customer_segmentation_w_rfm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e4e858a",
   "metadata": {},
   "source": [
    "## Story of dataset:\n",
    "\n",
    "### InvoiceNo: Invoice numbers are unique numbers, (canceled transactions starting with C)\n",
    "### StockCode: Product code, they are unique numbers.\n",
    "### Description: Product name\n",
    "### Quantity: Number of products\n",
    "### InvoiceDate: Invoice date and time\n",
    "### UnitPrice: Product price, (Sterling £)\n",
    "### CustomerID: Customer number, (Unique)\n",
    "### Country: Country where the customer lives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8be48dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime as dt\n",
    "\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"display.float_format\", lambda x: \"%.3f\" % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4751dfe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = pd.read_excel(r\"C:\\Users\\Atakan\\Desktop\\m\\crm_analitigi\\online_retail_II.xlsx\", sheet_name=\"Year 2009-2010\")\n",
    "df = df_.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "731ac505",
   "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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.950</td>\n",
       "      <td>13085.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.750</td>\n",
       "      <td>13085.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.750</td>\n",
       "      <td>13085.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.100</td>\n",
       "      <td>13085.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.250</td>\n",
       "      <td>13085.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "          InvoiceDate  Price  Customer ID         Country  \n",
       "0 2009-12-01 07:45:00  6.950    13085.000  United Kingdom  \n",
       "1 2009-12-01 07:45:00  6.750    13085.000  United Kingdom  \n",
       "2 2009-12-01 07:45:00  6.750    13085.000  United Kingdom  \n",
       "3 2009-12-01 07:45:00  2.100    13085.000  United Kingdom  \n",
       "4 2009-12-01 07:45:00  1.250    13085.000  United Kingdom  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1989dde6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(525461, 8)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9c8045b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 525461 entries, 0 to 525460\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   Invoice      525461 non-null  object        \n",
      " 1   StockCode    525461 non-null  object        \n",
      " 2   Description  522533 non-null  object        \n",
      " 3   Quantity     525461 non-null  int64         \n",
      " 4   InvoiceDate  525461 non-null  datetime64[ns]\n",
      " 5   Price        525461 non-null  float64       \n",
      " 6   Customer ID  417534 non-null  float64       \n",
      " 7   Country      525461 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 32.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5712cc80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Invoice             0\n",
       "StockCode           0\n",
       "Description      2928\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "Price               0\n",
       "Customer ID    107927\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b6e1fa43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WHITE HANGING HEART T-LIGHT HOLDER    3549\n",
       "REGENCY CAKESTAND 3 TIER              2212\n",
       "STRAWBERRY CERAMIC TRINKET BOX        1843\n",
       "PACK OF 72 RETRO SPOT CAKE CASES      1466\n",
       "ASSORTED COLOUR BIRD ORNAMENT         1457\n",
       "Name: Description, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many of which product are there?\n",
    "df[\"Description\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3ed684e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WHITE HANGING HEART T-LIGHT HOLDER</th>\n",
       "      <td>57733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WORLD WAR 2 GLIDERS ASSTD DESIGNS</th>\n",
       "      <td>54698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BROCADE RING PURSE</th>\n",
       "      <td>47647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PACK OF 72 RETRO SPOT CAKE CASES</th>\n",
       "      <td>46106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSORTED COLOUR BIRD ORNAMENT</th>\n",
       "      <td>44925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                    Quantity\n",
       "Description                                 \n",
       "WHITE HANGING HEART T-LIGHT HOLDER     57733\n",
       "WORLD WAR 2 GLIDERS ASSTD DESIGNS      54698\n",
       "BROCADE RING PURSE                     47647\n",
       "PACK OF 72 RETRO SPOT CAKE CASES       46106\n",
       "ASSORTED COLOUR BIRD ORNAMENT          44925"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many of which product were sold?\n",
    "df.groupby(\"Description\").agg({\"Quantity\":\"sum\"}).sort_values(\"Quantity\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e3aa3369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28816"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unique invoice number\n",
    "df[\"Invoice\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bde61259",
   "metadata": {},
   "outputs": [],
   "source": [
    "#How much money was earned in total per bill\n",
    "df[\"TotalPrice\"] = df[\"Quantity\"] * df[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b52381e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
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       "      <th>TotalPrice</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.950</td>\n",
       "      <td>13085.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>83.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.750</td>\n",
       "      <td>13085.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>81.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.750</td>\n",
       "      <td>13085.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>81.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.100</td>\n",
       "      <td>13085.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>100.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.250</td>\n",
       "      <td>13085.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>30.000</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525456</th>\n",
       "      <td>538171</td>\n",
       "      <td>22271</td>\n",
       "      <td>FELTCRAFT DOLL ROSIE</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-09 20:01:00</td>\n",
       "      <td>2.950</td>\n",
       "      <td>17530.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>5.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525457</th>\n",
       "      <td>538171</td>\n",
       "      <td>22750</td>\n",
       "      <td>FELTCRAFT PRINCESS LOLA DOLL</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-09 20:01:00</td>\n",
       "      <td>3.750</td>\n",
       "      <td>17530.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525458</th>\n",
       "      <td>538171</td>\n",
       "      <td>22751</td>\n",
       "      <td>FELTCRAFT PRINCESS OLIVIA DOLL</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-09 20:01:00</td>\n",
       "      <td>3.750</td>\n",
       "      <td>17530.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525459</th>\n",
       "      <td>538171</td>\n",
       "      <td>20970</td>\n",
       "      <td>PINK FLORAL FELTCRAFT SHOULDER BAG</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-09 20:01:00</td>\n",
       "      <td>3.750</td>\n",
       "      <td>17530.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>7.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525460</th>\n",
       "      <td>538171</td>\n",
       "      <td>21931</td>\n",
       "      <td>JUMBO STORAGE BAG SUKI</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-09 20:01:00</td>\n",
       "      <td>1.950</td>\n",
       "      <td>17530.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3.900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525461 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Invoice StockCode                          Description  Quantity  \\\n",
       "0       489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1       489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2       489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3       489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4       489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "...        ...       ...                                  ...       ...   \n",
       "525456  538171     22271                 FELTCRAFT DOLL ROSIE         2   \n",
       "525457  538171     22750         FELTCRAFT PRINCESS LOLA DOLL         1   \n",
       "525458  538171     22751       FELTCRAFT PRINCESS OLIVIA DOLL         1   \n",
       "525459  538171     20970   PINK FLORAL FELTCRAFT SHOULDER BAG         2   \n",
       "525460  538171     21931               JUMBO STORAGE BAG SUKI         2   \n",
       "\n",
       "               InvoiceDate  Price  Customer ID         Country  TotalPrice  \n",
       "0      2009-12-01 07:45:00  6.950    13085.000  United Kingdom      83.400  \n",
       "1      2009-12-01 07:45:00  6.750    13085.000  United Kingdom      81.000  \n",
       "2      2009-12-01 07:45:00  6.750    13085.000  United Kingdom      81.000  \n",
       "3      2009-12-01 07:45:00  2.100    13085.000  United Kingdom     100.800  \n",
       "4      2009-12-01 07:45:00  1.250    13085.000  United Kingdom      30.000  \n",
       "...                    ...    ...          ...             ...         ...  \n",
       "525456 2010-12-09 20:01:00  2.950    17530.000  United Kingdom       5.900  \n",
       "525457 2010-12-09 20:01:00  3.750    17530.000  United Kingdom       3.750  \n",
       "525458 2010-12-09 20:01:00  3.750    17530.000  United Kingdom       3.750  \n",
       "525459 2010-12-09 20:01:00  3.750    17530.000  United Kingdom       7.500  \n",
       "525460 2010-12-09 20:01:00  1.950    17530.000  United Kingdom       3.900  \n",
       "\n",
       "[525461 rows x 9 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8cf9b35a",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Invoice</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>489434</th>\n",
       "      <td>505.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489435</th>\n",
       "      <td>145.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489436</th>\n",
       "      <td>630.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489437</th>\n",
       "      <td>310.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489438</th>\n",
       "      <td>2286.240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         TotalPrice\n",
       "Invoice            \n",
       "489434      505.300\n",
       "489435      145.800\n",
       "489436      630.330\n",
       "489437      310.750\n",
       "489438     2286.240"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Invoice\").agg({\"TotalPrice\": \"sum\"}).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb8b8342",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "aead045c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f4c200c4",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Quantity</th>\n",
       "      <td>417534.000</td>\n",
       "      <td>12.759</td>\n",
       "      <td>101.220</td>\n",
       "      <td>-9360.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>19152.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>417534.000</td>\n",
       "      <td>3.888</td>\n",
       "      <td>71.132</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.950</td>\n",
       "      <td>3.750</td>\n",
       "      <td>25111.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <td>417534.000</td>\n",
       "      <td>15360.645</td>\n",
       "      <td>1680.811</td>\n",
       "      <td>12346.000</td>\n",
       "      <td>13983.000</td>\n",
       "      <td>15311.000</td>\n",
       "      <td>16799.000</td>\n",
       "      <td>18287.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalPrice</th>\n",
       "      <td>417534.000</td>\n",
       "      <td>19.994</td>\n",
       "      <td>99.916</td>\n",
       "      <td>-25111.090</td>\n",
       "      <td>4.250</td>\n",
       "      <td>11.250</td>\n",
       "      <td>19.350</td>\n",
       "      <td>15818.400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count      mean      std        min       25%       50%  \\\n",
       "Quantity    417534.000    12.759  101.220  -9360.000     2.000     4.000   \n",
       "Price       417534.000     3.888   71.132      0.000     1.250     1.950   \n",
       "Customer ID 417534.000 15360.645 1680.811  12346.000 13983.000 15311.000   \n",
       "TotalPrice  417534.000    19.994   99.916 -25111.090     4.250    11.250   \n",
       "\n",
       "                  75%       max  \n",
       "Quantity       12.000 19152.000  \n",
       "Price           3.750 25111.090  \n",
       "Customer ID 16799.000 18287.000  \n",
       "TotalPrice     19.350 15818.400  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e5a8b534",
   "metadata": {},
   "outputs": [],
   "source": [
    "#iade edilen faturalari cikardik\n",
    "df = df[~df[\"Invoice\"].str.contains(\"C\", na=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d5d5ca",
   "metadata": {},
   "source": [
    "# Calculating RFM Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fe566912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_date = dt.datetime(2010,12,11)\n",
    "type(today_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7ceb0a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm = df.groupby(\"Customer ID\").agg({\"InvoiceDate\": lambda InvoiceDate: (today_date - InvoiceDate.max()).days,\n",
    "                                     \"Invoice\": lambda Invoice: Invoice.nunique(),\n",
    "                                     \"TotalPrice\": lambda TotalPrice: TotalPrice.sum()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b1a3ddca",
   "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>InvoiceDate</th>\n",
       "      <th>Invoice</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.000</th>\n",
       "      <td>165</td>\n",
       "      <td>11</td>\n",
       "      <td>372.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.000</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1323.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.000</th>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>222.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.000</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>2671.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12351.000</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>300.930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             InvoiceDate  Invoice  TotalPrice\n",
       "Customer ID                                  \n",
       "12346.000            165       11     372.860\n",
       "12347.000              3        2    1323.320\n",
       "12348.000             74        1     222.160\n",
       "12349.000             43        3    2671.140\n",
       "12351.000             11        1     300.930"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "26448512",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm.columns = [\"recency\",\"frequency\",\"monetary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "acdc1f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>recency</th>\n",
       "      <td>4314.000</td>\n",
       "      <td>91.270</td>\n",
       "      <td>96.944</td>\n",
       "      <td>1.000</td>\n",
       "      <td>18.000</td>\n",
       "      <td>53.000</td>\n",
       "      <td>136.000</td>\n",
       "      <td>374.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>4314.000</td>\n",
       "      <td>4.454</td>\n",
       "      <td>8.169</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>205.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monetary</th>\n",
       "      <td>4314.000</td>\n",
       "      <td>2047.289</td>\n",
       "      <td>8912.523</td>\n",
       "      <td>0.000</td>\n",
       "      <td>307.950</td>\n",
       "      <td>705.550</td>\n",
       "      <td>1722.802</td>\n",
       "      <td>349164.350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count     mean      std   min     25%     50%      75%        max\n",
       "recency   4314.000   91.270   96.944 1.000  18.000  53.000  136.000    374.000\n",
       "frequency 4314.000    4.454    8.169 1.000   1.000   2.000    5.000    205.000\n",
       "monetary  4314.000 2047.289 8912.523 0.000 307.950 705.550 1722.802 349164.350"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "64d2cd09",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm = rfm[rfm[\"monetary\"] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8d2003d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>recency</th>\n",
       "      <td>4312.000</td>\n",
       "      <td>91.173</td>\n",
       "      <td>96.861</td>\n",
       "      <td>1.000</td>\n",
       "      <td>18.000</td>\n",
       "      <td>53.000</td>\n",
       "      <td>136.000</td>\n",
       "      <td>374.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>4312.000</td>\n",
       "      <td>4.456</td>\n",
       "      <td>8.170</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>205.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monetary</th>\n",
       "      <td>4312.000</td>\n",
       "      <td>2048.238</td>\n",
       "      <td>8914.481</td>\n",
       "      <td>2.950</td>\n",
       "      <td>307.988</td>\n",
       "      <td>706.020</td>\n",
       "      <td>1723.142</td>\n",
       "      <td>349164.350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count     mean      std   min     25%     50%      75%        max\n",
       "recency   4312.000   91.173   96.861 1.000  18.000  53.000  136.000    374.000\n",
       "frequency 4312.000    4.456    8.170 1.000   1.000   2.000    5.000    205.000\n",
       "monetary  4312.000 2048.238 8914.481 2.950 307.988 706.020 1723.142 349164.350"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06feaa15",
   "metadata": {},
   "source": [
    "# Calculating RFM Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "71a03fd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm[\"recency_score\"] = pd.qcut(rfm[\"recency\"],5, labels=[5, 4, 3, 2, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fd5b577e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm[\"monetary_score\"] = pd.qcut(rfm[\"monetary\"], 5, labels=[1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "797b2909",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm[\"frequency_score\"] = pd.qcut(rfm[\"frequency\"].rank(method=\"first\"), 5, labels=[1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3261b360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency_score</th>\n",
       "      <th>monetary_score</th>\n",
       "      <th>frequency_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</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>12346.000</th>\n",
       "      <td>165</td>\n",
       "      <td>11</td>\n",
       "      <td>372.860</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.000</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1323.320</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.000</th>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>222.160</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.000</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>2671.140</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12351.000</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>300.930</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283.000</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>641.770</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18284.000</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>461.680</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18285.000</th>\n",
       "      <td>296</td>\n",
       "      <td>1</td>\n",
       "      <td>427.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286.000</th>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "      <td>1296.430</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18287.000</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>2345.710</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4312 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary recency_score monetary_score  \\\n",
       "Customer ID                                                              \n",
       "12346.000        165         11   372.860             2              2   \n",
       "12347.000          3          2  1323.320             5              4   \n",
       "12348.000         74          1   222.160             2              1   \n",
       "12349.000         43          3  2671.140             3              5   \n",
       "12351.000         11          1   300.930             5              2   \n",
       "...              ...        ...       ...           ...            ...   \n",
       "18283.000         18          6   641.770             4              3   \n",
       "18284.000         67          1   461.680             3              2   \n",
       "18285.000        296          1   427.000             1              2   \n",
       "18286.000        112          2  1296.430             2              4   \n",
       "18287.000         18          4  2345.710             4              5   \n",
       "\n",
       "            frequency_score  \n",
       "Customer ID                  \n",
       "12346.000                 5  \n",
       "12347.000                 2  \n",
       "12348.000                 1  \n",
       "12349.000                 3  \n",
       "12351.000                 1  \n",
       "...                     ...  \n",
       "18283.000                 5  \n",
       "18284.000                 2  \n",
       "18285.000                 2  \n",
       "18286.000                 3  \n",
       "18287.000                 4  \n",
       "\n",
       "[4312 rows x 6 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ff99e871",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm[\"RFM_SCORE\"] = (rfm[\"recency_score\"].astype(str)+\n",
    "                    rfm[\"frequency_score\"].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "390fb51d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency_score</th>\n",
       "      <th>monetary_score</th>\n",
       "      <th>frequency_score</th>\n",
       "      <th>RFM_SCORE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.000</th>\n",
       "      <td>165</td>\n",
       "      <td>11</td>\n",
       "      <td>372.860</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.000</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1323.320</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.000</th>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>222.160</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.000</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>2671.140</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12351.000</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>300.930</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283.000</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>641.770</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18284.000</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>461.680</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18285.000</th>\n",
       "      <td>296</td>\n",
       "      <td>1</td>\n",
       "      <td>427.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286.000</th>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "      <td>1296.430</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18287.000</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>2345.710</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4312 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary recency_score monetary_score  \\\n",
       "Customer ID                                                              \n",
       "12346.000        165         11   372.860             2              2   \n",
       "12347.000          3          2  1323.320             5              4   \n",
       "12348.000         74          1   222.160             2              1   \n",
       "12349.000         43          3  2671.140             3              5   \n",
       "12351.000         11          1   300.930             5              2   \n",
       "...              ...        ...       ...           ...            ...   \n",
       "18283.000         18          6   641.770             4              3   \n",
       "18284.000         67          1   461.680             3              2   \n",
       "18285.000        296          1   427.000             1              2   \n",
       "18286.000        112          2  1296.430             2              4   \n",
       "18287.000         18          4  2345.710             4              5   \n",
       "\n",
       "            frequency_score RFM_SCORE  \n",
       "Customer ID                            \n",
       "12346.000                 5        25  \n",
       "12347.000                 2        52  \n",
       "12348.000                 1        21  \n",
       "12349.000                 3        33  \n",
       "12351.000                 1        51  \n",
       "...                     ...       ...  \n",
       "18283.000                 5        45  \n",
       "18284.000                 2        32  \n",
       "18285.000                 2        12  \n",
       "18286.000                 3        23  \n",
       "18287.000                 4        44  \n",
       "\n",
       "[4312 rows x 7 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e66ae3a",
   "metadata": {},
   "source": [
    "# Creating & Analysing RFM Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "34ed47c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "seg_map = {\n",
    "    r\"[1-2][1-2]\": \"hibernating\",\n",
    "    r\"[1-2][3-4]\": \"at_Risk\",\n",
    "    r\"[1-2]5\": \"cant_loose\",\n",
    "    r\"3[1-2]\": \"about_to_sleep\",\n",
    "    r\"33\": \"need_attention\",\n",
    "    r\"[3-4][4-5]\": \"loyal_customers\",\n",
    "    r\"41\": \"promising\",\n",
    "    r\"51\": \"new_customers\",\n",
    "    r\"[4-5][2-3]\": \"potential_loyalists\",\n",
    "    r\"5[4-5]\": \"champions\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "169b5b32",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm[\"segment\"] = rfm[\"RFM_SCORE\"].replace(seg_map, regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c4a50c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>frequency</th>\n",
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       "      <th>monetary_score</th>\n",
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       "      <th>RFM_SCORE</th>\n",
       "      <th>segment</th>\n",
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       "    <tr>\n",
       "      <th>Customer ID</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>12346.000</th>\n",
       "      <td>165</td>\n",
       "      <td>11</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>cant_loose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.000</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1323.320</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>potential_loyalists</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.000</th>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>222.160</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>hibernating</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.000</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>2671.140</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>need_attention</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12351.000</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>300.930</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>new_customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283.000</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>641.770</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>loyal_customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18284.000</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>461.680</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>about_to_sleep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18285.000</th>\n",
       "      <td>296</td>\n",
       "      <td>1</td>\n",
       "      <td>427.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>hibernating</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286.000</th>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "      <td>1296.430</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>at_Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18287.000</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>2345.710</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>loyal_customers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4312 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary recency_score monetary_score  \\\n",
       "Customer ID                                                              \n",
       "12346.000        165         11   372.860             2              2   \n",
       "12347.000          3          2  1323.320             5              4   \n",
       "12348.000         74          1   222.160             2              1   \n",
       "12349.000         43          3  2671.140             3              5   \n",
       "12351.000         11          1   300.930             5              2   \n",
       "...              ...        ...       ...           ...            ...   \n",
       "18283.000         18          6   641.770             4              3   \n",
       "18284.000         67          1   461.680             3              2   \n",
       "18285.000        296          1   427.000             1              2   \n",
       "18286.000        112          2  1296.430             2              4   \n",
       "18287.000         18          4  2345.710             4              5   \n",
       "\n",
       "            frequency_score RFM_SCORE              segment  \n",
       "Customer ID                                                 \n",
       "12346.000                 5        25           cant_loose  \n",
       "12347.000                 2        52  potential_loyalists  \n",
       "12348.000                 1        21          hibernating  \n",
       "12349.000                 3        33       need_attention  \n",
       "12351.000                 1        51        new_customers  \n",
       "...                     ...       ...                  ...  \n",
       "18283.000                 5        45      loyal_customers  \n",
       "18284.000                 2        32       about_to_sleep  \n",
       "18285.000                 2        12          hibernating  \n",
       "18286.000                 3        23              at_Risk  \n",
       "18287.000                 4        44      loyal_customers  \n",
       "\n",
       "[4312 rows x 8 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "88d69e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">frequency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">monetary</th>\n",
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       "      <th></th>\n",
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       "      <th>mean</th>\n",
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       "      <th>mean</th>\n",
       "      <th>count</th>\n",
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       "      <th>segment</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>about_to_sleep</th>\n",
       "      <td>53.819</td>\n",
       "      <td>343</td>\n",
       "      <td>1.201</td>\n",
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       "      <td>441.320</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>at_Risk</th>\n",
       "      <td>152.159</td>\n",
       "      <td>611</td>\n",
       "      <td>3.074</td>\n",
       "      <td>611</td>\n",
       "      <td>1188.878</td>\n",
       "      <td>611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cant_loose</th>\n",
       "      <td>124.117</td>\n",
       "      <td>77</td>\n",
       "      <td>9.117</td>\n",
       "      <td>77</td>\n",
       "      <td>4099.450</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>champions</th>\n",
       "      <td>7.119</td>\n",
       "      <td>663</td>\n",
       "      <td>12.554</td>\n",
       "      <td>663</td>\n",
       "      <td>6852.264</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hibernating</th>\n",
       "      <td>213.886</td>\n",
       "      <td>1015</td>\n",
       "      <td>1.126</td>\n",
       "      <td>1015</td>\n",
       "      <td>403.978</td>\n",
       "      <td>1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loyal_customers</th>\n",
       "      <td>36.287</td>\n",
       "      <td>742</td>\n",
       "      <td>6.830</td>\n",
       "      <td>742</td>\n",
       "      <td>2746.067</td>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>need_attention</th>\n",
       "      <td>53.266</td>\n",
       "      <td>207</td>\n",
       "      <td>2.449</td>\n",
       "      <td>207</td>\n",
       "      <td>1060.357</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_customers</th>\n",
       "      <td>8.580</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000</td>\n",
       "      <td>50</td>\n",
       "      <td>386.199</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potential_loyalists</th>\n",
       "      <td>18.793</td>\n",
       "      <td>517</td>\n",
       "      <td>2.017</td>\n",
       "      <td>517</td>\n",
       "      <td>729.511</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promising</th>\n",
       "      <td>25.747</td>\n",
       "      <td>87</td>\n",
       "      <td>1.000</td>\n",
       "      <td>87</td>\n",
       "      <td>367.087</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    recency       frequency       monetary      \n",
       "                       mean count      mean count     mean count\n",
       "segment                                                         \n",
       "about_to_sleep       53.819   343     1.201   343  441.320   343\n",
       "at_Risk             152.159   611     3.074   611 1188.878   611\n",
       "cant_loose          124.117    77     9.117    77 4099.450    77\n",
       "champions             7.119   663    12.554   663 6852.264   663\n",
       "hibernating         213.886  1015     1.126  1015  403.978  1015\n",
       "loyal_customers      36.287   742     6.830   742 2746.067   742\n",
       "need_attention       53.266   207     2.449   207 1060.357   207\n",
       "new_customers         8.580    50     1.000    50  386.199    50\n",
       "potential_loyalists  18.793   517     2.017   517  729.511   517\n",
       "promising            25.747    87     1.000    87  367.087    87"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm[[\"segment\", \"recency\", \"frequency\", \"monetary\"]].groupby(\"segment\").agg([\"mean\", \"count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9db04a16",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame()\n",
    "new_df[\"new_customer_id\"] = rfm[rfm[\"segment\"] == \"new_customers\"].index\n",
    "new_df[\"new_customer_id\"] = new_df[\"new_customer_id\"].astype(int)\n",
    "new_df.to_csv(\"new_customer_id.csv\")\n",
    "rfm.to_csv(\"rfm.csv\")"
   ]
  }
 ],
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