Trupti_RFM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4945c4d9",
   "metadata": {},
   "source": [
    "# RFM Model LMS Exam"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "998fa9fe",
   "metadata": {},
   "source": [
    "# Import DataSet and Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "eba18186",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import squarify\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "2eb960b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>17850.0</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",
       "  </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     17850.0  United Kingdom       15.30  \n",
       "1  2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
       "2  2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00  \n",
       "3  2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
       "4  2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  "
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(r\"C:\\Users\\TRUPTI\\Desktop\\Trupti_AdvMLExam\\RFM data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0e88545",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "7f7c5a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows in Dataset :  392735\n",
      "Number of Columnd in Dataset : 9\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Rows in Dataset : \",data.shape[0])\n",
    "print(\"Number of Columnd in Dataset :\",data.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "384b2223",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>InvoiceNo</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StockCode</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantity</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InvoiceDate</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UnitPrice</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalPrice</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   0\n",
       "InvoiceNo      int64\n",
       "StockCode     object\n",
       "Description   object\n",
       "Quantity       int64\n",
       "InvoiceDate   object\n",
       "UnitPrice    float64\n",
       "CustomerID   float64\n",
       "Country       object\n",
       "TotalPrice   float64"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Information about datatypes\n",
    "pd.DataFrame(data.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "a36b27cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics fo Dataset :\n",
      "\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>392735.000000</td>\n",
       "      <td>392735.000000</td>\n",
       "      <td>392735.000000</td>\n",
       "      <td>392735.000000</td>\n",
       "      <td>392735.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>560624.318859</td>\n",
       "      <td>10.242645</td>\n",
       "      <td>2.787111</td>\n",
       "      <td>15300.835136</td>\n",
       "      <td>18.246537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13113.076784</td>\n",
       "      <td>16.172009</td>\n",
       "      <td>2.704759</td>\n",
       "      <td>1709.095993</td>\n",
       "      <td>32.558080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>536365.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>12347.000000</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>549233.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13975.000000</td>\n",
       "      <td>4.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>561894.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>15159.000000</td>\n",
       "      <td>11.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>572103.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>16800.000000</td>\n",
       "      <td>19.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>581587.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>19.960000</td>\n",
       "      <td>18287.000000</td>\n",
       "      <td>1914.240000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           InvoiceNo       Quantity      UnitPrice     CustomerID  \\\n",
       "count  392735.000000  392735.000000  392735.000000  392735.000000   \n",
       "mean   560624.318859      10.242645       2.787111   15300.835136   \n",
       "std     13113.076784      16.172009       2.704759    1709.095993   \n",
       "min    536365.000000       1.000000       0.120000   12347.000000   \n",
       "25%    549233.000000       2.000000       1.250000   13975.000000   \n",
       "50%    561894.000000       5.000000       1.950000   15159.000000   \n",
       "75%    572103.000000      12.000000       3.750000   16800.000000   \n",
       "max    581587.000000     147.000000      19.960000   18287.000000   \n",
       "\n",
       "          TotalPrice  \n",
       "count  392735.000000  \n",
       "mean       18.246537  \n",
       "std        32.558080  \n",
       "min         0.120000  \n",
       "25%         4.680000  \n",
       "50%        11.700000  \n",
       "75%        19.500000  \n",
       "max      1914.240000  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Statistics fo Dataset :\\n\")\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "88ea7c39",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['InvoiceDate']=pd.to_datetime(data['InvoiceDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "ec11af2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 392735 entries, 0 to 392734\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    392735 non-null  int64         \n",
      " 1   StockCode    392735 non-null  object        \n",
      " 2   Description  392735 non-null  object        \n",
      " 3   Quantity     392735 non-null  int64         \n",
      " 4   InvoiceDate  392735 non-null  datetime64[ns]\n",
      " 5   UnitPrice    392735 non-null  float64       \n",
      " 6   CustomerID   392735 non-null  float64       \n",
      " 7   Country      392735 non-null  object        \n",
      " 8   TotalPrice   392735 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(3), int64(2), object(3)\n",
      "memory usage: 27.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "463dd015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5182"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "89552146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "392,735 rows; 9 columns\n",
      "0 transactions don't have a customer id\n",
      "Transactions timeframe from 2010-12-01 08:26:00 to 2011-12-09 12:50:00\n"
     ]
    }
   ],
   "source": [
    "print('{:,} rows; {:,} columns'\n",
    "      .format(data.shape[0], data.shape[1]))\n",
    "\n",
    "print('{:,} transactions don\\'t have a customer id'\n",
    "      .format(data[data.CustomerID.isnull()].shape[0]))\n",
    "\n",
    "print('Transactions timeframe from {} to {}'.format(data['InvoiceDate'].min(),\n",
    "                                    data['InvoiceDate'].max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "d46f5c4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Null values :\n",
      "\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",
       "TotalPrice     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Number of Null values :\\n\")\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "7493fe6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# no null values found"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fec1ce29",
   "metadata": {},
   "source": [
    "# Model Building RFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "1089961e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>17850.0</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",
       "  </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     17850.0  United Kingdom       15.30  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "de9a82ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-12-10 12:50:00\n"
     ]
    }
   ],
   "source": [
    "# Create snapshot date\n",
    "snapshot_date = data['InvoiceDate'].max() + timedelta(days=1)\n",
    "print(snapshot_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "ae38d35a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouping by CustomerID\n",
    "data_process=data.groupby(['CustomerID']).agg({\n",
    "    'InvoiceDate' : lambda x:(snapshot_date -x.max()).days,\n",
    "    'InvoiceNo':'count',\n",
    "    'TotalPrice':'sum'})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "13cc4439",
   "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>InvoiceDate</th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "      <td>4060.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>75</td>\n",
       "      <td>27</td>\n",
       "      <td>1437.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>1417.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>310</td>\n",
       "      <td>16</td>\n",
       "      <td>294.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.0</th>\n",
       "      <td>36</td>\n",
       "      <td>77</td>\n",
       "      <td>1385.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            InvoiceDate  InvoiceNo  TotalPrice\n",
       "CustomerID                                    \n",
       "12347.0               2        181     4060.40\n",
       "12348.0              75         27     1437.24\n",
       "12349.0              19         71     1417.60\n",
       "12350.0             310         16      294.40\n",
       "12352.0              36         77     1385.74"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_process.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "9084e9c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the columns \n",
    "data_process.rename(columns={'InvoiceDate': 'Recency',\n",
    "                         'InvoiceNo': 'Frequency',\n",
    "                         'TotalPrice': 'MonetaryValue'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "cb7ef1df",
   "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>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "      <td>4060.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>75</td>\n",
       "      <td>27</td>\n",
       "      <td>1437.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>1417.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>310</td>\n",
       "      <td>16</td>\n",
       "      <td>294.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.0</th>\n",
       "      <td>36</td>\n",
       "      <td>77</td>\n",
       "      <td>1385.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  MonetaryValue\n",
       "CustomerID                                   \n",
       "12347.0           2        181        4060.40\n",
       "12348.0          75         27        1437.24\n",
       "12349.0          19         71        1417.60\n",
       "12350.0         310         16         294.40\n",
       "12352.0          36         77        1385.74"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_process.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "4ec3c631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4298, 9)"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_process.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "0afaa88b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Recency  Frequency  MonetaryValue\n",
      "CustomerID                                   \n",
      "12347.0           2        181        4060.40\n",
      "12348.0          75         27        1437.24\n",
      "12349.0          19         71        1417.60\n",
      "12350.0         310         16         294.40\n",
      "12352.0          36         77        1385.74\n",
      "4,298 rows; 3 columns\n"
     ]
    }
   ],
   "source": [
    "# Print top 5 rows and shape of dataframe\n",
    "print(data_process.head())\n",
    "print('{:,} rows; {:,} columns'.format(data_process.shape[0], data_process.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "a5776ca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot RFM distributions\n",
    "plt.figure(figsize=(12,10))\n",
    "# Plot distribution of R\n",
    "plt.subplot(3, 1, 1); sns.distplot(data_process['Recency'])\n",
    "# Plot distribution of F\n",
    "plt.subplot(3, 1, 2); sns.distplot(data_process['Frequency'])\n",
    "# Plot distribution of M\n",
    "plt.subplot(3, 1, 3); sns.distplot(data_process['MonetaryValue'])\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "571568b5",
   "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>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "      <td>4060.40</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>75</td>\n",
       "      <td>27</td>\n",
       "      <td>1437.24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>1417.60</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>310</td>\n",
       "      <td>16</td>\n",
       "      <td>294.40</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.0</th>\n",
       "      <td>36</td>\n",
       "      <td>77</td>\n",
       "      <td>1385.74</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  MonetaryValue  R  F  M\n",
       "CustomerID                                            \n",
       "12347.0           2        181        4060.40  1  5  5\n",
       "12348.0          75         27        1437.24  4  2  4\n",
       "12349.0          19         71        1417.60  2  4  4\n",
       "12350.0         310         16         294.40  5  2  2\n",
       "12352.0          36         77        1385.74  3  4  4"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#--Calculate R and F groups--\n",
    "# Create labels for Recency  \n",
    "r_labels = range(1, 6)  #5, 4 , 3 , 2 , 1 \n",
    "\n",
    "# Create labels for Frequency\n",
    "\n",
    "f_labels = range(1, 6)      # 1 , 2 ,3 ,4 ,5\n",
    "\n",
    "# Create labels for MonetaryValue\n",
    "m_labels = range(1, 6)  # 1 , 2 ,3 ,4,5\n",
    "\n",
    "\n",
    "# Assign these labels to 4 equal percentile groups \n",
    "r_groups = pd.qcut(data_process['Recency'], q=5, labels=r_labels)\n",
    "\n",
    "\n",
    "# Assign these labels to 4 equal percentile groups \n",
    "f_groups = pd.qcut(data_process['Frequency'], q=5, labels=f_labels)\n",
    "\n",
    "# Assign these labels to three equal percentile groups \n",
    "m_groups = pd.qcut(data_process['MonetaryValue'], q=5, labels=m_labels)\n",
    "\n",
    "\n",
    "# Create new columns R and F \n",
    "data_process = data_process.assign(R = r_groups.values, F = f_groups.values, M = m_groups.values)\n",
    "\n",
    "\n",
    "data_process.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "d09588b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Concat RFM quartile values to create RFM Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "a5a4d75d",
   "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>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>RFM_Segment_Concat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "      <td>4060.40</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>75</td>\n",
       "      <td>27</td>\n",
       "      <td>1437.24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>1417.60</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>310</td>\n",
       "      <td>16</td>\n",
       "      <td>294.40</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.0</th>\n",
       "      <td>36</td>\n",
       "      <td>77</td>\n",
       "      <td>1385.74</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  MonetaryValue  R  F  M RFM_Segment_Concat\n",
       "CustomerID                                                               \n",
       "12347.0           2        181        4060.40  1  5  5                155\n",
       "12348.0          75         27        1437.24  4  2  4                424\n",
       "12349.0          19         71        1417.60  2  4  4                244\n",
       "12350.0         310         16         294.40  5  2  2                522\n",
       "12352.0          36         77        1385.74  3  4  4                344"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_process['RFM_Segment_Concat'] = data_process.R.astype(str) + data_process.F.astype(str) + data_process.M.astype(str)\n",
    "rfm = data_process\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "1a08bc9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count Number of unique Segments : 115\n"
     ]
    }
   ],
   "source": [
    "# Count num of unique segments\n",
    "rfm_count_unique = rfm.groupby('RFM_Segment_Concat')['RFM_Segment_Concat'].nunique()\n",
    "print(\"Count Number of unique Segments :\",rfm_count_unique.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "1f3b9c54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Recency                  int64\n",
       "Frequency                int64\n",
       "MonetaryValue          float64\n",
       "R                     category\n",
       "F                     category\n",
       "M                     category\n",
       "RFM_Segment_Concat      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "b79e3848",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm['R']=rfm['R'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "9ae3c4c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm['F']=rfm['F'].astype(int)\n",
    "rfm['M']=rfm['M'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "ee89833a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Recency                 int64\n",
       "Frequency               int64\n",
       "MonetaryValue         float64\n",
       "R                       int32\n",
       "F                       int32\n",
       "M                       int32\n",
       "RFM_Segment_Concat     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "7b241892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>RFM_Segment_Concat</th>\n",
       "      <th>RFM_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <th>12347.0</th>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "      <td>4060.40</td>\n",
       "      <td>1</td>\n",
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       "      <th>12348.0</th>\n",
       "      <td>75</td>\n",
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       "      <td>1437.24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>424</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>1417.60</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>244</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>310</td>\n",
       "      <td>16</td>\n",
       "      <td>294.40</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>522</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.0</th>\n",
       "      <td>36</td>\n",
       "      <td>77</td>\n",
       "      <td>1385.74</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>344</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  MonetaryValue  R  F  M RFM_Segment_Concat  \\\n",
       "CustomerID                                                                  \n",
       "12347.0           2        181        4060.40  1  5  5                155   \n",
       "12348.0          75         27        1437.24  4  2  4                424   \n",
       "12349.0          19         71        1417.60  2  4  4                244   \n",
       "12350.0         310         16         294.40  5  2  2                522   \n",
       "12352.0          36         77        1385.74  3  4  4                344   \n",
       "\n",
       "            RFM_Score  \n",
       "CustomerID             \n",
       "12347.0            11  \n",
       "12348.0            10  \n",
       "12349.0            10  \n",
       "12350.0             9  \n",
       "12352.0            11  "
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate RFM_Score\n",
    "rfm['RFM_Score'] = rfm[['R','F','M']].sum(axis=1)\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "c26e4f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define rfm_level function\n",
    "def rfm_level(df):\n",
    "    if df['RFM_Score'] >= 12:\n",
    "        return 'Champions'\n",
    "    elif (df['RFM_Score'] >= 9):\n",
    "        return 'Loyal Customers / Need Attention'\n",
    "    elif ((df['RFM_Score'] >= 8) and (df['RFM_Score'] <= 11)):\n",
    "        return \"Potential Loyalist / Can't Lose them\"\n",
    "    elif ((df['RFM_Score'] >= 4) and (df['RFM_Score'] <= 6)):\n",
    "        return 'At Risk'\n",
    "    elif ((df['RFM_Score'] >= 6) and (df['RFM_Score'] <= 9)):\n",
    "        return 'Recent Customers'\n",
    "    elif ((df['RFM_Score'] >= 6) and (df['RFM_Score'] <= 7)):\n",
    "        return 'Promising'\n",
    "    elif ((df['RFM_Score'] >= 3) and (df['RFM_Score'] <= 6)):\n",
    "        return 'About to Sleep'\n",
    "    else:\n",
    "        return 'Hibernating'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "697af2a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>RFM_Segment_Concat</th>\n",
       "      <th>RFM_Score</th>\n",
       "      <th>Customer_Segment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
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       "      <td>Loyal Customers / Need Attention</td>\n",
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       "      <th>12348.0</th>\n",
       "      <td>75</td>\n",
       "      <td>27</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>424</td>\n",
       "      <td>10</td>\n",
       "      <td>Loyal Customers / Need Attention</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>1417.60</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>244</td>\n",
       "      <td>10</td>\n",
       "      <td>Loyal Customers / Need Attention</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>310</td>\n",
       "      <td>16</td>\n",
       "      <td>294.40</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>522</td>\n",
       "      <td>9</td>\n",
       "      <td>Loyal Customers / Need Attention</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.0</th>\n",
       "      <td>36</td>\n",
       "      <td>77</td>\n",
       "      <td>1385.74</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>344</td>\n",
       "      <td>11</td>\n",
       "      <td>Loyal Customers / Need Attention</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  MonetaryValue  R  F  M RFM_Segment_Concat  \\\n",
       "CustomerID                                                                  \n",
       "12347.0           2        181        4060.40  1  5  5                155   \n",
       "12348.0          75         27        1437.24  4  2  4                424   \n",
       "12349.0          19         71        1417.60  2  4  4                244   \n",
       "12350.0         310         16         294.40  5  2  2                522   \n",
       "12352.0          36         77        1385.74  3  4  4                344   \n",
       "\n",
       "            RFM_Score                  Customer_Segment  \n",
       "CustomerID                                               \n",
       "12347.0            11  Loyal Customers / Need Attention  \n",
       "12348.0            10  Loyal Customers / Need Attention  \n",
       "12349.0            10  Loyal Customers / Need Attention  \n",
       "12350.0             9  Loyal Customers / Need Attention  \n",
       "12352.0            11  Loyal Customers / Need Attention  "
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new variable RFM_Level\n",
    "rfm['Customer_Segment'] = rfm.apply(rfm_level, axis=1)\n",
    "# Print the header with top 5 rows to the console\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "54585c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer_Segment\n",
       "Loyal Customers / Need Attention        1921\n",
       "Recent Customers                         662\n",
       "At Risk                                  583\n",
       "Champions                                576\n",
       "Potential Loyalist / Can't Lose them     520\n",
       "About to Sleep                            36\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.Customer_Segment.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "f07b12b8",
   "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 tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">MonetaryValue</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>Customer_Segment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>About to Sleep</th>\n",
       "      <td>6.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>147.8</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>At Risk</th>\n",
       "      <td>49.9</td>\n",
       "      <td>14.1</td>\n",
       "      <td>231.0</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Champions</th>\n",
       "      <td>89.0</td>\n",
       "      <td>180.6</td>\n",
       "      <td>3279.1</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loyal Customers / Need Attention</th>\n",
       "      <td>83.5</td>\n",
       "      <td>130.5</td>\n",
       "      <td>2421.6</td>\n",
       "      <td>1921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potential Loyalist / Can't Lose them</th>\n",
       "      <td>112.7</td>\n",
       "      <td>32.2</td>\n",
       "      <td>539.5</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recent Customers</th>\n",
       "      <td>148.9</td>\n",
       "      <td>19.3</td>\n",
       "      <td>309.6</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Recency Frequency MonetaryValue      \n",
       "                                        mean      mean          mean count\n",
       "Customer_Segment                                                          \n",
       "About to Sleep                           6.8       6.9         147.8    36\n",
       "At Risk                                 49.9      14.1         231.0   583\n",
       "Champions                               89.0     180.6        3279.1   576\n",
       "Loyal Customers / Need Attention        83.5     130.5        2421.6  1921\n",
       "Potential Loyalist / Can't Lose them   112.7      32.2         539.5   520\n",
       "Recent Customers                       148.9      19.3         309.6   662"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate average values for each RFM_Level, and return a size of each segment \n",
    "rfm_level_agg = rfm.groupby('Customer_Segment').agg({\n",
    "    'Recency': 'mean',\n",
    "    'Frequency': 'mean',\n",
    "    'MonetaryValue': ['mean', 'count']\n",
    "}).round(1)\n",
    "# Print the aggregated dataset\n",
    "rfm_level_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "a245c740",
   "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>RecencyMean</th>\n",
       "      <th>FrequencyMean</th>\n",
       "      <th>MonetaryMean</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer_Segment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>About to Sleep</th>\n",
       "      <td>6.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>147.8</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>At Risk</th>\n",
       "      <td>49.9</td>\n",
       "      <td>14.1</td>\n",
       "      <td>231.0</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Champions</th>\n",
       "      <td>89.0</td>\n",
       "      <td>180.6</td>\n",
       "      <td>3279.1</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loyal Customers / Need Attention</th>\n",
       "      <td>83.5</td>\n",
       "      <td>130.5</td>\n",
       "      <td>2421.6</td>\n",
       "      <td>1921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potential Loyalist / Can't Lose them</th>\n",
       "      <td>112.7</td>\n",
       "      <td>32.2</td>\n",
       "      <td>539.5</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recent Customers</th>\n",
       "      <td>148.9</td>\n",
       "      <td>19.3</td>\n",
       "      <td>309.6</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      RecencyMean  FrequencyMean  \\\n",
       "Customer_Segment                                                   \n",
       "About to Sleep                                6.8            6.9   \n",
       "At Risk                                      49.9           14.1   \n",
       "Champions                                    89.0          180.6   \n",
       "Loyal Customers / Need Attention             83.5          130.5   \n",
       "Potential Loyalist / Can't Lose them        112.7           32.2   \n",
       "Recent Customers                            148.9           19.3   \n",
       "\n",
       "                                      MonetaryMean  Count  \n",
       "Customer_Segment                                           \n",
       "About to Sleep                               147.8     36  \n",
       "At Risk                                      231.0    583  \n",
       "Champions                                   3279.1    576  \n",
       "Loyal Customers / Need Attention            2421.6   1921  \n",
       "Potential Loyalist / Can't Lose them         539.5    520  \n",
       "Recent Customers                             309.6    662  "
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_level_agg.columns = rfm_level_agg.columns.droplevel()\n",
    "rfm_level_agg.columns = ['RecencyMean','FrequencyMean','MonetaryMean', 'Count']\n",
    "rfm_level_agg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8479c20e",
   "metadata": {},
   "source": [
    "# Pareto Principal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "b0bd15e5",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer_Segment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>About to Sleep</th>\n",
       "      <td>5322.41</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>At Risk</th>\n",
       "      <td>134660.61</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Champions</th>\n",
       "      <td>1888735.27</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loyal Customers / Need Attention</th>\n",
       "      <td>4651871.29</td>\n",
       "      <td>1921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potential Loyalist / Can't Lose them</th>\n",
       "      <td>280520.88</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recent Customers</th>\n",
       "      <td>204943.07</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               sum           len\n",
       "                                     MonetaryValue MonetaryValue\n",
       "Customer_Segment                                                \n",
       "About to Sleep                             5322.41            36\n",
       "At Risk                                  134660.61           583\n",
       "Champions                               1888735.27           576\n",
       "Loyal Customers / Need Attention        4651871.29          1921\n",
       "Potential Loyalist / Can't Lose them     280520.88           520\n",
       "Recent Customers                         204943.07           662"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pareto=pd.pivot_table(rfm,index=[\"Customer_Segment\"],values=[\"MonetaryValue\"],aggfunc=[np.sum,len])\n",
    "Pareto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "ec5b386c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Name of Customer_Segment')"
      ]
     },
     "execution_count": 333,
     "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": [
    "rfm[\"Customer_Segment\"].value_counts().head(10).plot.pie(autopct=\"%.1f%%\") # Top 10 Times of the day at which the most number of messages were sent\n",
    "plt.ylabel('Count of Segment')\n",
    "plt.xlabel('Name of Customer_Segment')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e00beb44",
   "metadata": {},
   "source": [
    "# Interpetation\n",
    "13.4% of Customers are Champions.\n",
    "\n",
    "44.7% ( 1921 / 4298 ) of customer give 83% profit which require more attention.\n",
    "\n",
    "15% ( 662/ 4298 ) of customer have not recent from last 4 month ( 149 / 365 ).\n",
    "\n",
    "13.6% of customers are at Risk.\n",
    "\n",
    "12 % of Customer are Potential Loyalist.\n",
    "\n",
    "0.8% of customers about to sleep.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4cf998e",
   "metadata": {},
   "source": [
    "# Conclusion :\n",
    "\n",
    "Loyal Cutomers are the most important customer for bussiness point. whose gives more profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddaba8c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}