segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f61af28b-77e7-4d49-a5a1-e3435a04601f",
   "metadata": {},
   "source": [
    "## 1. Load the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "311f53b7-ebab-4dd7-aedf-2ef6cf501a1c",
   "metadata": {},
   "source": [
    "### Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98441cad-1b71-4644-b1fe-33b186d7d83a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb5a49b5-3d7d-4a61-90a4-f4e3a6b368a7",
   "metadata": {},
   "source": [
    "### Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de467da7-1196-46b9-8a7e-e126afac7c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Online Retail.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "099723cb-bf97-4a3f-b87b-c317725d0b5d",
   "metadata": {},
   "source": [
    "## 2. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3baa9cc5-b8fa-494d-844c-371ad0b686c6",
   "metadata": {},
   "source": [
    "### Inspect the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f766a40-2e1a-4374-99ce-73f93d0fbb81",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f44c4c36-7995-40ea-967a-beb11eebd5d4",
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eac20d6d-108c-41e7-9073-bb356c578229",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
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       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>2011-07-04 13:34:57.156386048</td>\n",
       "      <td>4.611114</td>\n",
       "      <td>15287.690570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>-11062.060000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2011-03-28 11:34:00</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2011-07-19 17:17:00</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>15152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>2011-10-19 11:27:00</td>\n",
       "      <td>4.130000</td>\n",
       "      <td>16791.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.759853</td>\n",
       "      <td>1713.600303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity                    InvoiceDate      UnitPrice  \\\n",
       "count  541909.000000                         541909  541909.000000   \n",
       "mean        9.552250  2011-07-04 13:34:57.156386048       4.611114   \n",
       "min    -80995.000000            2010-12-01 08:26:00  -11062.060000   \n",
       "25%         1.000000            2011-03-28 11:34:00       1.250000   \n",
       "50%         3.000000            2011-07-19 17:17:00       2.080000   \n",
       "75%        10.000000            2011-10-19 11:27:00       4.130000   \n",
       "max     80995.000000            2011-12-09 12:50:00   38970.000000   \n",
       "std       218.081158                            NaN      96.759853   \n",
       "\n",
       "          CustomerID  \n",
       "count  406829.000000  \n",
       "mean    15287.690570  \n",
       "min     12346.000000  \n",
       "25%     13953.000000  \n",
       "50%     15152.000000  \n",
       "75%     16791.000000  \n",
       "max     18287.000000  \n",
       "std      1713.600303  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e5e5cfa-c93e-4f2b-ae3c-17447a9ab794",
   "metadata": {},
   "source": [
    "### Remove Null Values in the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef50dd19-346d-46b7-b0a9-e4652c944b94",
   "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": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "28071aa4-ac69-4870-81c1-93c1d2561f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset=['CustomerID'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d3604be-8dcc-42ab-ac1d-98bc4fcfb04c",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a311fb-e18a-46ed-b39b-5e6779be34d5",
   "metadata": {},
   "source": [
    "### Remove Negative and Zero Values in Quantity and UnitPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e3cc4b2-c00d-4e3a-91fd-eeb904661016",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>406829.000000</td>\n",
       "      <td>406829</td>\n",
       "      <td>406829.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.061303</td>\n",
       "      <td>2011-07-10 16:30:57.879207424</td>\n",
       "      <td>3.460471</td>\n",
       "      <td>15287.690570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>2010-12-01 08:26:00</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>2011-04-06 15:02:00</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2011-07-31 11:48:00</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>15152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>2011-10-20 13:06:00</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>2011-12-09 12:50:00</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>248.693370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.315162</td>\n",
       "      <td>1713.600303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity                    InvoiceDate      UnitPrice  \\\n",
       "count  406829.000000                         406829  406829.000000   \n",
       "mean       12.061303  2011-07-10 16:30:57.879207424       3.460471   \n",
       "min    -80995.000000            2010-12-01 08:26:00       0.000000   \n",
       "25%         2.000000            2011-04-06 15:02:00       1.250000   \n",
       "50%         5.000000            2011-07-31 11:48:00       1.950000   \n",
       "75%        12.000000            2011-10-20 13:06:00       3.750000   \n",
       "max     80995.000000            2011-12-09 12:50:00   38970.000000   \n",
       "std       248.693370                            NaN      69.315162   \n",
       "\n",
       "          CustomerID  \n",
       "count  406829.000000  \n",
       "mean    15287.690570  \n",
       "min     12346.000000  \n",
       "25%     13953.000000  \n",
       "50%     15152.000000  \n",
       "75%     16791.000000  \n",
       "max     18287.000000  \n",
       "std      1713.600303  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dfaf0994-d6e5-4dfc-8c76-b78a7eba0cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['Quantity'] > 0) & (df['UnitPrice'] > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "379ee356-bdaa-473a-a9cf-32fc0075c729",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>397884.000000</td>\n",
       "      <td>397884</td>\n",
       "      <td>397884.000000</td>\n",
       "      <td>397884.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.988238</td>\n",
       "      <td>2011-07-10 23:41:23.511023360</td>\n",
       "      <td>3.116488</td>\n",
       "      <td>15294.423453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2011-04-07 11:12:00</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>2011-07-31 14:39:00</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>15159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>2011-10-20 14:33:00</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>16795.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>8142.750000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>179.331775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.097877</td>\n",
       "      <td>1713.141560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity                    InvoiceDate      UnitPrice  \\\n",
       "count  397884.000000                         397884  397884.000000   \n",
       "mean       12.988238  2011-07-10 23:41:23.511023360       3.116488   \n",
       "min         1.000000            2010-12-01 08:26:00       0.001000   \n",
       "25%         2.000000            2011-04-07 11:12:00       1.250000   \n",
       "50%         6.000000            2011-07-31 14:39:00       1.950000   \n",
       "75%        12.000000            2011-10-20 14:33:00       3.750000   \n",
       "max     80995.000000            2011-12-09 12:50:00    8142.750000   \n",
       "std       179.331775                            NaN      22.097877   \n",
       "\n",
       "          CustomerID  \n",
       "count  397884.000000  \n",
       "mean    15294.423453  \n",
       "min     12346.000000  \n",
       "25%     13969.000000  \n",
       "50%     15159.000000  \n",
       "75%     16795.000000  \n",
       "max     18287.000000  \n",
       "std      1713.141560  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b6c1d7d-78b6-4e73-9f37-a82771176bba",
   "metadata": {},
   "source": [
    "### Convert CustomerID to an Integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a7007aba-669e-45c7-8fb8-9691ab88e3cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CustomerID'] = df['CustomerID'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "798b33e5-3f94-467b-942f-8ddfcb4e4cf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo              object\n",
       "StockCode              object\n",
       "Description            object\n",
       "Quantity                int64\n",
       "InvoiceDate    datetime64[ns]\n",
       "UnitPrice             float64\n",
       "CustomerID              int64\n",
       "Country                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e33ad7d-e170-499d-a6eb-830605584c9a",
   "metadata": {},
   "source": [
    "## 3. Calculate RFM Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8af75d29-95ca-4ca5-9bce-55543825bcd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set a reference date as the most recent data in the dataset\n",
    "reference_date = df['InvoiceDate'].max() + pd.DateOffset(days=1)\n",
    "\n",
    "# Create TotalSpend column\n",
    "df['TotalSpend'] = df['Quantity'] * df['UnitPrice']\n",
    "\n",
    "# Create RFM DataFrame\n",
    "rfm = df.groupby('CustomerID').agg({\n",
    "    'InvoiceDate': lambda x: (reference_date - x.max()).days, # Recency\n",
    "    'InvoiceNo': 'nunique', # Frequency\n",
    "    'TotalSpend': 'sum' # Monetary\n",
    "}).rename(columns={\n",
    "    'InvoiceDate': 'Recency',\n",
    "    'InvoiceNo': 'Frequency',\n",
    "    'TotalSpend': 'Monetary'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dbd1e8f1-2061-4b21-bfff-8b731a254de3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</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>12346</th>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  Monetary\n",
       "CustomerID                              \n",
       "12346           326          1  77183.60\n",
       "12347             2          7   4310.00\n",
       "12348            75          4   1797.24\n",
       "12349            19          1   1757.55\n",
       "12350           310          1    334.40"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90142900-2b87-4319-b6a6-febde5a88cbe",
   "metadata": {},
   "source": [
    "## 4. Binning for RFM Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fef1bca1-964d-40b2-b40e-47826e9758fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4338.000000</td>\n",
       "      <td>4338.000000</td>\n",
       "      <td>4338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>92.536422</td>\n",
       "      <td>4.272015</td>\n",
       "      <td>2054.266460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>100.014169</td>\n",
       "      <td>7.697998</td>\n",
       "      <td>8989.230441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>307.415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>674.485000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>142.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1661.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>374.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>280206.020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Recency    Frequency       Monetary\n",
       "count  4338.000000  4338.000000    4338.000000\n",
       "mean     92.536422     4.272015    2054.266460\n",
       "std     100.014169     7.697998    8989.230441\n",
       "min       1.000000     1.000000       3.750000\n",
       "25%      18.000000     1.000000     307.415000\n",
       "50%      51.000000     2.000000     674.485000\n",
       "75%     142.000000     5.000000    1661.740000\n",
       "max     374.000000   209.000000  280206.020000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bc1b0080-7441-4daa-8748-294c3af81ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "recency_bins = [0, 20, 50, 150, 250, 400]\n",
    "frequency_bins = [0, 2, 3, 10, 50, 250]\n",
    "monetary_bins = [0, 300, 700, 2000, 5000, 280210]\n",
    "\n",
    "rfm['R_Score'] = pd.cut(rfm['Recency'], bins=recency_bins, labels=[5, 4, 3, 2, 1], right=False).astype(int)\n",
    "rfm['F_Score'] = pd.cut(rfm['Frequency'], bins=frequency_bins, labels=[1, 2, 3, 4, 5], right=False).astype(int)\n",
    "rfm['M_Score'] = pd.cut(rfm['Monetary'], bins=monetary_bins, labels=[1, 2, 3, 4, 5], right=False).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3e8622bb-a949-4e23-b83a-5e11d5023ddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .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>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</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>12346</th>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  Monetary  R_Score  F_Score  M_Score\n",
       "CustomerID                                                         \n",
       "12346           326          1  77183.60        1        1        5\n",
       "12347             2          7   4310.00        5        3        4\n",
       "12348            75          4   1797.24        3        3        3\n",
       "12349            19          1   1757.55        5        1        3\n",
       "12350           310          1    334.40        1        1        2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100baca4-60be-48c8-ab93-aab8349ff3eb",
   "metadata": {},
   "source": [
    "## 5. K-Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293feeea-5da3-4b16-bdb6-c61b7710b7fb",
   "metadata": {},
   "source": [
    "### Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e83cdd0b-af1c-4172-bb4c-e588ecf19e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfm_kmeans = rfm[['R_Score', 'F_Score', 'M_Score']]\n",
    "\n",
    "wcss = []\n",
    "K_range = range(1, 10) \n",
    "for k in K_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(rfm_kmeans)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "# Plot the elbow graph\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(K_range, wcss, marker='o')\n",
    "plt.title('Elbow Method to Determine Optimal K')\n",
    "plt.xlabel('Number of Clusters (K)')\n",
    "plt.ylabel('Within-Cluster Sum of Squares (WCSS)')\n",
    "plt.savefig('Elbow-Method.png', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571e8ea7-b603-487b-8a76-ebb46ace60b0",
   "metadata": {},
   "source": [
    "### Fit the K-Means Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "561fe7db-b142-4615-9b3c-d7b356f16b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose the optimal K based on the Elbow Method graph\n",
    "optimal_k = 3\n",
    "\n",
    "# Apply k_means clustering with optimal_k as n_clusters\n",
    "kmeans = KMeans(n_clusters=optimal_k, random_state = 42)\n",
    "rfm['Cluster'] = kmeans.fit_predict(rfm_kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "90fedd72-e98b-48ae-8e06-f9139b02b6f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>Monetary</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</th>\n",
       "      <th>Cluster</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>12346</th>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352</th>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>2506.04</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12353</th>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "      <td>89.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12354</th>\n",
       "      <td>232</td>\n",
       "      <td>1</td>\n",
       "      <td>1079.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12355</th>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>459.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12356</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2811.43</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12357</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>6207.67</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12358</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1168.06</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12359</th>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>6372.58</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12360</th>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>2662.06</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12361</th>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>189.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  Monetary  R_Score  F_Score  M_Score  Cluster\n",
       "CustomerID                                                                  \n",
       "12346           326          1  77183.60        1        1        5        1\n",
       "12347             2          7   4310.00        5        3        4        2\n",
       "12348            75          4   1797.24        3        3        3        2\n",
       "12349            19          1   1757.55        5        1        3        0\n",
       "12350           310          1    334.40        1        1        2        1\n",
       "12352            36          8   2506.04        4        3        4        2\n",
       "12353           204          1     89.00        2        1        1        1\n",
       "12354           232          1   1079.40        2        1        3        1\n",
       "12355           214          1    459.40        2        1        2        1\n",
       "12356            23          3   2811.43        4        3        4        2\n",
       "12357            33          1   6207.67        4        1        5        2\n",
       "12358             2          2   1168.06        5        2        3        0\n",
       "12359            58          4   6372.58        3        3        5        2\n",
       "12360            52          3   2662.06        3        3        4        2\n",
       "12361           287          1    189.90        1        1        1        1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8bfa147-61db-4e0f-8ee9-bfa6f1eae9d0",
   "metadata": {},
   "source": [
    "### Visualize the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "647e6818-36fc-4b41-b092-de697cb35a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary = rfm.groupby('Cluster').agg({\n",
    "    'R_Score': 'mean',\n",
    "    'F_Score': 'mean',\n",
    "    'M_Score': 'mean',\n",
    "}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "abde6d75-7423-4a9b-b9f7-87dd8b262ce6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Cluster   R_Score   F_Score   M_Score\n",
      "0        0  4.441527  1.779236  1.795943\n",
      "1        1  2.164294  1.484118  1.780394\n",
      "2        2  4.244922  3.225209  3.688172\n"
     ]
    }
   ],
   "source": [
    "print(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "97bb614f-587d-42d0-975c-ca37e2addf30",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x3000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#FFD166', '#8AC6D0', '#ACE1AF']\n",
    "\n",
    "fig, axes = plt.subplots(3, 1, figsize=(10, 15), dpi=200)\n",
    "legend_patches = [mpatches.Patch(color=colors[i], label=f'Cluster {i}') for i in summary.index]\n",
    "\n",
    "axes[0].bar(summary.index, summary['R_Score'], color=colors)\n",
    "axes[0].set_title('Average Recency per Cluster (K-Means)')\n",
    "axes[0].set_xlabel('Cluster')\n",
    "axes[0].set_ylabel('Recency')\n",
    "axes[0].legend(handles=legend_patches, title='Clusters')\n",
    "\n",
    "axes[1].bar(summary.index, summary['F_Score'], color=colors)\n",
    "axes[1].set_title('Average Frequency per Cluster (K-Means)')\n",
    "axes[1].set_xlabel('Cluster')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "axes[1].legend(handles=legend_patches, title='Clusters')\n",
    "\n",
    "axes[2].bar(summary.index, summary['M_Score'], color=colors)\n",
    "axes[2].set_title('Average Monetary Value per Cluster (K-Means)')\n",
    "axes[2].set_xlabel('Cluster')\n",
    "axes[2].set_ylabel('Monetary')\n",
    "axes[2].legend(handles=legend_patches, title='Clusters')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('rfm_summary.png', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "11d666fe-3f10-493d-8a41-50d856d97c1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster\n",
      "1    1826\n",
      "2    1674\n",
      "0     838\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(rfm['Cluster'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4db539ad-873a-4521-b897-5eb0c3163f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the number of customers in each cluster\n",
    "cluster_counts = rfm['Cluster'].value_counts()\n",
    "\n",
    "# Map the cluster to the desired names\n",
    "cluster_names = {0:\"Recent Customers\", 1:\"Occasional Customers\", 2:\"Champions\"}\n",
    "cluster_counts.index = cluster_counts.index.map(cluster_names)\n",
    "\n",
    "# Calculate the percentage of customers in each cluster\n",
    "total_customers = cluster_counts.sum()\n",
    "percentage_customers = (cluster_counts / total_customers) * 100\n",
    "\n",
    "# Create a horizontal bar chart\n",
    "plt.figure(figsize=(14, 6), dpi=200)\n",
    "\n",
    "# Define the custom color list correct matching\n",
    "colors = {\n",
    "    'Recent Customers': '#FFD166', \n",
    "    'Occasional Customers':'#8AC6D0', \n",
    "    'Champions':'#ACE1AF'\n",
    "}\n",
    "\n",
    "# Map the color to each cluster based on it's name\n",
    "bar_colors = [colors[cluster_name] for cluster_name in cluster_counts.index]\n",
    "\n",
    "# Plot the horizontal bar chart\n",
    "plt.barh(cluster_counts.index, cluster_counts, color=bar_colors)\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Customer Clusters by Count and Percentage', fontsize=14)\n",
    "plt.xlabel('Customer Count', fontsize=12)\n",
    "plt.ylabel('Customer Clusters', fontsize=12)\n",
    "\n",
    "# Show the percentage and count values on the bars\n",
    "for index, (count, percentage) in enumerate(zip(cluster_counts, percentage_customers)):\n",
    "    plt.text(count + 10, index, f'{count} ({percentage:.1f}%)', va='center', fontsize=10, color='black')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig('customer-clusters.png', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba49153-4c45-441a-984b-140730e460b7",
   "metadata": {},
   "source": [
    "## 6. Rule-Based Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "339cb760-2453-4da9-8bdf-41ca9bed45e1",
   "metadata": {},
   "source": [
    "### Drop the 'Cluster' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3937ee8a-b8c7-4166-9364-dc4843ced5b7",
   "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>Monetary</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</th>\n",
       "      <th>Cluster</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>12346</th>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  Monetary  R_Score  F_Score  M_Score  Cluster\n",
       "CustomerID                                                                  \n",
       "12346           326          1  77183.60        1        1        5        1\n",
       "12347             2          7   4310.00        5        3        4        2\n",
       "12348            75          4   1797.24        3        3        3        2\n",
       "12349            19          1   1757.55        5        1        3        0\n",
       "12350           310          1    334.40        1        1        2        1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a469b020-f776-482c-8722-36880321e4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm = rfm.drop('Cluster', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "737e87ce-1fed-4c16-a03b-b15cd8aab91c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</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>12346</th>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  Monetary  R_Score  F_Score  M_Score\n",
       "CustomerID                                                         \n",
       "12346           326          1  77183.60        1        1        5\n",
       "12347             2          7   4310.00        5        3        4\n",
       "12348            75          4   1797.24        3        3        3\n",
       "12349            19          1   1757.55        5        1        3\n",
       "12350           310          1    334.40        1        1        2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d9a8323-6ca6-467e-84a7-d4306122acbd",
   "metadata": {},
   "source": [
    "### Create the Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "069fe12d-0f3c-41ce-9482-0a1d096d968c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rfm_segmentation(row):\n",
    "    if row['R_Score'] == 5 and row['F_Score'] == 5 and row['M_Score'] == 5:\n",
    "        return 'Elites'\n",
    "    if row['R_Score'] >= 4 and row['F_Score'] >= 4 and row['M_Score'] >= 4:\n",
    "        return 'Champions'  \n",
    "    elif row['R_Score'] >= 4 and row['F_Score'] >= 3 and row['M_Score'] >= 3:\n",
    "        return 'Loyal Customers'\n",
    "    elif row['R_Score'] >= 3 and row['F_Score'] >= 3 and row['M_Score'] >= 3:\n",
    "        return 'Regular Customers'\n",
    "    elif row['R_Score'] <= 3 and row['F_Score'] <= 3 and row['M_Score'] >= 4:\n",
    "        return 'Occasional High Spenders'\n",
    "    elif row['R_Score'] <= 3 and row['F_Score'] <= 3 and row['M_Score'] <= 3:\n",
    "        return 'Occasional Customers'\n",
    "    elif row['R_Score'] >= 4 and row['F_Score'] >= 1 and row['M_Score'] >= 1:\n",
    "        return 'Recent Customers'\n",
    "    elif row['R_Score'] >= 1 and row['F_Score'] >= 1 and row['M_Score'] >= 1:\n",
    "        return 'At-Risk Customers'\n",
    "    else:\n",
    "        return 'Others'\n",
    "\n",
    "# Apply segmentation to the RFM data\n",
    "rfm['Segment'] = rfm.apply(rfm_segmentation, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "554b97fa-d7bb-492d-8baf-a3f66ddf89c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segment\n",
      "Occasional Customers        1801\n",
      "Loyal Customers              915\n",
      "Recent Customers             854\n",
      "Regular Customers            360\n",
      "Champions                    340\n",
      "Occasional High Spenders      49\n",
      "Elites                        15\n",
      "At-Risk Customers              4\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(rfm['Segment'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ce047712-7d5d-4bcf-838f-2b81846c9dc3",
   "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>Monetary</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</th>\n",
       "      <th>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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346</th>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Occasional High Spenders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Loyal Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Regular Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Recent Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Occasional Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352</th>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>2506.04</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Loyal Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12353</th>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "      <td>89.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Occasional Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12354</th>\n",
       "      <td>232</td>\n",
       "      <td>1</td>\n",
       "      <td>1079.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Occasional Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12355</th>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>459.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Occasional Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12356</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2811.43</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Loyal Customers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  Monetary  R_Score  F_Score  M_Score  \\\n",
       "CustomerID                                                            \n",
       "12346           326          1  77183.60        1        1        5   \n",
       "12347             2          7   4310.00        5        3        4   \n",
       "12348            75          4   1797.24        3        3        3   \n",
       "12349            19          1   1757.55        5        1        3   \n",
       "12350           310          1    334.40        1        1        2   \n",
       "12352            36          8   2506.04        4        3        4   \n",
       "12353           204          1     89.00        2        1        1   \n",
       "12354           232          1   1079.40        2        1        3   \n",
       "12355           214          1    459.40        2        1        2   \n",
       "12356            23          3   2811.43        4        3        4   \n",
       "\n",
       "                             Segment  \n",
       "CustomerID                            \n",
       "12346       Occasional High Spenders  \n",
       "12347                Loyal Customers  \n",
       "12348              Regular Customers  \n",
       "12349               Recent Customers  \n",
       "12350           Occasional Customers  \n",
       "12352                Loyal Customers  \n",
       "12353           Occasional Customers  \n",
       "12354           Occasional Customers  \n",
       "12355           Occasional Customers  \n",
       "12356                Loyal Customers  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102143a3-da79-4413-9b16-614f37efc41f",
   "metadata": {},
   "source": [
    "### Visualize the Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5d0cd806-1ea7-4a55-9688-621befc748b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the number of customers in each segment\n",
    "segment_counts = rfm['Segment'].value_counts()\n",
    "\n",
    "# Calculate the percentage of customers in each segment\n",
    "total_customers = segment_counts.sum()\n",
    "percentage_customers = (segment_counts / total_customers) * 100\n",
    "\n",
    "# Create a horizontal bar chart with different colors\n",
    "plt.figure(figsize=(14, 6), dpi=200)\n",
    "colors = plt.cm.viridis(np.linspace(0, 1, len(segment_counts)))\n",
    "\n",
    "# Plot the horizontal bar chart\n",
    "plt.barh(segment_counts.index, segment_counts, color=colors)\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Customer Segments by Count and Percentage', fontsize=14)\n",
    "plt.xlabel('Customer Count', fontsize=12)\n",
    "plt.ylabel('Customer Segments', fontsize=12)\n",
    "\n",
    "# Show the percentage and count values on the bars\n",
    "for index, (count, percentage) in enumerate(zip(segment_counts, percentage_customers)):\n",
    "    plt.text(count + 10, index, f'{count} ({percentage:.1f}%)', va='center', fontsize=10, color='black')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"segments-rule-based.png\", transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "414ce937-809e-40f5-8452-48f0e959c47a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_1",
   "language": "python",
   "name": "conda_1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}