RFM Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customer Segmentation Problem Statement\n",
    "We have a transactional dataset that contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.\n",
    "\n",
    "The company wants to segment its customers and determine marketing strategies according to these segments.\n",
    "\n",
    "The dataset consists of the following attributes:\n",
    "\n",
    "InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation.\n",
    "\n",
    "StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.\n",
    "\n",
    "Description: Product (item) name. Nominal.\n",
    "\n",
    "Quantity: The quantities of each product (item) per transaction. Numeric.\n",
    "\n",
    "InvoiceDate: Invoice date and time. Numeric, the day and time when each transaction was generated. The date-time format used here is yyyy-mm-dd hh:mm:ss.\n",
    "\n",
    "UnitPrice: Unit price. Numeric, product price per unit in pound sterling, also known as GBP (Great Britain Pound).\n",
    "\n",
    "CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.\n",
    "\n",
    "Country: Country name. Nominal, the name of the country where each customer resides.\n",
    "\n",
    "Dataset Credits: https://archive.ics.uci.edu/ml/datasets/online+retail\n",
    "\n",
    "Citation: Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the dataset and create a Pandas DataFrame.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_excel(\"online-retail-customers.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    541909 non-null  object        \n",
      " 1   StockCode    541909 non-null  object        \n",
      " 2   Description  540455 non-null  object        \n",
      " 3   Quantity     541909 non-null  int64         \n",
      " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
      " 5   UnitPrice    541909 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  float64       \n",
      " 7   Country      541909 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Get the total number of rows and columns, datatypes of columns and missing values (if exist) in the dataset. \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 541909 rows and 8 columns and some columns have missing values in the dataset. We will deal with these missing values in the upcoming section. Let us first deal with cancelled orders.\n",
    "\n",
    "As per the dataset description, some of the values in field InvoiceNo may start with the letter 'C', to indicate cancelled orders. To search for the rows in the dataset where the InvoiceNo starts with 'C', we will use Regular Expressions.\n",
    "\n",
    "The InvoiceNo field has integer values but to find the cancelled orders using RegEx, it has to be converted to string data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert 'InvoiceNo' field to string and verify whether the data type is converted or not.\n",
    "df['InvoiceNo'] = df['InvoiceNo'].astype('str')\n",
    "type(df['InvoiceNo'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values of the InvoiceNo column has now been converted into a string. Next, let's search for the InvoiceNo that contains C using Regular Expressions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>C536379</td>\n",
       "      <td>D</td>\n",
       "      <td>Discount</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-01 09:41:00</td>\n",
       "      <td>27.50</td>\n",
       "      <td>14527.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>C536383</td>\n",
       "      <td>35004C</td>\n",
       "      <td>SET OF 3 COLOURED  FLYING DUCKS</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-01 09:49:00</td>\n",
       "      <td>4.65</td>\n",
       "      <td>15311.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>C536391</td>\n",
       "      <td>22556</td>\n",
       "      <td>PLASTERS IN TIN CIRCUS PARADE</td>\n",
       "      <td>-12</td>\n",
       "      <td>2010-12-01 10:24:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>C536391</td>\n",
       "      <td>21984</td>\n",
       "      <td>PACK OF 12 PINK PAISLEY TISSUES</td>\n",
       "      <td>-24</td>\n",
       "      <td>2010-12-01 10:24:00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>C536391</td>\n",
       "      <td>21983</td>\n",
       "      <td>PACK OF 12 BLUE PAISLEY TISSUES</td>\n",
       "      <td>-24</td>\n",
       "      <td>2010-12-01 10:24:00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540449</th>\n",
       "      <td>C581490</td>\n",
       "      <td>23144</td>\n",
       "      <td>ZINC T-LIGHT HOLDER STARS SMALL</td>\n",
       "      <td>-11</td>\n",
       "      <td>2011-12-09 09:57:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14397.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541541</th>\n",
       "      <td>C581499</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-12-09 10:28:00</td>\n",
       "      <td>224.69</td>\n",
       "      <td>15498.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541715</th>\n",
       "      <td>C581568</td>\n",
       "      <td>21258</td>\n",
       "      <td>VICTORIAN SEWING BOX LARGE</td>\n",
       "      <td>-5</td>\n",
       "      <td>2011-12-09 11:57:00</td>\n",
       "      <td>10.95</td>\n",
       "      <td>15311.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541716</th>\n",
       "      <td>C581569</td>\n",
       "      <td>84978</td>\n",
       "      <td>HANGING HEART JAR T-LIGHT HOLDER</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-12-09 11:58:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17315.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541717</th>\n",
       "      <td>C581569</td>\n",
       "      <td>20979</td>\n",
       "      <td>36 PENCILS TUBE RED RETROSPOT</td>\n",
       "      <td>-5</td>\n",
       "      <td>2011-12-09 11:58:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17315.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9288 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                       Description  Quantity  \\\n",
       "141      C536379         D                          Discount        -1   \n",
       "154      C536383    35004C   SET OF 3 COLOURED  FLYING DUCKS        -1   \n",
       "235      C536391     22556    PLASTERS IN TIN CIRCUS PARADE        -12   \n",
       "236      C536391     21984  PACK OF 12 PINK PAISLEY TISSUES        -24   \n",
       "237      C536391     21983  PACK OF 12 BLUE PAISLEY TISSUES        -24   \n",
       "...          ...       ...                               ...       ...   \n",
       "540449   C581490     23144   ZINC T-LIGHT HOLDER STARS SMALL       -11   \n",
       "541541   C581499         M                            Manual        -1   \n",
       "541715   C581568     21258        VICTORIAN SEWING BOX LARGE        -5   \n",
       "541716   C581569     84978  HANGING HEART JAR T-LIGHT HOLDER        -1   \n",
       "541717   C581569     20979     36 PENCILS TUBE RED RETROSPOT        -5   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "141    2010-12-01 09:41:00      27.50     14527.0  United Kingdom  \n",
       "154    2010-12-01 09:49:00       4.65     15311.0  United Kingdom  \n",
       "235    2010-12-01 10:24:00       1.65     17548.0  United Kingdom  \n",
       "236    2010-12-01 10:24:00       0.29     17548.0  United Kingdom  \n",
       "237    2010-12-01 10:24:00       0.29     17548.0  United Kingdom  \n",
       "...                    ...        ...         ...             ...  \n",
       "540449 2011-12-09 09:57:00       0.83     14397.0  United Kingdom  \n",
       "541541 2011-12-09 10:28:00     224.69     15498.0  United Kingdom  \n",
       "541715 2011-12-09 11:57:00      10.95     15311.0  United Kingdom  \n",
       "541716 2011-12-09 11:58:00       1.25     17315.0  United Kingdom  \n",
       "541717 2011-12-09 11:58:00       1.25     17315.0  United Kingdom  \n",
       "\n",
       "[9288 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Use regex to find 'C' in the 'InvoiceNo' field\n",
    "import re\n",
    "df[df['InvoiceNo'].str.contains(pat = 'C', flags = re.IGNORECASE)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the output, there are 9288 orders whose InvoiceNo contains 'C'. Thus, there are 9288 cancelled orders.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
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       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>532621 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0         536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1         536365     71053                  WHITE METAL LANTERN         6   \n",
       "2         536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3         536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4         536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "...          ...       ...                                  ...       ...   \n",
       "541904    581587     22613          PACK OF 20 SPACEBOY NAPKINS        12   \n",
       "541905    581587     22899         CHILDREN'S APRON DOLLY GIRL          6   \n",
       "541906    581587     23254        CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "541907    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "541908    581587     22138        BAKING SET 9 PIECE RETROSPOT          3   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0      2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1      2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2      2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3      2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4      2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "...                    ...        ...         ...             ...  \n",
       "541904 2011-12-09 12:50:00       0.85     12680.0          France  \n",
       "541905 2011-12-09 12:50:00       2.10     12680.0          France  \n",
       "541906 2011-12-09 12:50:00       4.15     12680.0          France  \n",
       "541907 2011-12-09 12:50:00       4.15     12680.0          France  \n",
       "541908 2011-12-09 12:50:00       4.95     12680.0          France  \n",
       "\n",
       "[532621 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove cancelled invoices from the dataset\n",
    "df = df[~(df['InvoiceNo'].str.contains('C', flags = re.IGNORECASE, regex = True))]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, we obtained a DataFrame of 532621 rows consisting of delivered orders. Let us now remove the null values from the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     134697\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the number of missing or null values in df\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before removing null values:\n",
      "Number of rows = 532621\n",
      "After removing null values:\n",
      "Number of rows = 397924\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "InvoiceNo      0\n",
       "StockCode      0\n",
       "Description    0\n",
       "Quantity       0\n",
       "InvoiceDate    0\n",
       "UnitPrice      0\n",
       "CustomerID     0\n",
       "Country        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Remove the null valued rows.\n",
    "print(f\"Before removing null values:\\nNumber of rows = {df.shape[0]}\")\n",
    "df.dropna(inplace = True)\n",
    "print(f\"After removing null values:\\nNumber of rows = {df.shape[0]}\")\n",
    "\n",
    "#  Again obtain the number of null values in df.\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the data type of CustomerID column.\n",
    "df['CustomerID'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CustomerID column is of float data type. We need to convert it into integer-based categorical column as CustomerID cannot be a floating-point value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert 'CustomerID' field to integer based categorical column.\n",
    "df['CustomerID'] = df['CustomerID'].astype('int64')\n",
    "df['CustomerID'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RFM analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55       17850  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39       17850  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75       17850  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39       17850  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39       17850  United Kingdom  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 5 samples of the DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Monetary:\n",
    "\n",
    "Monetary value is how much money a customer spends on purchases.\n",
    "\n",
    "To calculate the monetary value, we first need to calculate the total purchase value for the customers. This can be obtained by multiplying the values of the Quantity column by the UnitPrice column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
       "0 2010-12-01 08:26:00       2.55       17850  United Kingdom       15.30  \n",
       "1 2010-12-01 08:26:00       3.39       17850  United Kingdom       20.34  \n",
       "2 2010-12-01 08:26:00       2.75       17850  United Kingdom       22.00  \n",
       "3 2010-12-01 08:26:00       3.39       17850  United Kingdom       20.34  \n",
       "4 2010-12-01 08:26:00       3.39       17850  United Kingdom       20.34  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Obtain the the total purchase amount for the customers\n",
    "df['TotalPrice'] = df['Quantity'] * df['UnitPrice']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a new column named: TotalPrice which indicates the total amount spent by each customer.\n",
    "\n",
    "For Monetary, calculate the amount for all the purchases made by every individual customer. For this, first, let us check how many customers do we have in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4339"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the number of unique customers\n",
    "len(df['CustomerID'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that there 4340 categories which imply that our dataset has 4339 unique customers. The next step is to calculate the total purchase amount spent by these 4339 customers. For this,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>CustomerID</th>\n",
       "      <th>Monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>77183.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>1797.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>1757.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>18280</td>\n",
       "      <td>180.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4335</th>\n",
       "      <td>18281</td>\n",
       "      <td>80.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>18282</td>\n",
       "      <td>178.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>18283</td>\n",
       "      <td>2094.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4338</th>\n",
       "      <td>18287</td>\n",
       "      <td>1837.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4339 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  Monetary\n",
       "0          12346  77183.60\n",
       "1          12347   4310.00\n",
       "2          12348   1797.24\n",
       "3          12349   1757.55\n",
       "4          12350    334.40\n",
       "...          ...       ...\n",
       "4334       18280    180.60\n",
       "4335       18281     80.82\n",
       "4336       18282    178.05\n",
       "4337       18283   2094.88\n",
       "4338       18287   1837.28\n",
       "\n",
       "[4339 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Obtain the Monetary information from the DataFrame\n",
    "monetary_df = df[['CustomerID', 'TotalPrice']].groupby('CustomerID', as_index = False).sum()\n",
    "monetary_df.rename(columns = {'TotalPrice' : 'Monetary'}, inplace = True)\n",
    "monetary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, we have now obtained a DataFrame that tells us the total amount spent by each customer. We can easily differentiate between heavy spenders and low spenders using the monetary_df DataFrame.\n",
    "\n",
    "Let us get the frequency of purchase for respective customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>CustomerID</th>\n",
       "      <th>Frequency</th>\n",
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       "      <th>4335</th>\n",
       "      <td>18281</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>18282</td>\n",
       "      <td>12</td>\n",
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       "      <th>4337</th>\n",
       "      <td>18283</td>\n",
       "      <td>756</td>\n",
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       "    <tr>\n",
       "      <th>4338</th>\n",
       "      <td>18287</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4339 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  Frequency\n",
       "0          12346          1\n",
       "1          12347        182\n",
       "2          12348         31\n",
       "3          12349         73\n",
       "4          12350         17\n",
       "...          ...        ...\n",
       "4334       18280         10\n",
       "4335       18281          7\n",
       "4336       18282         12\n",
       "4337       18283        756\n",
       "4338       18287         70\n",
       "\n",
       "[4339 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Obtain the Frequency information from the DataFrame\n",
    "frequency_df =  df[['CustomerID', 'InvoiceNo']].groupby('CustomerID', as_index = False).count()\n",
    "frequency_df.rename(columns = {'InvoiceNo': 'Frequency'}, inplace = True)\n",
    "frequency_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hus, we obtained a DataFrame that shows the frequency of purchases made by each unique customer.\n",
    "\n",
    "Let us merge the above 2 DataFrames i.e. monetary_df and frequency_df into a single DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>Frequency</th>\n",
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       "      <td>31</td>\n",
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       "      <td>73</td>\n",
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       "      <td>17</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   CustomerID  Monetary  Frequency\n",
       "0       12346  77183.60          1\n",
       "1       12347   4310.00        182\n",
       "2       12348   1797.24         31\n",
       "3       12349   1757.55         73\n",
       "4       12350    334.40         17"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Merge 'monetary_df' and 'frequency_df' DataFrames.\n",
    "rfm_df = pd.merge(monetary_df, frequency_df, on='CustomerID', how = 'inner')\n",
    "rfm_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have obtained monetary and frequency values for RFM analysis, let us proceed with calculating the last factor i.e recency for our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>LastPurchaseDate</th>\n",
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       "      <td>18282</td>\n",
       "      <td>2011-12-02 11:43:00</td>\n",
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       "      <td>2011-12-06 12:02:00</td>\n",
       "    </tr>\n",
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       "      <th>4338</th>\n",
       "      <td>18287</td>\n",
       "      <td>2011-10-28 09:29:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4339 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID    LastPurchaseDate\n",
       "0          12346 2011-01-18 10:01:00\n",
       "1          12347 2011-12-07 15:52:00\n",
       "2          12348 2011-09-25 13:13:00\n",
       "3          12349 2011-11-21 09:51:00\n",
       "4          12350 2011-02-02 16:01:00\n",
       "...          ...                 ...\n",
       "4334       18280 2011-03-07 09:52:00\n",
       "4335       18281 2011-06-12 10:53:00\n",
       "4336       18282 2011-12-02 11:43:00\n",
       "4337       18283 2011-12-06 12:02:00\n",
       "4338       18287 2011-10-28 09:29:00\n",
       "\n",
       "[4339 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the last purchase date for each customer\n",
    "recency_df = df[['CustomerID', 'InvoiceDate']].groupby('CustomerID', as_index = False).max()\n",
    "recency_df.rename(columns = {'InvoiceDate': 'LastPurchaseDate'}, inplace = True)\n",
    "recency_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have obtained a DataFrame that shows the last purchase date of each customer. But for recency, we need the number of days between the last purchase date and the present date (or a reference date).\n",
    "\n",
    "First, find out the last invoice date in the dataset from the InvoiceDate column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-09 12:50:00')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Obtain the last invoice date in the dataset.\n",
    "df['InvoiceDate'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the last invoice date is 2011–12–09, we will consider 2011–12–10 as the present date to calculate recency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-10 12:50:00')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Obtain the present date i.e LastPurchaseDate + 1 day\n",
    "present_date = df['InvoiceDate'].max() + pd.Timedelta(\"1 day\")\n",
    "present_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have both the dates i.e. the last purchase date of each customer (recency_df['LastPurchaseDate']) and the present date (present_date). We can now easily calculate the number of days that have passed since the customer's last purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      326 days 02:49:00\n",
       "1        2 days 20:58:00\n",
       "2       75 days 23:37:00\n",
       "3       19 days 02:59:00\n",
       "4      310 days 20:49:00\n",
       "              ...       \n",
       "4334   278 days 02:58:00\n",
       "4335   181 days 01:57:00\n",
       "4336     8 days 01:07:00\n",
       "4337     4 days 00:48:00\n",
       "4338    43 days 03:21:00\n",
       "Name: LastPurchaseDate, Length: 4339, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Obtain the days since last purchase made by a customer\n",
    "days_last_purchase = present_date - recency_df['LastPurchaseDate'] \n",
    "days_last_purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       326\n",
       "1         2\n",
       "2        75\n",
       "3        19\n",
       "4       310\n",
       "       ... \n",
       "4334    278\n",
       "4335    181\n",
       "4336      8\n",
       "4337      4\n",
       "4338     43\n",
       "Name: LastPurchaseDate, Length: 4339, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Extract days from datetime using 'dt.days' attribute\n",
    "recency_days = days_last_purchase.dt.days\n",
    "recency_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>4335</th>\n",
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       "      <td>80.82</td>\n",
       "      <td>7</td>\n",
       "      <td>181</td>\n",
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       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>18282</td>\n",
       "      <td>178.05</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>18283</td>\n",
       "      <td>2094.88</td>\n",
       "      <td>756</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4338</th>\n",
       "      <td>18287</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>70</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4339 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  Monetary  Frequency  Recency\n",
       "0          12346  77183.60          1      326\n",
       "1          12347   4310.00        182        2\n",
       "2          12348   1797.24         31       75\n",
       "3          12349   1757.55         73       19\n",
       "4          12350    334.40         17      310\n",
       "...          ...       ...        ...      ...\n",
       "4334       18280    180.60         10      278\n",
       "4335       18281     80.82          7      181\n",
       "4336       18282    178.05         12        8\n",
       "4337       18283   2094.88        756        4\n",
       "4338       18287   1837.28         70       43\n",
       "\n",
       "[4339 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add 'recency_days' as column to the merged DataFrame 'rfm_df'.\n",
    "rfm_df['Recency'] = recency_days\n",
    "rfm_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a DataFrame for RFM analysis consisting of the necessary fields to carry out the customer segmentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Analysis\n",
    "\n",
    "For clustering, the CustomerID field is not required hence it can be dropped from the rfm_df DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monetary</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77183.60</td>\n",
       "      <td>1</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4310.00</td>\n",
       "      <td>182</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1797.24</td>\n",
       "      <td>31</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>334.40</td>\n",
       "      <td>17</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>180.60</td>\n",
       "      <td>10</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4335</th>\n",
       "      <td>80.82</td>\n",
       "      <td>7</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>178.05</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>2094.88</td>\n",
       "      <td>756</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4338</th>\n",
       "      <td>1837.28</td>\n",
       "      <td>70</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4339 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Monetary  Frequency  Recency\n",
       "0     77183.60          1      326\n",
       "1      4310.00        182        2\n",
       "2      1797.24         31       75\n",
       "3      1757.55         73       19\n",
       "4       334.40         17      310\n",
       "...        ...        ...      ...\n",
       "4334    180.60         10      278\n",
       "4335     80.82          7      181\n",
       "4336    178.05         12        8\n",
       "4337   2094.88        756        4\n",
       "4338   1837.28         70       43\n",
       "\n",
       "[4339 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the 'CustomerID' column\n",
    "rfm_df.drop(columns = 'CustomerID', inplace = True)\n",
    "rfm_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create histogram and boxplots to understand the distribution of Monetary, Frequency, and Recency columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Obtain the histogram and boxplots\n",
    "fig, axis = plt.subplots(nrows = 2, ncols = 3, figsize = (15, 5), dpi = 100)\n",
    "\n",
    "# Construct Histogram and Boxplot for 'Monetary'\n",
    "axis[0, 0].hist(rfm_df['Monetary'], bins = 'sturges', facecolor = 'red', edgecolor = 'black')\n",
    "sns.boxplot(x = 'Monetary', data = rfm_df, ax = axis[1, 0], color = 'red')\n",
    "axis[0, 0].set_title(\"Histogram and Boxplot for Monetary\")\n",
    "\n",
    "# Construct Histogram and Boxplot for 'Frequency'\n",
    "axis[0, 1].hist(rfm_df['Frequency'], bins = 'sturges', facecolor = 'green', edgecolor = 'black')\n",
    "sns.boxplot(x = 'Frequency', data = rfm_df, ax = axis[1, 1], color = 'green')\n",
    "axis[0, 1].set_title(\"Histogram and Boxplot for Frequency\")\n",
    "\n",
    "# Construct Histogram and Boxplot for 'Recency'\n",
    "axis[0, 2].hist(rfm_df['Recency'], bins = 'sturges', facecolor = 'purple', edgecolor = 'black')\n",
    "sns.boxplot(x = 'Recency', data = rfm_df, ax = axis[1, 2], color = 'purple')\n",
    "axis[0, 2].set_title(\"Histogram and Boxplot for Recency\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot it is clear there are lot of outliers in Monetary and Frequency fields.\n",
    "\n",
    "These outliers will affect the model as the K-Means clustering is based on the distance of data points from the cluster centroids. These outliers will shift the cluster centroids away from their intended positions thereby generating inaccurate clusters. To compensate for this we need to remove the outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Create a function for removing the outliers.\n",
    "def remove_outliers(df, col):\n",
    "  q1 = df[col].quantile(0.25)  # Q1\n",
    "  q3 = df[col].quantile(0.75)  # Q3\n",
    "  iqr = q3 - q1                # IQR = Q3 - Q1\n",
    "  lower_bound =  q1 - 1.5 * iqr  # lower_bound = Q1 − 1.5 * IQR\n",
    "  upper_bound = q3 + 1.5 * iqr  # upper_bound = Q3 + 1.5 * IQR\n",
    "  new_df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n",
    "\n",
    "  return new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Monetary</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1797.24</td>\n",
       "      <td>31</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>334.40</td>\n",
       "      <td>17</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2506.04</td>\n",
       "      <td>85</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89.00</td>\n",
       "      <td>4</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3907</th>\n",
       "      <td>180.60</td>\n",
       "      <td>10</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3908</th>\n",
       "      <td>80.82</td>\n",
       "      <td>7</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3909</th>\n",
       "      <td>178.05</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3910</th>\n",
       "      <td>2094.88</td>\n",
       "      <td>756</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3911</th>\n",
       "      <td>1837.28</td>\n",
       "      <td>70</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3912 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Monetary  Frequency  Recency\n",
       "0      1797.24         31       75\n",
       "1      1757.55         73       19\n",
       "2       334.40         17      310\n",
       "3      2506.04         85       36\n",
       "4        89.00          4      204\n",
       "...        ...        ...      ...\n",
       "3907    180.60         10      278\n",
       "3908     80.82          7      181\n",
       "3909    178.05         12        8\n",
       "3910   2094.88        756        4\n",
       "3911   1837.28         70       43\n",
       "\n",
       "[3912 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Removing outliers from 'Monetary' field\n",
    "m_clean_df = remove_outliers(rfm_df, 'Monetary').reset_index(drop = True)\n",
    "m_clean_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rfm_df had 4339 rows and after removal of outliers m_clean_df has 3912 rows which means there were  4339−3912=427  outliers in the Monetary field.\n",
    "\n",
    "Let us again create boxplots for the Recency, Monetary, and Frequency field and observe whether there is any improvement in data distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Obtain the boxplots\n",
    "fig, axis = plt.subplots(nrows = 1, ncols = 3, figsize = (15, 5), dpi = 100)\n",
    "\n",
    "# Construct Boxplot for 'Monetary'\n",
    "sns.boxplot(x = 'Monetary', data = m_clean_df, ax = axis[0], color = 'red')\n",
    "axis[0].set_title(\"Boxplot for Monetary\")\n",
    "\n",
    "# Construct Boxplot for 'Frequency'\n",
    "sns.boxplot(x = 'Frequency', data = m_clean_df, ax = axis[1], color = 'green')\n",
    "axis[1].set_title(\"Boxplot for Frequency\")\n",
    "\n",
    "# Construct Boxplot for 'Recency'\n",
    "sns.boxplot(x = 'Recency', data = m_clean_df, ax = axis[2], color = 'purple')\n",
    "axis[2].set_title(\"Boxplot for Recency\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that lot of outliers has been removed from the Monetary and Frequency columns.\n",
    "Next, let us standardise the DataFrame, so that all the columns have mean equals to 0 and the standard deviation equals to 1. For this,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Monetary</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.099421</td>\n",
       "      <td>-0.394646</td>\n",
       "      <td>-0.244806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.051512</td>\n",
       "      <td>0.148701</td>\n",
       "      <td>-0.796176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.666340</td>\n",
       "      <td>-0.575761</td>\n",
       "      <td>2.068980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.954997</td>\n",
       "      <td>0.303943</td>\n",
       "      <td>-0.628796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.962557</td>\n",
       "      <td>-0.743940</td>\n",
       "      <td>1.025315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3907</th>\n",
       "      <td>-0.851989</td>\n",
       "      <td>-0.666319</td>\n",
       "      <td>1.753911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3908</th>\n",
       "      <td>-0.972431</td>\n",
       "      <td>-0.705130</td>\n",
       "      <td>0.798859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3909</th>\n",
       "      <td>-0.855067</td>\n",
       "      <td>-0.640446</td>\n",
       "      <td>-0.904481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3910</th>\n",
       "      <td>1.458695</td>\n",
       "      <td>8.984552</td>\n",
       "      <td>-0.943865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3911</th>\n",
       "      <td>1.147752</td>\n",
       "      <td>0.109890</td>\n",
       "      <td>-0.559875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3912 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Monetary  Frequency   Recency\n",
       "0     1.099421  -0.394646 -0.244806\n",
       "1     1.051512   0.148701 -0.796176\n",
       "2    -0.666340  -0.575761  2.068980\n",
       "3     1.954997   0.303943 -0.628796\n",
       "4    -0.962557  -0.743940  1.025315\n",
       "...        ...        ...       ...\n",
       "3907 -0.851989  -0.666319  1.753911\n",
       "3908 -0.972431  -0.705130  0.798859\n",
       "3909 -0.855067  -0.640446 -0.904481\n",
       "3910  1.458695   8.984552 -0.943865\n",
       "3911  1.147752   0.109890 -0.559875\n",
       "\n",
       "[3912 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Normalise the RFM parameters\n",
    "# Import StandardScaler Module from sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Make an object 'StandardScaler()'\n",
    "standard_scaler = StandardScaler()\n",
    "\n",
    "# Perform fit and transform operation using 'fit_transform()'\n",
    "scaled_values = standard_scaler.fit_transform(m_clean_df)\n",
    "\n",
    "# Make a new DataFrame\n",
    "scaled_df = pd.DataFrame(scaled_values)\n",
    "scaled_df.columns = m_clean_df.columns\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obtain the histograms.\n",
    "fig, axis = plt.subplots(nrows = 1, ncols = 3, figsize = (15, 5), dpi = 100)\n",
    "\n",
    "# Construct Histogram for 'Monetary'\n",
    "axis[0].hist(scaled_df['Monetary'], bins = 'sturges', facecolor = 'red', edgecolor = 'black')\n",
    "axis[0].set_title(\"Histogram for Monetary\")\n",
    "axis[0].axvline(x=scaled_df['Monetary'].mean())\n",
    "# Construct Histogram for 'Frequency'\n",
    "axis[1].hist(scaled_df['Frequency'], bins = 'sturges', facecolor = 'green', edgecolor = 'black')\n",
    "axis[1].set_title(\"Histogram for Frequency\")\n",
    "axis[1].axvline(x=scaled_df['Frequency'].mean())\n",
    "# Construct Histogram for 'Recency'\n",
    "axis[2].hist(scaled_df['Recency'], bins = 'sturges', facecolor = 'purple', edgecolor = 'black')\n",
    "axis[2].set_title(\"Histogram for Recency\")\n",
    "axis[2].axvline(x=scaled_df['Recency'].mean())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that all the columns now have same mean and variance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying K-Means Clustering\n",
    "\n",
    "We start by finding the optimal number of clusters for the K-Means algorithm. We will use the Elbow method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Determine 'K' using Elbow method.\n",
    "from sklearn.cluster import KMeans\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "wcss = []\n",
    "\n",
    "clusters = range(1, 11)\n",
    "# Initiate a for loop that ranges from 1 to 10.\n",
    "for k in clusters:\n",
    "    # Inside for loop, perform K-means clustering for current value of K. Use 'fit()' to train the model.\n",
    "    kmeans = KMeans(n_clusters = k, random_state = 10)\n",
    "    kmeans.fit(scaled_df)\n",
    "    # Find wcss for current K value using 'inertia_' attribute and append it to the empty list.\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "# Plot WCSS vs number of clusters.\n",
    "plt.figure(figsize = (14, 5))\n",
    "plt.title('WCSS (Within Cluster Sum of Squares)')\n",
    "plt.plot(clusters, wcss)\n",
    "plt.xlabel(\"K\")\n",
    "plt.ylabel(\"WCSS\")\n",
    "plt.grid()\n",
    "plt.xticks(range(1,11))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot, it looks like decrease starts to slow down between 3 and 5. So you can choose any number of clusters from 3 to 5. Let us use 4 clusters to perform K-Means clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2024\n",
       "2     983\n",
       "3     786\n",
       "0     119\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Clustering the dataset for K = 4\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Perform K-Means clustering with n_clusters = 4 and random_state = 10\n",
    "kmeans_model = KMeans(n_clusters = 4, random_state = 10)\n",
    "\n",
    "# Fit the model to the scaled_df\n",
    "kmeans_model.fit(scaled_df)\n",
    "\n",
    "# Make a series using predictions by K-Means\n",
    "cluster_labels = pd.Series(kmeans_model.predict(scaled_df))\n",
    "cluster_labels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Monetary</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Cluster Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1797.24</td>\n",
       "      <td>31</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>334.40</td>\n",
       "      <td>17</td>\n",
       "      <td>310</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2506.04</td>\n",
       "      <td>85</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89.00</td>\n",
       "      <td>4</td>\n",
       "      <td>204</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3907</th>\n",
       "      <td>180.60</td>\n",
       "      <td>10</td>\n",
       "      <td>278</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3908</th>\n",
       "      <td>80.82</td>\n",
       "      <td>7</td>\n",
       "      <td>181</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3909</th>\n",
       "      <td>178.05</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3910</th>\n",
       "      <td>2094.88</td>\n",
       "      <td>756</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3911</th>\n",
       "      <td>1837.28</td>\n",
       "      <td>70</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3912 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Monetary  Frequency  Recency  Cluster Label\n",
       "0      1797.24         31       75              3\n",
       "1      1757.55         73       19              3\n",
       "2       334.40         17      310              2\n",
       "3      2506.04         85       36              3\n",
       "4        89.00          4      204              2\n",
       "...        ...        ...      ...            ...\n",
       "3907    180.60         10      278              2\n",
       "3908     80.82          7      181              2\n",
       "3909    178.05         12        8              1\n",
       "3910   2094.88        756        4              0\n",
       "3911   1837.28         70       43              3\n",
       "\n",
       "[3912 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame with cluster labels for cluster visualisation\n",
    "km_df = pd.concat([m_clean_df, cluster_labels], axis = 1)\n",
    "km_df.columns = list(m_clean_df.columns) + ['Cluster Label']\n",
    "km_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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   "source": [
    "# Visualising the clusters for customer segmentation\n",
    "import plotly.express as px\n",
    "px.scatter_3d(km_df, x = 'Monetary', y = 'Frequency', z = 'Recency', color = 'Cluster Label')\n"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "Summarising clusters:"
   ]
  },
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   "execution_count": 34,
   "metadata": {},
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    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster Label</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>2024</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>24.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>983</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2096.0</td>\n",
       "      <td>786</td>\n",
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      "text/plain": [
       "              Recency Frequency Monetary      \n",
       "                 mean      mean     mean count\n",
       "Cluster Label                                 \n",
       "0                24.0     374.0   2405.0   119\n",
       "1                52.0      39.0    573.0  2024\n",
       "2               255.0      24.0    380.0   983\n",
       "3                42.0     119.0   2096.0   786"
      ]
     },
     "execution_count": 34,
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   ],
   "source": [
    "#Understanding the Cluster Distribution\n",
    "mean_df = km_df.groupby(['Cluster Label']).agg({'Recency':['mean'],\n",
    "                                              'Frequency':['mean'],\n",
    "                                              'Monetary':['mean','count']}).round(0)\n",
    "mean_df"
   ]
  },
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   "metadata": {},
   "source": [
    "The above dataframe gives an optimal interpretation of clusters. Let us understand what each cluster represent.\n",
    "\n",
    "\n",
    "\n",
    "- The <b><font color = blue>first cluster</font></b> (label 3) belongs to the \"Promising Customers\" segment as:\n",
    "    - They purchased recently (`R = 52 days`).\n",
    "    - Average purchase frequency is very less (`F = 39 purchases`).\n",
    "    - They spend little (`M = 573 GBP`).\n",
    "\n",
    "- The <b><font color= purple>second cluster</font></b> (label 0) belongs to the \"Almost Lost Customers\" segment as:\n",
    "  - Their last purchase  is long ago (`R = 255 days`).\n",
    "  - Average purchase frequency is very less (`F = 24 purchases`).\n",
    "  - They spend very little (`M = 380 GBP`).\n",
    "\n",
    "- The \"best customers\" are in <b><font color = orange>third cluster</font></b> (label 2) and <b><mark>fourth cluster</mark></b> (label 1). They spent the greatest amount of money, made many purchases and their last purchase was few days before.\n",
    "\n",
    "Hence, we can see that using K-Means clustering we divided customers into clusters. Customers in each cluster have similar buying behaviours, so we can use them to personalise marketing offers."
   ]
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