Customers Segmentation Analysis.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customers Segmentation for Marketing | RFM Modeling"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we create a customer segmentation for marketing using a RFM model in python. To perform the segmentation, we calculate and score each customer based on how recent they made their last purchase, how often they make a purchase and how much they have spent in purchase since they first order. Using these scores we are able to identify the most valuable customers and the customers at risk of churning."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Summary\n",
    "***"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context\n",
    "***\n",
    "The data set contains all the transactions for a UK-based online retail company from 01/12/2010 to 09/12/2011. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.\n",
    "\n",
    "> The retail company wants to rank and group their customers. They want to identify the most valuable customers and the customers at risk of churning. We will perform the segmentation by using the RFM Model.\n",
    "\n",
    "RFM (recency, frequency, monetary) analysis is a marketing approach that is used to quantitatively evaluate and group clients based on the recency, frequency and monetary of their most recent transactions in order to pinpoint the best customers and execute focused marketing campaigns. In order to provide an objective analysis, the system gives each consumer a numerical score based on these variables. These scores ranges from 1 to 5. Below are the details of the variables.\n",
    "\n",
    "**Recency:** What was the customer's most recent purchase? Customers who have recently made a purchase are more likely to make another buy or use the product since they are still thinking about it. This factor is usually measured in days.\n",
    "\n",
    "**Frequency:** How frequently did this customer buy throughout a certain period of time? Customers who have previously made a purchase are more likely to do so again. In order to turn first-time clients into loyal customers, follow-up advertising may make them a great target.\n",
    "\n",
    "**Monetary:** How much did the customer spend over a given timeframe? Customers that spend a lot of money are more likely to make additional purchases in the future and are valuable to a company.\n",
    "\n",
    "RFM analysis is a powerful tool in marketing that helps marketers make the best of their advertising budget. It enables businesses to use a targeted marketing campaigns which a specific group of customers are more likely to respond to.\n",
    "\n",
    "## Actions\n",
    "***\n",
    "The data contains severals columns which includes the *invoice number, stock code, description, quantity, invoice date, unit price, customer id and country*. We dropped all records with missing *customer ID* and filter out all cancelled transactions. From the data description, we observed that some transactions have a unit price of 0. This could be items purchased through promotions such as *buy 1 get 1 free* or it could be a data entry error. Since we have only 40 transactions with such issues, we will exclude them from this analysis.\n",
    "\n",
    "We grouped the transactions by customers to help us compute the **Recency**, the **Frequency**, and the **Monetary** values. We then computed scores for the RFM variables of each customer. These scores ranks each customer from 1 to 5 where 5 is the highest rank and 1 is the lowest.\n",
    "\n",
    "We finally computed the RFM values by summing the scores of Recency, Frequency and Monetary. We then computed the RFM score by ranking the RFM values. We will assign a customer to a segment based on their RFM score. The segments are made up of *Bronze Customers*, *Silver Customers*, *Gold Customers*, *Platinum Customers*, and *Diamond Customers*. A RFM score of 0 corresponds to a *Bronze Customer* and RFM score of 5 corresponds to a *Diamond Customer*.\n",
    "\n",
    "## Result\n",
    "***\n",
    "![CustomersSegments](./img/segments.png)\n",
    "\n",
    "Our most valuable customers, **Diamond Customers**, makes up **7%** of our entire customers base. On average, they have purchased 489 times and spent 13,665 USD on average in this evaluate period. Our customers who are at risk of churning, **Bronze Customers**, makes up **28%** of our entire customers base. On average, they made a purchase only 14 times and spent 253 USD within the evaluation period.\n",
    "\n",
    "### Recommendation\n",
    "Based on the RFM Group, customers with high recency, high frequency and high monetary values (5,5,5) are valuable customers. They should be targeted with special promotions to keep them active.\n",
    "Customers with high recency and low frequency value (5,1,X) are new customers. A targeted follow-up may convert them into frequent customers.\n",
    "Customers with low recency but high monetary value (1,X,5) were once valuable customers. A targeted offer may reactivate them."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Required Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import datetime as dt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the data to a data frame\n",
    "data = pd.read_excel(\"./data/Online Retail.xlsx\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Definition\n",
    "- InvoiceNo: Invoice number, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation.\n",
    "- StockCode: Product (item) code, a 5-digit integral number uniquely assigned to each distinct product.\n",
    "- Description: Product (item) name.\n",
    "- Quantity: The quantities of each product (item) per transaction.\n",
    "- InvoiceDate: Invoice Date and time, the day and time when each transaction was generated.\n",
    "- UnitPrice: Unit price, Product price per unit in sterling.\n",
    "- CustomerID: Customer number, a 5-digit integral number uniquely assigned to each customer.\n",
    "- Country: Country name, the name of the country where each customer resides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Definition\n",
    "- InvoiceNo: Invoice number, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation.\n",
    "- StockCode: Product code, a 5-digit integral number uniquely assigned to each distinct product.\n",
    "- Description: Product name.\n",
    "- Quantity: The quantities of each product per transaction.\n",
    "- InvoiceDate: Invoice Date and time, the day and time when each transaction was generated.\n",
    "- UnitPrice: Unit price, Product price per unit in sterling.\n",
    "- CustomerID: Customer number, a 5-digit integral number uniquely assigned to each customer.\n",
    "- Country: Country name, the name of the country where each customer resides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "# getting some information about the data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows and columns in the dataset\n",
    "data.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice data columns are well formatted and there are some null values in the *CustomerID and Description* columns."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "Let's check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135080\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop rows with no customer ID\n",
    "data = data.dropna(subset=['CustomerID'])\n",
    "data.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check for cancelled orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>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",
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       "    <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>8905 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",
       "[8905 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancelled_records = data[data[\"InvoiceNo\"].str.startswith(\"C\").fillna(False)]\n",
    "cancelled_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exclude cancelled orders from the data\n",
    "data = data.drop(cancelled_records.index, axis=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check for duplicated orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5192"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicates\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>517</th>\n",
       "      <td>536409</td>\n",
       "      <td>21866</td>\n",
       "      <td>UNION JACK FLAG LUGGAGE TAG</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>536409</td>\n",
       "      <td>22866</td>\n",
       "      <td>HAND WARMER SCOTTY DOG DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>536409</td>\n",
       "      <td>22900</td>\n",
       "      <td>SET 2 TEA TOWELS I LOVE LONDON</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:45:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>536409</td>\n",
       "      <td>22111</td>\n",
       "      <td>SCOTTIE DOG HOT WATER BOTTLE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:45:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>536412</td>\n",
       "      <td>22327</td>\n",
       "      <td>ROUND SNACK BOXES SET OF 4 SKULLS</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>17920.0</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>541675</th>\n",
       "      <td>581538</td>\n",
       "      <td>22068</td>\n",
       "      <td>BLACK PIRATE TREASURE CHEST</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-09 11:34:00</td>\n",
       "      <td>0.39</td>\n",
       "      <td>14446.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541689</th>\n",
       "      <td>581538</td>\n",
       "      <td>23318</td>\n",
       "      <td>BOX OF 6 MINI VINTAGE CRACKERS</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-09 11:34:00</td>\n",
       "      <td>2.49</td>\n",
       "      <td>14446.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541692</th>\n",
       "      <td>581538</td>\n",
       "      <td>22992</td>\n",
       "      <td>REVOLVER WOODEN RULER</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-09 11:34:00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>14446.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541699</th>\n",
       "      <td>581538</td>\n",
       "      <td>22694</td>\n",
       "      <td>WICKER STAR</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-09 11:34:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>14446.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541701</th>\n",
       "      <td>581538</td>\n",
       "      <td>23343</td>\n",
       "      <td>JUMBO BAG VINTAGE CHRISTMAS</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-09 11:34:00</td>\n",
       "      <td>2.08</td>\n",
       "      <td>14446.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5192 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                        Description  Quantity  \\\n",
       "517       536409     21866        UNION JACK FLAG LUGGAGE TAG         1   \n",
       "527       536409     22866      HAND WARMER SCOTTY DOG DESIGN         1   \n",
       "537       536409     22900    SET 2 TEA TOWELS I LOVE LONDON          1   \n",
       "539       536409     22111       SCOTTIE DOG HOT WATER BOTTLE         1   \n",
       "555       536412     22327  ROUND SNACK BOXES SET OF 4 SKULLS         1   \n",
       "...          ...       ...                                ...       ...   \n",
       "541675    581538     22068        BLACK PIRATE TREASURE CHEST         1   \n",
       "541689    581538     23318     BOX OF 6 MINI VINTAGE CRACKERS         1   \n",
       "541692    581538     22992             REVOLVER WOODEN RULER          1   \n",
       "541699    581538     22694                       WICKER STAR          1   \n",
       "541701    581538     23343       JUMBO BAG VINTAGE CHRISTMAS          1   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "517    2010-12-01 11:45:00       1.25     17908.0  United Kingdom  \n",
       "527    2010-12-01 11:45:00       2.10     17908.0  United Kingdom  \n",
       "537    2010-12-01 11:45:00       2.95     17908.0  United Kingdom  \n",
       "539    2010-12-01 11:45:00       4.95     17908.0  United Kingdom  \n",
       "555    2010-12-01 11:49:00       2.95     17920.0  United Kingdom  \n",
       "...                    ...        ...         ...             ...  \n",
       "541675 2011-12-09 11:34:00       0.39     14446.0  United Kingdom  \n",
       "541689 2011-12-09 11:34:00       2.49     14446.0  United Kingdom  \n",
       "541692 2011-12-09 11:34:00       1.95     14446.0  United Kingdom  \n",
       "541699 2011-12-09 11:34:00       2.10     14446.0  United Kingdom  \n",
       "541701 2011-12-09 11:34:00       2.08     14446.0  United Kingdom  \n",
       "\n",
       "[5192 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicated_records = data[data.duplicated()]\n",
    "duplicated_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop duplicated transactions\n",
    "data = data.drop(duplicated_records.index, axis=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We noticed there are some data issues such as 5192 duplicated transactions, 135080 missing customer IDs and 8905 transactions which were cancelled. These data issues will influence the accuracy of our analysis hence we omitted all duplicates, null values and cancelled transactions from our records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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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       "    }\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>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>392732.000000</td>\n",
       "      <td>392732.000000</td>\n",
       "      <td>392732.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.153718</td>\n",
       "      <td>3.125596</td>\n",
       "      <td>15287.734822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>181.588420</td>\n",
       "      <td>22.240725</td>\n",
       "      <td>1713.567773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13955.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>15150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>16791.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>8142.750000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID\n",
       "count  392732.000000  392732.000000  392732.000000\n",
       "mean       13.153718       3.125596   15287.734822\n",
       "std       181.588420      22.240725    1713.567773\n",
       "min         1.000000       0.000000   12346.000000\n",
       "25%         2.000000       1.250000   13955.000000\n",
       "50%         6.000000       1.950000   15150.000000\n",
       "75%        12.000000       3.750000   16791.000000\n",
       "max     80995.000000    8142.750000   18287.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 8)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for unit price with 0 value\n",
    "data.query('UnitPrice == 0').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>358655</th>\n",
       "      <td>568158</td>\n",
       "      <td>PADS</td>\n",
       "      <td>PADS TO MATCH ALL CUSHIONS</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-09-25 12:22:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16133.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454463</th>\n",
       "      <td>575579</td>\n",
       "      <td>22437</td>\n",
       "      <td>SET OF 9 BLACK SKULL BALLOONS</td>\n",
       "      <td>20</td>\n",
       "      <td>2011-11-10 11:49:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379913</th>\n",
       "      <td>569716</td>\n",
       "      <td>22778</td>\n",
       "      <td>GLASS CLOCHE SMALL</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-10-06 08:17:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15804.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454464</th>\n",
       "      <td>575579</td>\n",
       "      <td>22089</td>\n",
       "      <td>PAPER BUNTING VINTAGE PAISLEY</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-11-10 11:49:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479079</th>\n",
       "      <td>577129</td>\n",
       "      <td>22464</td>\n",
       "      <td>HANGING METAL HEART LANTERN</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-11-17 19:52:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15602.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>374542</th>\n",
       "      <td>569382</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-03 16:44:00</td>\n",
       "      <td>3155.95</td>\n",
       "      <td>15502.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406406</th>\n",
       "      <td>571751</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-19 11:18:00</td>\n",
       "      <td>3949.32</td>\n",
       "      <td>12744.0</td>\n",
       "      <td>Singapore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422351</th>\n",
       "      <td>573077</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-27 14:13:00</td>\n",
       "      <td>4161.06</td>\n",
       "      <td>12536.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422376</th>\n",
       "      <td>573080</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-27 14:20:00</td>\n",
       "      <td>4161.06</td>\n",
       "      <td>12536.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173382</th>\n",
       "      <td>551697</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-05-03 13:46:00</td>\n",
       "      <td>8142.75</td>\n",
       "      <td>16029.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392732 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                    Description  Quantity  \\\n",
       "358655    568158      PADS     PADS TO MATCH ALL CUSHIONS         1   \n",
       "454463    575579     22437  SET OF 9 BLACK SKULL BALLOONS        20   \n",
       "379913    569716     22778             GLASS CLOCHE SMALL         2   \n",
       "454464    575579     22089  PAPER BUNTING VINTAGE PAISLEY        24   \n",
       "479079    577129     22464    HANGING METAL HEART LANTERN         4   \n",
       "...          ...       ...                            ...       ...   \n",
       "374542    569382         M                         Manual         1   \n",
       "406406    571751         M                         Manual         1   \n",
       "422351    573077         M                         Manual         1   \n",
       "422376    573080         M                         Manual         1   \n",
       "173382    551697      POST                        POSTAGE         1   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "358655 2011-09-25 12:22:00       0.00     16133.0  United Kingdom  \n",
       "454463 2011-11-10 11:49:00       0.00     13081.0  United Kingdom  \n",
       "379913 2011-10-06 08:17:00       0.00     15804.0  United Kingdom  \n",
       "454464 2011-11-10 11:49:00       0.00     13081.0  United Kingdom  \n",
       "479079 2011-11-17 19:52:00       0.00     15602.0  United Kingdom  \n",
       "...                    ...        ...         ...             ...  \n",
       "374542 2011-10-03 16:44:00    3155.95     15502.0  United Kingdom  \n",
       "406406 2011-10-19 11:18:00    3949.32     12744.0       Singapore  \n",
       "422351 2011-10-27 14:13:00    4161.06     12536.0          France  \n",
       "422376 2011-10-27 14:20:00    4161.06     12536.0          France  \n",
       "173382 2011-05-03 13:46:00    8142.75     16029.0  United Kingdom  \n",
       "\n",
       "[392732 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values('UnitPrice')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the data description, we observed that some transactions have a unit price of 0. This could be items purchased through promotions such as *buy 1 get 1 free* or it could be a data entry error. Since we have only 40 transactions with such issues, we will exclude them from this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(data.query('UnitPrice == 0').index, axis=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "We compute the total amount spent per transaction using the quantity and unit price columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>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>Total_Amount</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",
       "      <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>17850.0</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>17850.0</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>17850.0</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>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </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",
       "      <td>10.20</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",
       "      <td>12.60</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",
       "      <td>16.60</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",
       "      <td>16.60</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",
       "      <td>14.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392692 rows × 9 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",
       "        Total_Amount  \n",
       "0              15.30  \n",
       "1              20.34  \n",
       "2              22.00  \n",
       "3              20.34  \n",
       "4              20.34  \n",
       "...              ...  \n",
       "541904         10.20  \n",
       "541905         12.60  \n",
       "541906         16.60  \n",
       "541907         16.60  \n",
       "541908         14.85  \n",
       "\n",
       "[392692 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute total amount \n",
    "data['Total_Amount'] = data.Quantity*data.UnitPrice\n",
    "data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RFM Analysis\n",
    "Our data set is now clean and ready for the analysis. We will group the transactions by customers. This will help us compute the **Recency** which is the number of days since the last purchase, the **Frequency** which is the total number of transactions made, and the **Monetary** which is the total amount spent on transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
       "      <th>Frequency</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-18</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>182</td>\n",
       "      <td>2011-12-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>31</td>\n",
       "      <td>2011-09-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "      <td>2011-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>334.40</td>\n",
       "      <td>17</td>\n",
       "      <td>2011-02-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4333</th>\n",
       "      <td>18280.0</td>\n",
       "      <td>180.60</td>\n",
       "      <td>10</td>\n",
       "      <td>2011-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>80.82</td>\n",
       "      <td>7</td>\n",
       "      <td>2011-06-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4335</th>\n",
       "      <td>18282.0</td>\n",
       "      <td>178.05</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>18283.0</td>\n",
       "      <td>2045.53</td>\n",
       "      <td>721</td>\n",
       "      <td>2011-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>18287.0</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>70</td>\n",
       "      <td>2011-10-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4338 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  Monetary  Frequency Last Purchase Date\n",
       "0        12346.0  77183.60          1         2011-01-18\n",
       "1        12347.0   4310.00        182         2011-12-07\n",
       "2        12348.0   1797.24         31         2011-09-25\n",
       "3        12349.0   1757.55         73         2011-11-21\n",
       "4        12350.0    334.40         17         2011-02-02\n",
       "...          ...       ...        ...                ...\n",
       "4333     18280.0    180.60         10         2011-03-07\n",
       "4334     18281.0     80.82          7         2011-06-12\n",
       "4335     18282.0    178.05         12         2011-12-02\n",
       "4336     18283.0   2045.53        721         2011-12-06\n",
       "4337     18287.0   1837.28         70         2011-10-28\n",
       "\n",
       "[4338 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_data=data.groupby('CustomerID', as_index=False)[['Total_Amount', 'InvoiceNo', 'InvoiceDate']].agg({'Total_Amount': 'sum', 'InvoiceNo': 'count', 'InvoiceDate':'max'})\n",
    "grouped_data.columns=['CustomerID','Monetary', 'Frequency', 'Last Purchase Date']\n",
    "grouped_data['Last Purchase Date']=grouped_data['Last Purchase Date'].dt.date\n",
    "grouped_data\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will used the most recent transaction date as our reference point for this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>12347.0</td>\n",
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       "      <td>2011-12-07</td>\n",
       "      <td>2011-12-09</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>1797.24</td>\n",
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       "      <td>12349.0</td>\n",
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       "      <td>2011-11-21</td>\n",
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       "      <td>12350.0</td>\n",
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       "      <td>2011-12-09</td>\n",
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       "      <td>2011-12-09</td>\n",
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       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>18281.0</td>\n",
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       "      <td>2011-12-09</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4338 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  Monetary  Frequency Last Purchase Date Current Date\n",
       "0        12346.0  77183.60          1         2011-01-18   2011-12-09\n",
       "1        12347.0   4310.00        182         2011-12-07   2011-12-09\n",
       "2        12348.0   1797.24         31         2011-09-25   2011-12-09\n",
       "3        12349.0   1757.55         73         2011-11-21   2011-12-09\n",
       "4        12350.0    334.40         17         2011-02-02   2011-12-09\n",
       "...          ...       ...        ...                ...          ...\n",
       "4333     18280.0    180.60         10         2011-03-07   2011-12-09\n",
       "4334     18281.0     80.82          7         2011-06-12   2011-12-09\n",
       "4335     18282.0    178.05         12         2011-12-02   2011-12-09\n",
       "4336     18283.0   2045.53        721         2011-12-06   2011-12-09\n",
       "4337     18287.0   1837.28         70         2011-10-28   2011-12-09\n",
       "\n",
       "[4338 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the last transaction date as the current date of analysis\n",
    "grouped_data['Current Date']= grouped_data['Last Purchase Date'].agg('max')\n",
    "grouped_data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the Recency in days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
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       "      <th>Frequency</th>\n",
       "      <th>Last Purchase Date</th>\n",
       "      <th>Current Date</th>\n",
       "      <th>Recency</th>\n",
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       "      <th>1</th>\n",
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       "      <td>2011-12-07</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
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       "      <td>31</td>\n",
       "      <td>2011-09-25</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "      <td>2011-11-21</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>334.40</td>\n",
       "      <td>17</td>\n",
       "      <td>2011-02-02</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>310</td>\n",
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       "      <th>4333</th>\n",
       "      <td>18280.0</td>\n",
       "      <td>180.60</td>\n",
       "      <td>10</td>\n",
       "      <td>2011-03-07</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>277</td>\n",
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       "      <th>4334</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>80.82</td>\n",
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       "      <td>2011-06-12</td>\n",
       "      <td>2011-12-09</td>\n",
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       "      <td>18282.0</td>\n",
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       "      <td>2011-12-02</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>18283.0</td>\n",
       "      <td>2045.53</td>\n",
       "      <td>721</td>\n",
       "      <td>2011-12-06</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>18287.0</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>70</td>\n",
       "      <td>2011-10-28</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4338 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  Monetary  Frequency Last Purchase Date Current Date  Recency\n",
       "0        12346.0  77183.60          1         2011-01-18   2011-12-09      325\n",
       "1        12347.0   4310.00        182         2011-12-07   2011-12-09        2\n",
       "2        12348.0   1797.24         31         2011-09-25   2011-12-09       75\n",
       "3        12349.0   1757.55         73         2011-11-21   2011-12-09       18\n",
       "4        12350.0    334.40         17         2011-02-02   2011-12-09      310\n",
       "...          ...       ...        ...                ...          ...      ...\n",
       "4333     18280.0    180.60         10         2011-03-07   2011-12-09      277\n",
       "4334     18281.0     80.82          7         2011-06-12   2011-12-09      180\n",
       "4335     18282.0    178.05         12         2011-12-02   2011-12-09        7\n",
       "4336     18283.0   2045.53        721         2011-12-06   2011-12-09        3\n",
       "4337     18287.0   1837.28         70         2011-10-28   2011-12-09       42\n",
       "\n",
       "[4338 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the difference between two dates\n",
    "grouped_data['Recency']= grouped_data['Current Date']-grouped_data['Last Purchase Date']\n",
    "grouped_data['Recency'] = (grouped_data['Recency']/np.timedelta64(1,'D')).map(int)\n",
    "grouped_data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute Recency, Frequency and Monetary Score\n",
    "We are going to create a score for the RFM variables which are Recency, Frequency and Monetary. These scores ranks each customer from 1 to 5 where 5 is the highest rank and 1 is the lowest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>12349.0</td>\n",
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       "      <td>73</td>\n",
       "      <td>2011-11-21</td>\n",
       "      <td>2011-12-09</td>\n",
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       "      <td>10</td>\n",
       "      <td>2011-03-07</td>\n",
       "      <td>2011-12-09</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>80.82</td>\n",
       "      <td>7</td>\n",
       "      <td>2011-06-12</td>\n",
       "      <td>2011-12-09</td>\n",
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       "      <td>2011-12-06</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
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       "      <td>2011-12-09</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "<p>4338 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "      CustomerID  Monetary  Frequency Last Purchase Date Current Date  \\\n",
       "0        12346.0  77183.60          1         2011-01-18   2011-12-09   \n",
       "1        12347.0   4310.00        182         2011-12-07   2011-12-09   \n",
       "2        12348.0   1797.24         31         2011-09-25   2011-12-09   \n",
       "3        12349.0   1757.55         73         2011-11-21   2011-12-09   \n",
       "4        12350.0    334.40         17         2011-02-02   2011-12-09   \n",
       "...          ...       ...        ...                ...          ...   \n",
       "4333     18280.0    180.60         10         2011-03-07   2011-12-09   \n",
       "4334     18281.0     80.82          7         2011-06-12   2011-12-09   \n",
       "4335     18282.0    178.05         12         2011-12-02   2011-12-09   \n",
       "4336     18283.0   2045.53        721         2011-12-06   2011-12-09   \n",
       "4337     18287.0   1837.28         70         2011-10-28   2011-12-09   \n",
       "\n",
       "      Recency  Recency_Score  Frequency_Score  Monetary_Score  \n",
       "0         325              1                1               5  \n",
       "1           2              5                4               4  \n",
       "2          75              2                2               4  \n",
       "3          18              3                3               4  \n",
       "4         310              1                1               2  \n",
       "...       ...            ...              ...             ...  \n",
       "4333      277              1                1               1  \n",
       "4334      180              1                1               1  \n",
       "4335        7              4                1               1  \n",
       "4336        3              4                5               4  \n",
       "4337       42              3                3               4  \n",
       "\n",
       "[4338 rows x 9 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_data['Recency_Score']=((1-grouped_data['Recency'].rank(pct=True)).round(1)*4+1).map(int)\n",
    "grouped_data['Frequency_Score']=(grouped_data['Frequency'].rank(pct=True).round(1)*4+1).map(int)\n",
    "grouped_data['Monetary_Score']=(grouped_data['Monetary'].rank(pct=True).round(1)*4+1).map(int)\n",
    "grouped_data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute RFM Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
       "      <td>12352.0</td>\n",
       "      <td>2506.04</td>\n",
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       "      <td>2011-11-03</td>\n",
       "      <td>2011-12-09</td>\n",
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       "      <td>334</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12353.0</td>\n",
       "      <td>89.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-05-19</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12354.0</td>\n",
       "      <td>1079.40</td>\n",
       "      <td>58</td>\n",
       "      <td>2011-04-21</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>232</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>133</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12355.0</td>\n",
       "      <td>459.40</td>\n",
       "      <td>13</td>\n",
       "      <td>2011-05-09</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12356.0</td>\n",
       "      <td>2811.43</td>\n",
       "      <td>59</td>\n",
       "      <td>2011-11-17</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>334</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12357.0</td>\n",
       "      <td>6207.67</td>\n",
       "      <td>131</td>\n",
       "      <td>2011-11-06</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>345</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12358.0</td>\n",
       "      <td>1168.06</td>\n",
       "      <td>19</td>\n",
       "      <td>2011-12-08</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>523</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12359.0</td>\n",
       "      <td>6310.03</td>\n",
       "      <td>245</td>\n",
       "      <td>2011-10-13</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>57</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>345</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12360.0</td>\n",
       "      <td>2662.06</td>\n",
       "      <td>129</td>\n",
       "      <td>2011-10-18</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>344</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12361.0</td>\n",
       "      <td>189.90</td>\n",
       "      <td>10</td>\n",
       "      <td>2011-02-25</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustomerID  Monetary  Frequency Last Purchase Date Current Date  Recency  \\\n",
       "0      12346.0  77183.60          1         2011-01-18   2011-12-09      325   \n",
       "1      12347.0   4310.00        182         2011-12-07   2011-12-09        2   \n",
       "2      12348.0   1797.24         31         2011-09-25   2011-12-09       75   \n",
       "3      12349.0   1757.55         73         2011-11-21   2011-12-09       18   \n",
       "4      12350.0    334.40         17         2011-02-02   2011-12-09      310   \n",
       "5      12352.0   2506.04         85         2011-11-03   2011-12-09       36   \n",
       "6      12353.0     89.00          4         2011-05-19   2011-12-09      204   \n",
       "7      12354.0   1079.40         58         2011-04-21   2011-12-09      232   \n",
       "8      12355.0    459.40         13         2011-05-09   2011-12-09      214   \n",
       "9      12356.0   2811.43         59         2011-11-17   2011-12-09       22   \n",
       "10     12357.0   6207.67        131         2011-11-06   2011-12-09       33   \n",
       "11     12358.0   1168.06         19         2011-12-08   2011-12-09        1   \n",
       "12     12359.0   6310.03        245         2011-10-13   2011-12-09       57   \n",
       "13     12360.0   2662.06        129         2011-10-18   2011-12-09       52   \n",
       "14     12361.0    189.90         10         2011-02-25   2011-12-09      287   \n",
       "\n",
       "    Recency_Score  Frequency_Score  Monetary_Score RFM_Group  RFM  RFM_Score  \n",
       "0               1                1               5       115    7          2  \n",
       "1               5                4               4       544   13          5  \n",
       "2               2                2               4       224    8          3  \n",
       "3               3                3               4       334   10          3  \n",
       "4               1                1               2       112    4          1  \n",
       "5               3                3               4       334   10          3  \n",
       "6               1                1               1       111    3          1  \n",
       "7               1                3               3       133    7          2  \n",
       "8               1                1               2       112    4          1  \n",
       "9               3                3               4       334   10          3  \n",
       "10              3                4               5       345   12          4  \n",
       "11              5                2               3       523   10          3  \n",
       "12              3                4               5       345   12          4  \n",
       "13              3                4               4       344   11          4  \n",
       "14              1                1               1       111    3          1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_data['RFM_Group'] = grouped_data['Recency_Score'].map(str)+grouped_data['Frequency_Score'].map(str)+grouped_data['Monetary_Score'].map(str)\n",
    "grouped_data['RFM'] = grouped_data['Recency_Score']+grouped_data['Frequency_Score']+grouped_data['Monetary_Score']\n",
    "grouped_data['RFM_Score'] = (grouped_data['RFM'].rank(pct=True).round(1)*4+1).map(int)\n",
    "#grouped_data.sort_values('RFM_Score').tail(490)\n",
    "grouped_data.head(15)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customer Segmentation\n",
    "We will assign a customer to a segment based on their RFM score. A RFM score of 0 corresponds to a *Bronze Customers* and RFM score of 5 corresponds to a *Diamond Customers*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>CustomerID</th>\n",
       "      <th>Recency_Score</th>\n",
       "      <th>Frequency_Score</th>\n",
       "      <th>Monetary_Score</th>\n",
       "      <th>RFM_Group</th>\n",
       "      <th>RFM_Score</th>\n",
       "      <th>Customer_Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>115</td>\n",
       "      <td>2</td>\n",
       "      <td>Silver Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>544</td>\n",
       "      <td>5</td>\n",
       "      <td>Diamond Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>224</td>\n",
       "      <td>3</td>\n",
       "      <td>Gold Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>334</td>\n",
       "      <td>3</td>\n",
       "      <td>Gold Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>Bronze Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4333</th>\n",
       "      <td>18280.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>Bronze Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>Bronze Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4335</th>\n",
       "      <td>18282.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>411</td>\n",
       "      <td>2</td>\n",
       "      <td>Silver Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>18283.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>454</td>\n",
       "      <td>5</td>\n",
       "      <td>Diamond Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>18287.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>334</td>\n",
       "      <td>3</td>\n",
       "      <td>Gold Customers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4338 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  Recency_Score  Frequency_Score  Monetary_Score RFM_Group  \\\n",
       "0        12346.0              1                1               5       115   \n",
       "1        12347.0              5                4               4       544   \n",
       "2        12348.0              2                2               4       224   \n",
       "3        12349.0              3                3               4       334   \n",
       "4        12350.0              1                1               2       112   \n",
       "...          ...            ...              ...             ...       ...   \n",
       "4333     18280.0              1                1               1       111   \n",
       "4334     18281.0              1                1               1       111   \n",
       "4335     18282.0              4                1               1       411   \n",
       "4336     18283.0              4                5               4       454   \n",
       "4337     18287.0              3                3               4       334   \n",
       "\n",
       "      RFM_Score   Customer_Segment  \n",
       "0             2   Silver Customers  \n",
       "1             5  Diamond Customers  \n",
       "2             3     Gold Customers  \n",
       "3             3     Gold Customers  \n",
       "4             1   Bronze Customers  \n",
       "...         ...                ...  \n",
       "4333          1   Bronze Customers  \n",
       "4334          1   Bronze Customers  \n",
       "4335          2   Silver Customers  \n",
       "4336          5  Diamond Customers  \n",
       "4337          3     Gold Customers  \n",
       "\n",
       "[4338 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a function for assigning a  customer to a segment based on their RFM Score\n",
    "def define_segment(rfm_score):\n",
    "    segments = ['Bronze Customers', 'Silver Customers', 'Gold Customers', 'Platinum Customers', 'Diamond Customers']\n",
    "    return segments[rfm_score-1]\n",
    "\n",
    "# assign each customer to a segment\n",
    "grouped_data['Customer_Segment'] = grouped_data.RFM_Score.apply(define_segment)\n",
    "final_result = grouped_data[['CustomerID', 'Recency_Score',\t'Frequency_Score',\t'Monetary_Score', 'RFM_Group', 'RFM_Score',\t'Customer_Segment']]\n",
    "final_result"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Business Metrics per Segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_Segment</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bronze Customers</td>\n",
       "      <td>193.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Diamond Customers</td>\n",
       "      <td>6.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>13665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gold Customers</td>\n",
       "      <td>51.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Platinum Customers</td>\n",
       "      <td>18.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Silver Customers</td>\n",
       "      <td>99.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>765.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer_Segment  Recency  Frequency  Monetary\n",
       "0    Bronze Customers    193.0       14.0     253.0\n",
       "1   Diamond Customers      6.0      489.0   13665.0\n",
       "2      Gold Customers     51.0       74.0    1256.0\n",
       "3  Platinum Customers     18.0      168.0    3600.0\n",
       "4    Silver Customers     99.0       32.0     765.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_data.groupby('Customer_Segment', as_index=False)[['Recency', 'Frequency', 'Monetary']].mean().round()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average our most valuable customers, Diamond Customers, have purchased 489 times and spent $13,665 in this evaluate period. Our customers who are at risk of churning, Bronze Customers, purchased only 14 times and spent $253 on average within the evaluation period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d=final_result.Customer_Segment.value_counts()\n",
    "plt.title('Customer Segmentation', fontsize='x-large')\n",
    "plt.pie(d,labels=d.index,autopct='%.0f%%', explode=[0,0,0,0,0.2])\n",
    "\"\"\"\"\"\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our most valuable customers, **Diamond Customers**, makes up **7%** of our entire customers base. On average, they have purchased 489 times and spent 13,665 USD on average in this evaluate period. Our customers who are at risk of churning, **Bronze Customers**, makes up **28%** of our entire customers base. On average, they made a purchase only 14 times and spent 253 USD within the evaluation period.\n",
    "\n",
    "### Recommendation\n",
    "Based on the RFM Group, customers with high recency, high frequency and high monetary values (5,5,5) are valuable customers. They should be targeted with special promotions to keep them active.\n",
    "Customers with high recency and low frequency value (5,1,X) are new customers. A targeted follow-up may convert them into frequent customers.\n",
    "Customers with low recency but high monetary value (1,X,5) were once valuable customers. A targeted offer may reactivate them."
   ]
  }
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