MMA831Dos2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DOS purpose:\n",
    "Group A: (customer segmentation – RFM & hierarchical clustering)\n",
    "1.\tDevelop customer segmentation based on RFM + Hierarchical clustering \n",
    "2.\tCalculate Recency, Frequency, and Monetary metrics at the customer level \n",
    "3.\tRun hierarchical clustering on your data and decide where you’d like to cut your dendrogram (i.e. how many clusters/segments do you want) \n",
    "4.\tConduct exploratory analysis to see if you can find any meaningful differences amongst customers in terms of the most/least popular products purchased within each cluster, number of refunds, day of week, etc. \n",
    "5.\tInterpret your outputs, describe each segment in terms of value, hypothesize what type of customers might make up the majority of each segment, and brainstorm marketing strategies for each segment – subgroup 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "import sklearn.metrics\n",
    "import itertools\n",
    "import scipy\n",
    "import pandas_profiling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel('/System/Volumes/Data/Users/Michael/Library/CloudStorage/iCloud Drive/MMA/MMA 831/Dos2/Online Retail.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>536365</td>\n",
       "      <td>22752</td>\n",
       "      <td>SET 7 BABUSHKA NESTING BOXES</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>7.65</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>536365</td>\n",
       "      <td>21730</td>\n",
       "      <td>GLASS STAR FROSTED T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>536366</td>\n",
       "      <td>22633</td>\n",
       "      <td>HAND WARMER UNION JACK</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:28:00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>536366</td>\n",
       "      <td>22632</td>\n",
       "      <td>HAND WARMER RED POLKA DOT</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:28:00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>536367</td>\n",
       "      <td>84879</td>\n",
       "      <td>ASSORTED COLOUR BIRD ORNAMENT</td>\n",
       "      <td>32</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>1.69</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>536367</td>\n",
       "      <td>22745</td>\n",
       "      <td>POPPY'S PLAYHOUSE BEDROOM</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>536367</td>\n",
       "      <td>22748</td>\n",
       "      <td>POPPY'S PLAYHOUSE KITCHEN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>536367</td>\n",
       "      <td>22749</td>\n",
       "      <td>FELTCRAFT PRINCESS CHARLOTTE DOLL</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>536367</td>\n",
       "      <td>22310</td>\n",
       "      <td>IVORY KNITTED MUG COSY</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>536367</td>\n",
       "      <td>84969</td>\n",
       "      <td>BOX OF 6 ASSORTED COLOUR TEASPOONS</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>536367</td>\n",
       "      <td>22623</td>\n",
       "      <td>BOX OF VINTAGE JIGSAW BLOCKS</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>536367</td>\n",
       "      <td>22622</td>\n",
       "      <td>BOX OF VINTAGE ALPHABET BLOCKS</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>9.95</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>536367</td>\n",
       "      <td>21754</td>\n",
       "      <td>HOME BUILDING BLOCK WORD</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>5.95</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>536367</td>\n",
       "      <td>21755</td>\n",
       "      <td>LOVE BUILDING BLOCK WORD</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>5.95</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>536367</td>\n",
       "      <td>21777</td>\n",
       "      <td>RECIPE BOX WITH METAL HEART</td>\n",
       "      <td>4</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>7.95</td>\n",
       "      <td>13047.0</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",
       "5     536365     22752         SET 7 BABUSHKA NESTING BOXES         2   \n",
       "6     536365     21730    GLASS STAR FROSTED T-LIGHT HOLDER         6   \n",
       "7     536366     22633               HAND WARMER UNION JACK         6   \n",
       "8     536366     22632            HAND WARMER RED POLKA DOT         6   \n",
       "9     536367     84879        ASSORTED COLOUR BIRD ORNAMENT        32   \n",
       "10    536367     22745           POPPY'S PLAYHOUSE BEDROOM          6   \n",
       "11    536367     22748            POPPY'S PLAYHOUSE KITCHEN         6   \n",
       "12    536367     22749    FELTCRAFT PRINCESS CHARLOTTE DOLL         8   \n",
       "13    536367     22310              IVORY KNITTED MUG COSY          6   \n",
       "14    536367     84969   BOX OF 6 ASSORTED COLOUR TEASPOONS         6   \n",
       "15    536367     22623        BOX OF VINTAGE JIGSAW BLOCKS          3   \n",
       "16    536367     22622       BOX OF VINTAGE ALPHABET BLOCKS         2   \n",
       "17    536367     21754             HOME BUILDING BLOCK WORD         3   \n",
       "18    536367     21755             LOVE BUILDING BLOCK WORD         3   \n",
       "19    536367     21777          RECIPE BOX WITH METAL HEART         4   \n",
       "\n",
       "           InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2  2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "5  2010-12-01 08:26:00       7.65     17850.0  United Kingdom  \n",
       "6  2010-12-01 08:26:00       4.25     17850.0  United Kingdom  \n",
       "7  2010-12-01 08:28:00       1.85     17850.0  United Kingdom  \n",
       "8  2010-12-01 08:28:00       1.85     17850.0  United Kingdom  \n",
       "9  2010-12-01 08:34:00       1.69     13047.0  United Kingdom  \n",
       "10 2010-12-01 08:34:00       2.10     13047.0  United Kingdom  \n",
       "11 2010-12-01 08:34:00       2.10     13047.0  United Kingdom  \n",
       "12 2010-12-01 08:34:00       3.75     13047.0  United Kingdom  \n",
       "13 2010-12-01 08:34:00       1.65     13047.0  United Kingdom  \n",
       "14 2010-12-01 08:34:00       4.25     13047.0  United Kingdom  \n",
       "15 2010-12-01 08:34:00       4.95     13047.0  United Kingdom  \n",
       "16 2010-12-01 08:34:00       9.95     13047.0  United Kingdom  \n",
       "17 2010-12-01 08:34:00       5.95     13047.0  United Kingdom  \n",
       "18 2010-12-01 08:34:00       5.95     13047.0  United Kingdom  \n",
       "19 2010-12-01 08:34:00       7.95     13047.0  United Kingdom  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
      "InvoiceNo      541909 non-null object\n",
      "StockCode      541909 non-null object\n",
      "Description    540455 non-null object\n",
      "Quantity       541909 non-null int64\n",
      "InvoiceDate    541909 non-null datetime64[ns]\n",
      "UnitPrice      541909 non-null float64\n",
      "CustomerID     406829 non-null float64\n",
      "Country        541909 non-null object\n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    },
    {
     "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>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</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>4.611114</td>\n",
       "      <td>15287.690570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "      <td>1713.600303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</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>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</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>4.130000</td>\n",
       "      <td>16791.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID\n",
       "count  541909.000000  541909.000000  406829.000000\n",
       "mean        9.552250       4.611114   15287.690570\n",
       "std       218.081158      96.759853    1713.600303\n",
       "min    -80995.000000  -11062.060000   12346.000000\n",
       "25%         1.000000       1.250000   13953.000000\n",
       "50%         3.000000       2.080000   15152.000000\n",
       "75%        10.000000       4.130000   16791.000000\n",
       "max     80995.000000   38970.000000   18287.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)\n",
    "df.info()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is nagive number in both quantity and Unitprice. So, we dropped these instances.\n",
    "Besides, our analysis is based on CustomerID. So we drop the instances that are missing value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 397884 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      "InvoiceNo      397884 non-null object\n",
      "StockCode      397884 non-null object\n",
      "Description    397884 non-null object\n",
      "Quantity       397884 non-null int64\n",
      "InvoiceDate    397884 non-null datetime64[ns]\n",
      "UnitPrice      397884 non-null float64\n",
      "CustomerID     397884 non-null float64\n",
      "Country        397884 non-null object\n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 27.3+ MB\n"
     ]
    },
    {
     "data": {
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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>Quantity</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>397884.000000</td>\n",
       "      <td>397884.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.988238</td>\n",
       "      <td>3.116488</td>\n",
       "      <td>15294.423453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>179.331775</td>\n",
       "      <td>22.097877</td>\n",
       "      <td>1713.141560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</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>1.250000</td>\n",
       "      <td>13969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</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>3.750000</td>\n",
       "      <td>16795.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>8142.750000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID\n",
       "count  397884.000000  397884.000000  397884.000000\n",
       "mean       12.988238       3.116488   15294.423453\n",
       "std       179.331775      22.097877    1713.141560\n",
       "min         1.000000       0.001000   12346.000000\n",
       "25%         2.000000       1.250000   13969.000000\n",
       "50%         6.000000       1.950000   15159.000000\n",
       "75%        12.000000       3.750000   16795.000000\n",
       "max     80995.000000    8142.750000   18287.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop_nagtive=df[(df.Quantity>0)&(df.UnitPrice)>0]\n",
    "df_drop_missing_value=df_drop_nagtive[pd.notnull(df_drop_nagtive['CustomerID'])]\n",
    "df_drop_missing_value.info()\n",
    "df_drop_missing_value.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the latest date and set the baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-09 12:50:00')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop_missing_value['InvoiceDate'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-10 12:50:00')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Day_baseline=df_drop_missing_value['InvoiceDate'].max()+dt.timedelta(days=1)\n",
    "Day_baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caculate the total price for each instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Michael/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_drop_missing_value['Total_Price']=df_drop_missing_value.Quantity*df_drop_missing_value.UnitPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Total_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  Total_Price  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom        15.30  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom        20.34  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom        22.00  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom        20.34  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom        20.34  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned=df_drop_missing_value\n",
    "df_cleaned.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create RFM table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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_value</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>12346.0</th>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>75</td>\n",
       "      <td>31</td>\n",
       "      <td>1797.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>19</td>\n",
       "      <td>73</td>\n",
       "      <td>1757.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>310</td>\n",
       "      <td>17</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value\n",
       "CustomerID                                    \n",
       "12346.0         326          1        77183.60\n",
       "12347.0           2        182         4310.00\n",
       "12348.0          75         31         1797.24\n",
       "12349.0          19         73         1757.55\n",
       "12350.0         310         17          334.40"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Float64Index: 4338 entries, 12346.0 to 18287.0\n",
      "Data columns (total 3 columns):\n",
      "recency           4338 non-null int64\n",
      "frequency         4338 non-null int64\n",
      "monetary_value    4338 non-null float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 135.6 KB\n"
     ]
    }
   ],
   "source": [
    "rfmTable = df_cleaned.groupby('CustomerID').agg({'InvoiceDate': lambda x: (Day_baseline - x.max()).days, 'InvoiceNo': lambda x: len(x), 'Total_Price': lambda x: x.sum()})\n",
    "rfmTable['InvoiceDate'] = rfmTable['InvoiceDate'].astype(int)\n",
    "rfmTable.rename(columns={'InvoiceDate': 'recency', \n",
    "                         'InvoiceNo': 'frequency', \n",
    "                         'Total_Price': 'monetary_value'}, inplace=True)\n",
    "rfmTable.head()\n",
    "rfmTable.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantiles = rfmTable.quantile(q=[0.25,0.5,0.75])\n",
    "quantiles = quantiles.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'recency': {0.25: 18.0, 0.5: 51.0, 0.75: 142.0},\n",
       " 'frequency': {0.25: 17.0, 0.5: 41.0, 0.75: 100.0},\n",
       " 'monetary_value': {0.25: 307.41499999999996,\n",
       "  0.5: 674.4849999999999,\n",
       "  0.75: 1661.7400000000002}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RScore(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 1\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 2\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "    \n",
    "def FMScore(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 4\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 3\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 2\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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_value</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</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>12346.0</th>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>75</td>\n",
       "      <td>31</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>19</td>\n",
       "      <td>73</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>310</td>\n",
       "      <td>17</td>\n",
       "      <td>334.40</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value  r_quartile  f_quartile  \\\n",
       "CustomerID                                                               \n",
       "12346.0         326          1        77183.60           4           4   \n",
       "12347.0           2        182         4310.00           1           1   \n",
       "12348.0          75         31         1797.24           3           3   \n",
       "12349.0          19         73         1757.55           2           2   \n",
       "12350.0         310         17          334.40           4           4   \n",
       "\n",
       "            m_quartile  \n",
       "CustomerID              \n",
       "12346.0              1  \n",
       "12347.0              1  \n",
       "12348.0              1  \n",
       "12349.0              1  \n",
       "12350.0              3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segmented_rfm = rfmTable.copy()\n",
    "segmented_rfm['r_quartile'] = segmented_rfm['recency'].apply(RScore, args=('recency',quantiles,))\n",
    "segmented_rfm['f_quartile'] = segmented_rfm['frequency'].apply(FMScore, args=('frequency',quantiles,))\n",
    "segmented_rfm['m_quartile'] = segmented_rfm['monetary_value'].apply(FMScore, args=('monetary_value',quantiles,))\n",
    "segmented_rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalize the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</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>12346.0</th>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>75</td>\n",
       "      <td>31</td>\n",
       "      <td>1797.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>19</td>\n",
       "      <td>73</td>\n",
       "      <td>1757.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>310</td>\n",
       "      <td>17</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value\n",
       "CustomerID                                    \n",
       "12346.0         326          1        77183.60\n",
       "12347.0           2        182         4310.00\n",
       "12348.0          75         31         1797.24\n",
       "12349.0          19         73         1757.55\n",
       "12350.0         310         17          334.40"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = rfmTable.copy()\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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_value</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>12346.0</th>\n",
       "      <td>2.334574</td>\n",
       "      <td>-0.396578</td>\n",
       "      <td>8.358668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>-0.905340</td>\n",
       "      <td>0.394649</td>\n",
       "      <td>0.250966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>-0.175360</td>\n",
       "      <td>-0.265435</td>\n",
       "      <td>-0.028596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>-0.735345</td>\n",
       "      <td>-0.081836</td>\n",
       "      <td>-0.033012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>2.174578</td>\n",
       "      <td>-0.326635</td>\n",
       "      <td>-0.191347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary_value\n",
       "CustomerID                                     \n",
       "12346.0     2.334574  -0.396578        8.358668\n",
       "12347.0    -0.905340   0.394649        0.250966\n",
       "12348.0    -0.175360  -0.265435       -0.028596\n",
       "12349.0    -0.735345  -0.081836       -0.033012\n",
       "12350.0     2.174578  -0.326635       -0.191347"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X.monetary_value=np.log(X.monetary_value)\n",
    "scaler = StandardScaler()\n",
    "features = ['recency','frequency','monetary_value']\n",
    "X[features] = scaler.fit_transform(X[features])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#pandas_profiling.ProfileReport(X, check_correlation = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elbow Method to find the best cluster number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "silhouettes = {}\n",
    "for k in range(2, 11):\n",
    "    agg = AgglomerativeClustering(n_clusters=k, affinity='euclidean', linkage='ward').fit(X)\n",
    "    silhouettes[k] = silhouette_score(X, agg.labels_, metric='cosine')\n",
    "    \n",
    "    \n",
    "    plt.figure();\n",
    "plt.grid(True);\n",
    "plt.plot(list(silhouettes.keys()), list(silhouettes.values()));\n",
    "plt.title('Agg, Elbow Method')\n",
    "plt.xlabel(\"Number of clusters, K\");\n",
    "plt.ylabel(\"Silhouette\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 is the best cluster number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "                        connectivity=None, distance_threshold=None,\n",
       "                        linkage='ward', memory=None, n_clusters=3,\n",
       "                        pooling_func='deprecated')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward')\n",
    "agg.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5269205165271833"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(X, agg.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# find the best linkage and metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linkage=complete, Metric=euclidean, Clusters=3, Silhouette=0.93\n",
      "Linkage=complete, Metric=minkowski, Clusters=3, Silhouette=0.93\n",
      "Linkage=complete, Metric=cityblock, Clusters=3, Silhouette=0.925\n",
      "Linkage=complete, Metric=cosine, Clusters=3, Silhouette=0.707\n",
      "Linkage=complete, Metric=correlation, Clusters=3, Silhouette=0.85\n",
      "Linkage=complete, Metric=chebyshev, Clusters=3, Silhouette=0.946\n",
      "Linkage=complete, Metric=mahalanobis, Clusters=3, Silhouette=0.946\n",
      "Linkage=single, Metric=euclidean, Clusters=3, Silhouette=0.949\n",
      "Linkage=single, Metric=minkowski, Clusters=3, Silhouette=0.949\n",
      "Linkage=single, Metric=cityblock, Clusters=3, Silhouette=0.952\n",
      "Linkage=single, Metric=cosine, Clusters=3, Silhouette=-0.132\n",
      "Linkage=single, Metric=correlation, Clusters=3, Silhouette=-0.101\n",
      "Linkage=single, Metric=chebyshev, Clusters=3, Silhouette=0.943\n",
      "Linkage=single, Metric=mahalanobis, Clusters=3, Silhouette=0.943\n",
      "Linkage=centroid, Metric=euclidean, Clusters=3, Silhouette=0.93\n",
      "Linkage=average, Metric=euclidean, Clusters=3, Silhouette=0.944\n",
      "Linkage=average, Metric=minkowski, Clusters=3, Silhouette=0.944\n",
      "Linkage=average, Metric=cityblock, Clusters=3, Silhouette=0.925\n",
      "Linkage=average, Metric=cosine, Clusters=3, Silhouette=0.739\n",
      "Linkage=average, Metric=correlation, Clusters=3, Silhouette=0.787\n",
      "Linkage=average, Metric=chebyshev, Clusters=3, Silhouette=0.932\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Michael/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:13: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linkage=average, Metric=mahalanobis, Clusters=3, Silhouette=0.946\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Michael/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:13: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_agg(X, linkage, metric):\n",
    "    aggl = scipy.cluster.hierarchy.linkage(X, method=linkage, metric=metric)\n",
    "    \n",
    "    labels = scipy.cluster.hierarchy.fcluster(aggl, 3, criterion=\"maxclust\")\n",
    "    \n",
    "    sil = 0\n",
    "    n = len(set(labels))\n",
    "    if n > 1:\n",
    "        sil = silhouette_score(X , labels, metric=metric)\n",
    "    print(\"Linkage={}, Metric={}, Clusters={}, Silhouette={:.3}\".format(linkage, metric, n, sil))\n",
    "    \n",
    "    # Plot the dendogram\n",
    "    plt.figure(figsize=(12, 5))  \n",
    "    plt.title(\"Dendogram (Linkage={}, Distance={}, N={}, Sil={:.3f})\".format(linkage, metric, n, sil))  \n",
    "    dend = scipy.cluster.hierarchy.dendrogram(aggl) \n",
    "    \n",
    "linkages = ['complete', 'single', 'centroid', 'average']\n",
    "metrics = ['euclidean', 'minkowski', 'cityblock', 'cosine', 'correlation', 'chebyshev', 'mahalanobis']\n",
    "\n",
    "for prod in list(itertools.product(linkages, metrics)):\n",
    "    \n",
    "    # Some combos are not allowed\n",
    "    if (prod[0] in [ 'centroid']) and prod[1] != 'euclidean':\n",
    "        continue\n",
    "        \n",
    "    plot_agg(X, prod[0], prod[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='cosine', compute_full_tree='auto',\n",
       "                        connectivity=None, distance_threshold=None,\n",
       "                        linkage='complete', memory=None, n_clusters=3,\n",
       "                        pooling_func='deprecated')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg = AgglomerativeClustering(n_clusters=3, affinity='cosine', linkage='complete')\n",
    "agg.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 2, ..., 2, 0, 2])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "4338"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "4338"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg.labels_\n",
    "len(agg.labels_)\n",
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['label']=agg.labels_\n",
    "rfmTable['label']=agg.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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_value</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25.850254</td>\n",
       "      <td>308.436548</td>\n",
       "      <td>7929.100063</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.167708</td>\n",
       "      <td>473.245234</td>\n",
       "      <td>20017.722830</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>532.960000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>2607.340000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>3774.170000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.250000</td>\n",
       "      <td>329.250000</td>\n",
       "      <td>6210.242500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>326.000000</td>\n",
       "      <td>7847.000000</td>\n",
       "      <td>280206.020000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          recency    frequency  monetary_value  label\n",
       "count  788.000000   788.000000      788.000000  788.0\n",
       "mean    25.850254   308.436548     7929.100063    0.0\n",
       "std     29.167708   473.245234    20017.722830    0.0\n",
       "min      1.000000     1.000000      532.960000    0.0\n",
       "25%      5.000000   154.000000     2607.340000    0.0\n",
       "50%     16.000000   216.000000     3774.170000    0.0\n",
       "75%     35.250000   329.250000     6210.242500    0.0\n",
       "max    326.000000  7847.000000   280206.020000    0.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable.loc[rfmTable['label']==0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1320.000000</td>\n",
       "      <td>1320.000000</td>\n",
       "      <td>1320.000000</td>\n",
       "      <td>1320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>224.425758</td>\n",
       "      <td>31.739394</td>\n",
       "      <td>576.569236</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>77.410961</td>\n",
       "      <td>35.870548</td>\n",
       "      <td>770.469744</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>158.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>183.550000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>217.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>335.125000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>283.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>632.302500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>374.000000</td>\n",
       "      <td>327.000000</td>\n",
       "      <td>9864.260000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           recency    frequency  monetary_value   label\n",
       "count  1320.000000  1320.000000     1320.000000  1320.0\n",
       "mean    224.425758    31.739394      576.569236     1.0\n",
       "std      77.410961    35.870548      770.469744     0.0\n",
       "min      96.000000     1.000000        3.750000     1.0\n",
       "25%     158.000000    10.000000      183.550000     1.0\n",
       "50%     217.000000    20.000000      335.125000     1.0\n",
       "75%     283.000000    40.000000      632.302500     1.0\n",
       "max     374.000000   327.000000     9864.260000     1.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable.loc[rfmTable['label']==1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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_value</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2230.000000</td>\n",
       "      <td>2230.000000</td>\n",
       "      <td>2230.000000</td>\n",
       "      <td>2230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.031839</td>\n",
       "      <td>50.645740</td>\n",
       "      <td>853.007024</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27.766578</td>\n",
       "      <td>39.823704</td>\n",
       "      <td>675.987465</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>328.612500</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>656.960000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>73.750000</td>\n",
       "      <td>1215.545000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>113.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>3861.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           recency    frequency  monetary_value   label\n",
       "count  2230.000000  2230.000000     2230.000000  2230.0\n",
       "mean     38.031839    50.645740      853.007024     2.0\n",
       "std      27.766578    39.823704      675.987465     0.0\n",
       "min       1.000000     1.000000        6.200000     2.0\n",
       "25%      15.000000    19.000000      328.612500     2.0\n",
       "50%      31.000000    40.000000      656.960000     2.0\n",
       "75%      59.000000    73.750000     1215.545000     2.0\n",
       "max     113.000000   201.000000     3861.000000     2.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable.loc[rfmTable['label']==2].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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