutSalt/Vinegr175g</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Smiths Crinkle Cut  French OnionDip 150g</td>\n",
       "      <td>1438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Red Rock Deli Chikn&amp;Garlic Aioli 150g</td>\n",
       "      <td>1434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Sunbites Whlegrn    Crisps Frch/Onin 90g</td>\n",
       "      <td>1432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>RRD Pc Sea Salt     165g</td>\n",
       "      <td>1431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Woolworths Medium   Salsa 300g</td>\n",
       "      <td>1430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>NCC Sour Cream &amp;    Garden Chives 175g</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>French Fries Potato Chips 175g</td>\n",
       "      <td>1418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>WW Crinkle Cut      Original 175g</td>\n",
       "      <td>1410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    PROD_NAME   Qty\n",
       "0      Kettle Mozzarella   Basil & Pesto 175g  3304\n",
       "1    Kettle Tortilla ChpsHny&Jlpno Chili 150g  3296\n",
       "2    Cobs Popd Swt/Chlli &Sr/Cream Chips 110g  3269\n",
       "3      Tyrrells Crisps     Ched & Chives 165g  3268\n",
       "4              Cobs Popd Sea Salt  Chips 110g  3265\n",
       "5                Kettle 135g Swt Pot Sea Salt  3257\n",
       "6               Tostitos Splash Of  Lime 175g  3252\n",
       "7    Infuzions Thai SweetChili PotatoMix 110g  3242\n",
       "8      Smiths Crnkle Chip  Orgnl Big Bag 380g  3233\n",
       "9        Thins Potato Chips  Hot & Spicy 175g  3229\n",
       "10   Kettle Sensations   Camembert & Fig 150g  3219\n",
       "11    Doritos Corn Chips  Cheese Supreme 170g  3217\n",
       "12                   Pringles Barbeque   134g  3210\n",
       "13    Doritos Corn Chip Mexican Jalapeno 150g  3204\n",
       "14    Kettle Sweet Chilli And Sour Cream 175g  3200\n",
       "15   Smiths Crinkle Chips Salt & Vinegar 330g  3197\n",
       "16             Thins Chips Light&  Tangy 175g  3188\n",
       "17           Dorito Corn Chp     Supreme 380g  3185\n",
       "18               Pringles Sweet&Spcy BBQ 134g  3177\n",
       "19    Tyrrells Crisps     Lightly Salted 165g  3174\n",
       "20    Infuzions BBQ Rib   Prawn Crackers 110g  3174\n",
       "21       Kettle Sea Salt     And Vinegar 175g  3173\n",
       "22    Doritos Corn Chip Southern Chicken 150g  3172\n",
       "23                       Twisties Chicken270g  3170\n",
       "24            Twisties Cheese     Burger 250g  3169\n",
       "25      Grain Waves         Sweet Chilli 210g  3167\n",
       "26             Pringles SourCream  Onion 134g  3162\n",
       "27      Doritos Corn Chips  Nacho Cheese 170g  3160\n",
       "28     Cobs Popd Sour Crm  &Chives Chips 110g  3159\n",
       "29                       Kettle Original 175g  3159\n",
       "..                                        ...   ...\n",
       "84             Cheetos Chs & Bacon Balls 190g  1479\n",
       "85                   RRD Salt & Vinegar  165g  1474\n",
       "86                   RRD Lime & Pepper   165g  1473\n",
       "87     Smiths Chip Thinly  S/Cream&Onion 175g  1473\n",
       "88                   Doritos Salsa Mild  300g  1472\n",
       "89      Smiths Crinkle Cut  Tomato Salsa 150g  1470\n",
       "90          WW D/Style Chip     Sea Salt 200g  1469\n",
       "91     Natural Chip        Compny SeaSalt175g  1468\n",
       "92      GrnWves Plus Btroot & Chilli Jam 180g  1468\n",
       "93           WW Crinkle Cut      Chicken 175g  1467\n",
       "94    Smiths Crinkle Cut  Chips Original 170g  1461\n",
       "95   Smiths Thinly       Swt Chli&S/Cream175G  1461\n",
       "96     Natural ChipCo      Hony Soy Chckn175g  1460\n",
       "97   Red Rock Deli SR    Salsa & Mzzrlla 150g  1458\n",
       "98    Smiths Crinkle Cut  Salt & Vinegar 170g  1455\n",
       "99       RRD Steak &         Chimuchurri 150g  1455\n",
       "100                  Cheezels Cheese Box 125g  1454\n",
       "101        Smith Crinkle Cut   Bolognese 150g  1451\n",
       "102           Doritos Salsa       Medium 300g  1449\n",
       "103                        Cheetos Puffs 165g  1448\n",
       "104    Thins Chips         Originl saltd 175g  1441\n",
       "105    Smiths Chip Thinly  CutSalt/Vinegr175g  1440\n",
       "106  Smiths Crinkle Cut  French OnionDip 150g  1438\n",
       "107     Red Rock Deli Chikn&Garlic Aioli 150g  1434\n",
       "108  Sunbites Whlegrn    Crisps Frch/Onin 90g  1432\n",
       "109                  RRD Pc Sea Salt     165g  1431\n",
       "110            Woolworths Medium   Salsa 300g  1430\n",
       "111    NCC Sour Cream &    Garden Chives 175g  1419\n",
       "112            French Fries Potato Chips 175g  1418\n",
       "113         WW Crinkle Cut      Original 175g  1410\n",
       "\n",
       "[114 rows x 2 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for null values in the transaction dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE              0\n",
       "STORE_NBR         0\n",
       "LYLTY_CARD_NBR    0\n",
       "TXN_ID            0\n",
       "PROD_NBR          0\n",
       "PROD_NAME         0\n",
       "PROD_QTY          0\n",
       "TOT_SALES         0\n",
       "date_modified     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null values in the dataset !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Statistics of the metric columns in the transaction dataset</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>264836.000000</td>\n",
       "      <td>264836.00000</td>\n",
       "      <td>2.648360e+05</td>\n",
       "      <td>2.648360e+05</td>\n",
       "      <td>264836.000000</td>\n",
       "      <td>264836.000000</td>\n",
       "      <td>264836.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43464.036260</td>\n",
       "      <td>135.08011</td>\n",
       "      <td>1.355495e+05</td>\n",
       "      <td>1.351583e+05</td>\n",
       "      <td>56.583157</td>\n",
       "      <td>1.907309</td>\n",
       "      <td>7.304200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105.389282</td>\n",
       "      <td>76.78418</td>\n",
       "      <td>8.057998e+04</td>\n",
       "      <td>7.813303e+04</td>\n",
       "      <td>32.826638</td>\n",
       "      <td>0.643654</td>\n",
       "      <td>3.083226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>43282.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>43373.000000</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>7.002100e+04</td>\n",
       "      <td>6.760150e+04</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>43464.000000</td>\n",
       "      <td>130.00000</td>\n",
       "      <td>1.303575e+05</td>\n",
       "      <td>1.351375e+05</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43555.000000</td>\n",
       "      <td>203.00000</td>\n",
       "      <td>2.030942e+05</td>\n",
       "      <td>2.027012e+05</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>43646.000000</td>\n",
       "      <td>272.00000</td>\n",
       "      <td>2.373711e+06</td>\n",
       "      <td>2.415841e+06</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>650.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                DATE     STORE_NBR  LYLTY_CARD_NBR        TXN_ID  \\\n",
       "count  264836.000000  264836.00000    2.648360e+05  2.648360e+05   \n",
       "mean    43464.036260     135.08011    1.355495e+05  1.351583e+05   \n",
       "std       105.389282      76.78418    8.057998e+04  7.813303e+04   \n",
       "min     43282.000000       1.00000    1.000000e+03  1.000000e+00   \n",
       "25%     43373.000000      70.00000    7.002100e+04  6.760150e+04   \n",
       "50%     43464.000000     130.00000    1.303575e+05  1.351375e+05   \n",
       "75%     43555.000000     203.00000    2.030942e+05  2.027012e+05   \n",
       "max     43646.000000     272.00000    2.373711e+06  2.415841e+06   \n",
       "\n",
       "            PROD_NBR       PROD_QTY      TOT_SALES  \n",
       "count  264836.000000  264836.000000  264836.000000  \n",
       "mean       56.583157       1.907309       7.304200  \n",
       "std        32.826638       0.643654       3.083226  \n",
       "min         1.000000       1.000000       1.500000  \n",
       "25%        28.000000       2.000000       5.400000  \n",
       "50%        56.000000       2.000000       7.400000  \n",
       "75%        85.000000       2.000000       9.200000  \n",
       "max       114.000000     200.000000     650.000000  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above statistics we find an <b>outlier</b> in the <b>PROD_QTY</b> column. We see the max PROD_QTY value is 200 while till 75% percentile the value is 2. We need to investigate this transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>date_modified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69762</th>\n",
       "      <td>43331</td>\n",
       "      <td>226</td>\n",
       "      <td>226000</td>\n",
       "      <td>226201</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>200</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2018-08-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69763</th>\n",
       "      <td>43605</td>\n",
       "      <td>226</td>\n",
       "      <td>226000</td>\n",
       "      <td>226210</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>200</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2019-05-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "69762  43331        226          226000  226201         4   \n",
       "69763  43605        226          226000  226210         4   \n",
       "\n",
       "                              PROD_NAME  PROD_QTY  TOT_SALES date_modified  \n",
       "69762  Dorito Corn Chp     Supreme 380g       200      650.0    2018-08-19  \n",
       "69763  Dorito Corn Chp     Supreme 380g       200      650.0    2019-05-20  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter transactions where PROD_QTY = 200\n",
    "\n",
    "trans[trans['PROD_QTY']==200]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find that one customer have done two transactions of purchase of Dorito Corn Chp Supreme 380 g with PROD_QTY 200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purchase transactions donot seem to be ordinary retail transaction. The customer may have purchased the product in bulk amount for commercial purposes, hence we will drop this customer records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetching the index in dataframe for that customer 226000\n",
    "# Index to drop from dataframe\n",
    "\n",
    "cust_to_drop = trans[trans['LYLTY_CARD_NBR']==226000].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans.drop(cust_to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Number of transactions happened over time</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_over_time = trans[['date_modified']].groupby(['date_modified']).size().reset_index().sort_values(['date_modified'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_over_time.rename(columns={0:'Num_Transactions'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_modified</th>\n",
       "      <th>Num_Transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-02</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-07-03</td>\n",
       "      <td>722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-07-06</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-07-07</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-07-08</td>\n",
       "      <td>696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-07-09</td>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-07-10</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-07-11</td>\n",
       "      <td>752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-07-12</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-07-14</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-07-15</td>\n",
       "      <td>758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-07-16</td>\n",
       "      <td>734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-07-17</td>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-07-18</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-07-19</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-07-20</td>\n",
       "      <td>727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-07-21</td>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-07-22</td>\n",
       "      <td>724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-07-23</td>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-07-24</td>\n",
       "      <td>689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018-07-25</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-07-26</td>\n",
       "      <td>719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018-07-27</td>\n",
       "      <td>746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018-07-28</td>\n",
       "      <td>693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018-07-29</td>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018-07-30</td>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2019-06-02</td>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2019-06-04</td>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2019-06-05</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2019-06-06</td>\n",
       "      <td>741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>2019-06-08</td>\n",
       "      <td>752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>2019-06-09</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>2019-06-11</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>2019-06-12</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>2019-06-14</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2019-06-15</td>\n",
       "      <td>781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>2019-06-16</td>\n",
       "      <td>741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>2019-06-17</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>2019-06-18</td>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2019-06-19</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>2019-06-22</td>\n",
       "      <td>697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2019-06-23</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>2019-06-25</td>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    date_modified  Num_Transactions\n",
       "0      2018-07-01               724\n",
       "1      2018-07-02               711\n",
       "2      2018-07-03               722\n",
       "3      2018-07-04               714\n",
       "4      2018-07-05               712\n",
       "5      2018-07-06               762\n",
       "6      2018-07-07               750\n",
       "7      2018-07-08               696\n",
       "8      2018-07-09               749\n",
       "9      2018-07-10               705\n",
       "10     2018-07-11               752\n",
       "11     2018-07-12               760\n",
       "12     2018-07-13               760\n",
       "13     2018-07-14               713\n",
       "14     2018-07-15               758\n",
       "15     2018-07-16               734\n",
       "16     2018-07-17               742\n",
       "17     2018-07-18               740\n",
       "18     2018-07-19               711\n",
       "19     2018-07-20               727\n",
       "20     2018-07-21               742\n",
       "21     2018-07-22               724\n",
       "22     2018-07-23               715\n",
       "23     2018-07-24               689\n",
       "24     2018-07-25               714\n",
       "25     2018-07-26               719\n",
       "26     2018-07-27               746\n",
       "27     2018-07-28               693\n",
       "28     2018-07-29               698\n",
       "29     2018-07-30               753\n",
       "..            ...               ...\n",
       "334    2019-06-01               737\n",
       "335    2019-06-02               709\n",
       "336    2019-06-03               703\n",
       "337    2019-06-04               684\n",
       "338    2019-06-05               730\n",
       "339    2019-06-06               741\n",
       "340    2019-06-07               807\n",
       "341    2019-06-08               752\n",
       "342    2019-06-09               769\n",
       "343    2019-06-10               727\n",
       "344    2019-06-11               673\n",
       "345    2019-06-12               762\n",
       "346    2019-06-13               659\n",
       "347    2019-06-14               784\n",
       "348    2019-06-15               781\n",
       "349    2019-06-16               741\n",
       "350    2019-06-17               701\n",
       "351    2019-06-18               690\n",
       "352    2019-06-19               712\n",
       "353    2019-06-20               746\n",
       "354    2019-06-21               753\n",
       "355    2019-06-22               697\n",
       "356    2019-06-23               705\n",
       "357    2019-06-24               662\n",
       "358    2019-06-25               753\n",
       "359    2019-06-26               723\n",
       "360    2019-06-27               709\n",
       "361    2019-06-28               730\n",
       "362    2019-06-29               745\n",
       "363    2019-06-30               744\n",
       "\n",
       "[364 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_over_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of dates on which transactions happened : 364\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of dates on which transactions happened : {}\".format(trans_over_time.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Transaction Date : 2019-06-30 00:00:00\n",
      "Min Transaction Date : 2018-07-01 00:00:00\n",
      "Days between Max and Min Transaction dates : 365\n"
     ]
    }
   ],
   "source": [
    "print(\"Max Transaction Date : {}\".format(np.max(trans['date_modified'])))\n",
    "print(\"Min Transaction Date : {}\".format(np.min(trans['date_modified'])))\n",
    "days_between = np.max(trans['date_modified'])-np.min(trans['date_modified'])+timedelta(days=1)\n",
    "print(\"Days between Max and Min Transaction dates : {}\".format(days_between.days))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Over the year transactions happened on 364 days out of 365 days, we need to find out the day when there were no transaction. On one day there were no transactions</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a date sequence\n",
    "\n",
    "start = datetime.strptime(\"2018-07-01\", \"%Y-%m-%d\")\n",
    "end = datetime.strptime(\"2019-06-30\", \"%Y-%m-%d\")\n",
    "date_generated = [start + timedelta(days=x) for x in range(0, (end-start+timedelta(1)).days)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_seq = pd.DataFrame(date_generated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_seq.rename(columns={0:'Date'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_date_trans = pd.merge(date_seq, trans_over_time, left_on='Date', right_on='date_modified', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Date</th>\n",
       "      <th>date_modified</th>\n",
       "      <th>Num_Transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>2018-12-25</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date date_modified  Num_Transactions\n",
       "177 2018-12-25           NaT               NaN"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_date_trans[missing_date_trans['Num_Transactions'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On <b> Christmas</b> there were no transactions. This may be due to national / festive holiday for the stores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot to visualize the number of transactions over time\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.plot(missing_date_trans['Date'], missing_date_trans['Num_Transactions'])\n",
    "plt.xlabel(\"Dates\")  \n",
    "plt.ylabel(\"Number of Transactions\")  \n",
    "plt.title(\"Number of Transactions over time\")  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>We can see that there is an increase in purchases in December and a break in late December.</b> We need to zoom in <b> Decembers sales / transactions </b>for the chain of stores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.strptime(\"2018-12-01\", \"%Y-%m-%d\")\n",
    "end = datetime.strptime(\"2018-12-31\", \"%Y-%m-%d\")\n",
    "december_dates = (missing_date_trans['Date'] >= start) & (missing_date_trans['Date'] <= end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "december_trans = missing_date_trans.loc[december_dates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot to visualize the number of transactions over time in December\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.plot(december_trans['Date'], december_trans['Num_Transactions'])\n",
    "plt.xlabel(\"Dates\")  \n",
    "plt.ylabel(\"Number of Transactions\")  \n",
    "plt.title(\"December Transactions over time\")  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An increase in purchases is observed in December that leads upto Christmas and there are zero sales in Christmas itself. The reason for 0 Sales on Chirstmas being shops are closed on Christmas day."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has no outliers now. We will create a feature <b>pack_size</b> by extracting the numbers from PROD_NAME column to get more insights about the products which the customers tend to purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pack Size\n",
    "\n",
    "def extract_pack_size(cols):\n",
    "    pack = cols[0]\n",
    "    \n",
    "    # Regukar expressions to extract digits \n",
    "    \n",
    "    list_match = re.findall(r'\\d+',pack.lower())\n",
    "    if len(list_match)==0:\n",
    "        return '0'\n",
    "    else :\n",
    "        return re.findall(r'\\d+',pack)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating pack_size feature from PROD_NAME\n",
    "\n",
    "trans['pack_size'] = trans[['PROD_NAME']].apply(extract_pack_size, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans = trans.astype({'pack_size' : int})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Statistics of pack size of products purchase from the retail stores</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    264834.000000\n",
       "mean        182.425512\n",
       "std          64.325148\n",
       "min          70.000000\n",
       "25%         150.000000\n",
       "50%         170.000000\n",
       "75%         175.000000\n",
       "max         380.000000\n",
       "Name: pack_size, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans['pack_size'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> We need to check whether the pack sizes are sensible and also have a look at the most popular pack size in the retail stores</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_by_pack_size = trans[['pack_size']].groupby(['pack_size']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_by_pack_size.rename(columns={0: 'Num_Transactions'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>pack_size</th>\n",
       "      <th>Num_Transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>1507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90</td>\n",
       "      <td>3008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110</td>\n",
       "      <td>22387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>125</td>\n",
       "      <td>1454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134</td>\n",
       "      <td>25102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>135</td>\n",
       "      <td>3257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>150</td>\n",
       "      <td>43131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>160</td>\n",
       "      <td>2970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>165</td>\n",
       "      <td>15297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>170</td>\n",
       "      <td>19983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pack_size  Num_Transactions\n",
       "0         70              1507\n",
       "1         90              3008\n",
       "2        110             22387\n",
       "3        125              1454\n",
       "4        134             25102\n",
       "5        135              3257\n",
       "6        150             43131\n",
       "7        160              2970\n",
       "8        165             15297\n",
       "9        170             19983"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_by_pack_size.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a histogram of PACK_SIZE\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "sns.barplot(x='pack_size', y='Num_Transactions', data=trans_by_pack_size)\n",
    "plt.xlabel(\"Pack Size\")  \n",
    "plt.ylabel(\"Number of Transactions\")  \n",
    "plt.title(\"Number of Transactions by pack size\")  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest pack size is 380 and the smallest pack is 70"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pack sizes created look reasonable and now to create brands, we can use the first word in PROD_NAME to\n",
    "work out the brand name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_brand(cols):\n",
    "    prod_name = cols[0]\n",
    "    \n",
    "    brand_name = re.findall(r'[A-Za-z]+\\s', prod_name.strip())\n",
    "    if len(brand_name)==0:\n",
    "        return 'None'\n",
    "    else:\n",
    "        return brand_name[0]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans['brand'] = trans[['PROD_NAME']].apply(get_brand, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>PROD_NAME</th>\n",
       "      <th>brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>Natural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CCs Nacho Cheese    175g</td>\n",
       "      <td>CCs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old El Paso Salsa   Dip Tomato Mild 300g</td>\n",
       "      <td>Old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Smiths Crinkle Chips Salt &amp; Vinegar 330g</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grain Waves         Sweet Chilli 210g</td>\n",
       "      <td>Grain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno 150g</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Grain Waves Sour    Cream&amp;Chives 210G</td>\n",
       "      <td>Grain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kettle Sensations   Siracha Lime 150g</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Twisties Cheese     270g</td>\n",
       "      <td>Twisties</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>WW Crinkle Cut      Chicken 175g</td>\n",
       "      <td>WW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Thins Chips Light&amp;  Tangy 175g</td>\n",
       "      <td>Thins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CCs Original 175g</td>\n",
       "      <td>CCs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Burger Rings 220g</td>\n",
       "      <td>Burger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NCC Sour Cream &amp;    Garden Chives 175g</td>\n",
       "      <td>NCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Doritos Corn Chip Southern Chicken 150g</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cheezels Cheese Box 125g</td>\n",
       "      <td>Cheezels</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Smiths Crinkle      Original 330g</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Infzns Crn Crnchers Tangy Gcamole 110g</td>\n",
       "      <td>Infzns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Kettle Sea Salt     And Vinegar 175g</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Smiths Chip Thinly  Cut Original 175g</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Kettle Original 175g</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Red Rock Deli Thai  Chilli&amp;Lime 150g</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Pringles Sthrn FriedChicken 134g</td>\n",
       "      <td>Pringles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Pringles Sweet&amp;Spcy BBQ 134g</td>\n",
       "      <td>Pringles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Red Rock Deli SR    Salsa &amp; Mzzrlla 150g</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Thins Chips         Originl saltd 175g</td>\n",
       "      <td>Thins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Red Rock Deli Sp    Salt &amp; Truffle 150G</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Tostitos Splash Of  Lime 175g</td>\n",
       "      <td>Tostitos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Woolworths Medium   Salsa 300g</td>\n",
       "      <td>Woolworths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Kettle Tortilla ChpsBtroot&amp;Ricotta 150g</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>CCs Tasty Cheese    175g</td>\n",
       "      <td>CCs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Woolworths Cheese   Rings 190g</td>\n",
       "      <td>Woolworths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Tostitos Smoked     Chipotle 175g</td>\n",
       "      <td>Tostitos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Pringles Barbeque   134g</td>\n",
       "      <td>Pringles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>WW Supreme Cheese   Corn Chips 200g</td>\n",
       "      <td>WW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Pringles Mystery    Flavour 134g</td>\n",
       "      <td>Pringles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Tyrrells Crisps     Ched &amp; Chives 165g</td>\n",
       "      <td>Tyrrells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Snbts Whlgrn Crisps Cheddr&amp;Mstrd 90g</td>\n",
       "      <td>Snbts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>Cheetos Chs &amp; Bacon Balls 190g</td>\n",
       "      <td>Cheetos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Pringles Slt Vingar 134g</td>\n",
       "      <td>Pringles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Infuzions SourCream&amp;Herbs Veg Strws 110g</td>\n",
       "      <td>Infuzions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Kettle Tortilla ChpsFeta&amp;Garlic 150g</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Infuzions Mango     Chutny Papadums 70g</td>\n",
       "      <td>Infuzions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>RRD Steak &amp;         Chimuchurri 150g</td>\n",
       "      <td>RRD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>RRD Honey Soy       Chicken 165g</td>\n",
       "      <td>RRD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>Sunbites Whlegrn    Crisps Frch/Onin 90g</td>\n",
       "      <td>Sunbites</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>RRD Salt &amp; Vinegar  165g</td>\n",
       "      <td>RRD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>Doritos Cheese      Supreme 330g</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>Smiths Crinkle Cut  Snag&amp;Sauce 150g</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>WW Sour Cream &amp;OnionStacked Chips 160g</td>\n",
       "      <td>WW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>RRD Lime &amp; Pepper   165g</td>\n",
       "      <td>RRD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Natural ChipCo Sea  Salt &amp; Vinegr 175g</td>\n",
       "      <td>Natural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>Red Rock Deli Chikn&amp;Garlic Aioli 150g</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>RRD SR Slow Rst     Pork Belly 150g</td>\n",
       "      <td>RRD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>RRD Pc Sea Salt     165g</td>\n",
       "      <td>RRD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Smith Crinkle Cut   Bolognese 150g</td>\n",
       "      <td>Smith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>Doritos Salsa Mild  300g</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    PROD_NAME        brand\n",
       "0      Natural Chip        Compny SeaSalt175g     Natural \n",
       "1                    CCs Nacho Cheese    175g         CCs \n",
       "2      Smiths Crinkle Cut  Chips Chicken 170g      Smiths \n",
       "3      Smiths Chip Thinly  S/Cream&Onion 175g      Smiths \n",
       "4    Kettle Tortilla ChpsHny&Jlpno Chili 150g      Kettle \n",
       "5    Old El Paso Salsa   Dip Tomato Mild 300g         Old \n",
       "6    Smiths Crinkle Chips Salt & Vinegar 330g      Smiths \n",
       "7       Grain Waves         Sweet Chilli 210g       Grain \n",
       "8     Doritos Corn Chip Mexican Jalapeno 150g     Doritos \n",
       "9       Grain Waves Sour    Cream&Chives 210G       Grain \n",
       "11      Kettle Sensations   Siracha Lime 150g      Kettle \n",
       "12                   Twisties Cheese     270g    Twisties \n",
       "13           WW Crinkle Cut      Chicken 175g          WW \n",
       "14             Thins Chips Light&  Tangy 175g       Thins \n",
       "15                          CCs Original 175g         CCs \n",
       "16                          Burger Rings 220g      Burger \n",
       "17     NCC Sour Cream &    Garden Chives 175g         NCC \n",
       "18    Doritos Corn Chip Southern Chicken 150g     Doritos \n",
       "19                   Cheezels Cheese Box 125g    Cheezels \n",
       "20          Smiths Crinkle      Original 330g      Smiths \n",
       "22     Infzns Crn Crnchers Tangy Gcamole 110g      Infzns \n",
       "23       Kettle Sea Salt     And Vinegar 175g      Kettle \n",
       "26      Smiths Chip Thinly  Cut Original 175g      Smiths \n",
       "27                       Kettle Original 175g      Kettle \n",
       "28       Red Rock Deli Thai  Chilli&Lime 150g         Red \n",
       "30           Pringles Sthrn FriedChicken 134g    Pringles \n",
       "31               Pringles Sweet&Spcy BBQ 134g    Pringles \n",
       "32   Red Rock Deli SR    Salsa & Mzzrlla 150g         Red \n",
       "33     Thins Chips         Originl saltd 175g       Thins \n",
       "34    Red Rock Deli Sp    Salt & Truffle 150G         Red \n",
       "..                                        ...          ...\n",
       "147             Tostitos Splash Of  Lime 175g    Tostitos \n",
       "158            Woolworths Medium   Salsa 300g  Woolworths \n",
       "160   Kettle Tortilla ChpsBtroot&Ricotta 150g      Kettle \n",
       "164                  CCs Tasty Cheese    175g         CCs \n",
       "166            Woolworths Cheese   Rings 190g  Woolworths \n",
       "171         Tostitos Smoked     Chipotle 175g    Tostitos \n",
       "179                  Pringles Barbeque   134g    Pringles \n",
       "182       WW Supreme Cheese   Corn Chips 200g          WW \n",
       "192          Pringles Mystery    Flavour 134g    Pringles \n",
       "201    Tyrrells Crisps     Ched & Chives 165g    Tyrrells \n",
       "224      Snbts Whlgrn Crisps Cheddr&Mstrd 90g       Snbts \n",
       "228            Cheetos Chs & Bacon Balls 190g     Cheetos \n",
       "232                  Pringles Slt Vingar 134g    Pringles \n",
       "256  Infuzions SourCream&Herbs Veg Strws 110g   Infuzions \n",
       "264      Kettle Tortilla ChpsFeta&Garlic 150g      Kettle \n",
       "319   Infuzions Mango     Chutny Papadums 70g   Infuzions \n",
       "321      RRD Steak &         Chimuchurri 150g         RRD \n",
       "325          RRD Honey Soy       Chicken 165g         RRD \n",
       "333  Sunbites Whlegrn    Crisps Frch/Onin 90g    Sunbites \n",
       "336                  RRD Salt & Vinegar  165g         RRD \n",
       "352          Doritos Cheese      Supreme 330g     Doritos \n",
       "396       Smiths Crinkle Cut  Snag&Sauce 150g      Smiths \n",
       "408    WW Sour Cream &OnionStacked Chips 160g          WW \n",
       "469                  RRD Lime & Pepper   165g         RRD \n",
       "487    Natural ChipCo Sea  Salt & Vinegr 175g     Natural \n",
       "520     Red Rock Deli Chikn&Garlic Aioli 150g         Red \n",
       "521       RRD SR Slow Rst     Pork Belly 150g         RRD \n",
       "528                  RRD Pc Sea Salt     165g         RRD \n",
       "549        Smith Crinkle Cut   Bolognese 150g       Smith \n",
       "689                  Doritos Salsa Mild  300g     Doritos \n",
       "\n",
       "[114 rows x 2 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the brands\n",
    "\n",
    "trans[['PROD_NAME', 'brand']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Records in Transaction Dataset : 264834\n"
     ]
    }
   ],
   "source": [
    "print('Total Records in Transaction Dataset : {}'.format(trans.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Purchase Behaviour of Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>LYLTY_CARD_NBR</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1007</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1009</td>\n",
       "      <td>NEW FAMILIES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1010</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1011</td>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1012</td>\n",
       "      <td>OLDER FAMILIES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LYLTY_CARD_NBR               LIFESTAGE PREMIUM_CUSTOMER\n",
       "0            1000   YOUNG SINGLES/COUPLES          Premium\n",
       "1            1002   YOUNG SINGLES/COUPLES       Mainstream\n",
       "2            1003          YOUNG FAMILIES           Budget\n",
       "3            1004   OLDER SINGLES/COUPLES       Mainstream\n",
       "4            1005  MIDAGE SINGLES/COUPLES       Mainstream\n",
       "5            1007   YOUNG SINGLES/COUPLES           Budget\n",
       "6            1009            NEW FAMILIES          Premium\n",
       "7            1010   YOUNG SINGLES/COUPLES       Mainstream\n",
       "8            1011   OLDER SINGLES/COUPLES       Mainstream\n",
       "9            1012          OLDER FAMILIES       Mainstream"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the customer data\n",
    "\n",
    "purchase.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 72637 entries, 0 to 72636\n",
      "Data columns (total 3 columns):\n",
      "LYLTY_CARD_NBR      72637 non-null int64\n",
      "LIFESTAGE           72637 non-null object\n",
      "PREMIUM_CUSTOMER    72637 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "purchase.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Checking for missing values in the dataset</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LYLTY_CARD_NBR      0\n",
       "LIFESTAGE           0\n",
       "PREMIUM_CUSTOMER    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of records : 72637\n",
      "Total Number of unique customers : 72637\n"
     ]
    }
   ],
   "source": [
    "print('Total Number of records : {}'.format(purchase.shape[0]))\n",
    "print('Total Number of unique customers : {}'.format(purchase['LYLTY_CARD_NBR'].drop_duplicates().shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicate customers in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RETIREES</td>\n",
       "      <td>14805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>14609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>14441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OLDER FAMILIES</td>\n",
       "      <td>9780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>9178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>7275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NEW FAMILIES</td>\n",
       "      <td>2549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    index  LIFESTAGE\n",
       "0                RETIREES      14805\n",
       "1   OLDER SINGLES/COUPLES      14609\n",
       "2   YOUNG SINGLES/COUPLES      14441\n",
       "3          OLDER FAMILIES       9780\n",
       "4          YOUNG FAMILIES       9178\n",
       "5  MIDAGE SINGLES/COUPLES       7275\n",
       "6            NEW FAMILIES       2549"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking LIFESTAGE column\n",
    "\n",
    "purchase['LIFESTAGE'].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>index</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mainstream</td>\n",
       "      <td>29245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Budget</td>\n",
       "      <td>24470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Premium</td>\n",
       "      <td>18922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  PREMIUM_CUSTOMER\n",
       "0  Mainstream             29245\n",
       "1      Budget             24470\n",
       "2     Premium             18922"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking PREMIUM_CUSTOMER column\n",
    "\n",
    "purchase['PREMIUM_CUSTOMER'].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no data issues in the customer data. Hence we can <b> merge the customer (purchase) data and the transactions dataset</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(trans, purchase, left_on = 'LYLTY_CARD_NBR', right_on='LYLTY_CARD_NBR', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>date_modified</th>\n",
       "      <th>pack_size</th>\n",
       "      <th>brand</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43390</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>175</td>\n",
       "      <td>Natural</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43599</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>CCs Nacho Cheese    175g</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>175</td>\n",
       "      <td>CCs</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43605</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>170</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43329</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>175</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43330</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>150</td>\n",
       "      <td>Kettle</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>43604</td>\n",
       "      <td>4</td>\n",
       "      <td>4074</td>\n",
       "      <td>2982</td>\n",
       "      <td>57</td>\n",
       "      <td>Old El Paso Salsa   Dip Tomato Mild 300g</td>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>300</td>\n",
       "      <td>Old</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>43601</td>\n",
       "      <td>4</td>\n",
       "      <td>4149</td>\n",
       "      <td>3333</td>\n",
       "      <td>16</td>\n",
       "      <td>Smiths Crinkle Chips Salt &amp; Vinegar 330g</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2019-05-16</td>\n",
       "      <td>330</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43601</td>\n",
       "      <td>4</td>\n",
       "      <td>4196</td>\n",
       "      <td>3539</td>\n",
       "      <td>24</td>\n",
       "      <td>Grain Waves         Sweet Chilli 210g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2019-05-16</td>\n",
       "      <td>210</td>\n",
       "      <td>Grain</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>43332</td>\n",
       "      <td>5</td>\n",
       "      <td>5026</td>\n",
       "      <td>4525</td>\n",
       "      <td>42</td>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno 150g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2018-08-20</td>\n",
       "      <td>150</td>\n",
       "      <td>Doritos</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43330</td>\n",
       "      <td>7</td>\n",
       "      <td>7150</td>\n",
       "      <td>6900</td>\n",
       "      <td>52</td>\n",
       "      <td>Grain Waves Sour    Cream&amp;Chives 210G</td>\n",
       "      <td>2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>210</td>\n",
       "      <td>Grain</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0  43390          1            1000       1         5   \n",
       "1  43599          1            1307     348        66   \n",
       "2  43605          1            1343     383        61   \n",
       "3  43329          2            2373     974        69   \n",
       "4  43330          2            2426    1038       108   \n",
       "5  43604          4            4074    2982        57   \n",
       "6  43601          4            4149    3333        16   \n",
       "7  43601          4            4196    3539        24   \n",
       "8  43332          5            5026    4525        42   \n",
       "9  43330          7            7150    6900        52   \n",
       "\n",
       "                                  PROD_NAME  PROD_QTY  TOT_SALES  \\\n",
       "0    Natural Chip        Compny SeaSalt175g         2        6.0   \n",
       "1                  CCs Nacho Cheese    175g         3        6.3   \n",
       "2    Smiths Crinkle Cut  Chips Chicken 170g         2        2.9   \n",
       "3    Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0   \n",
       "4  Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8   \n",
       "5  Old El Paso Salsa   Dip Tomato Mild 300g         1        5.1   \n",
       "6  Smiths Crinkle Chips Salt & Vinegar 330g         1        5.7   \n",
       "7     Grain Waves         Sweet Chilli 210g         1        3.6   \n",
       "8   Doritos Corn Chip Mexican Jalapeno 150g         1        3.9   \n",
       "9     Grain Waves Sour    Cream&Chives 210G         2        7.2   \n",
       "\n",
       "  date_modified  pack_size     brand               LIFESTAGE PREMIUM_CUSTOMER  \n",
       "0    2018-10-17        175  Natural    YOUNG SINGLES/COUPLES          Premium  \n",
       "1    2019-05-14        175      CCs   MIDAGE SINGLES/COUPLES           Budget  \n",
       "2    2019-05-20        170   Smiths   MIDAGE SINGLES/COUPLES           Budget  \n",
       "3    2018-08-17        175   Smiths   MIDAGE SINGLES/COUPLES           Budget  \n",
       "4    2018-08-18        150   Kettle   MIDAGE SINGLES/COUPLES           Budget  \n",
       "5    2019-05-19        300      Old   MIDAGE SINGLES/COUPLES           Budget  \n",
       "6    2019-05-16        330   Smiths   MIDAGE SINGLES/COUPLES           Budget  \n",
       "7    2019-05-16        210    Grain   MIDAGE SINGLES/COUPLES           Budget  \n",
       "8    2018-08-20        150  Doritos   MIDAGE SINGLES/COUPLES           Budget  \n",
       "9    2018-08-18        210    Grain   MIDAGE SINGLES/COUPLES           Budget  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns={'DATE'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Transactions :264834\n",
      "Total records in data :264834\n"
     ]
    }
   ],
   "source": [
    "print('Total Transactions :{}'.format(trans.shape[0]))\n",
    "print('Total records in data :{}'.format(data.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>date_modified</th>\n",
       "      <th>pack_size</th>\n",
       "      <th>brand</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [STORE_NBR, LYLTY_CARD_NBR, TXN_ID, PROD_NBR, PROD_NAME, PROD_QTY, TOT_SALES, date_modified, pack_size, brand, LIFESTAGE, PREMIUM_CUSTOMER]\n",
       "Index: []"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['TXN_ID'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STORE_NBR           0\n",
       "LYLTY_CARD_NBR      0\n",
       "TXN_ID              0\n",
       "PROD_NBR            0\n",
       "PROD_NAME           0\n",
       "PROD_QTY            0\n",
       "TOT_SALES           0\n",
       "date_modified       0\n",
       "pack_size           0\n",
       "brand               0\n",
       "LIFESTAGE           0\n",
       "PREMIUM_CUSTOMER    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values in the data set. Hence we can export the data into a <b>csv</b> file for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting the data into a csv file\n",
    "\n",
    "data.to_csv('Customer_Purchase_Behaviour_data.csv', index=False)"
   ]
  }
 ],
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