Scikit - 28 Clustering - Online Retail Sales Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering Online Retail Sales Data\n",
    "\n",
    "Dataset: https://archive.ics.uci.edu/ml/datasets/online+retail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing, metrics, cluster\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"/data/Online Retail.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out if there are any nan in the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      "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"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      False\n",
       "StockCode      False\n",
       "Description     True\n",
       "Quantity       False\n",
       "InvoiceDate    False\n",
       "UnitPrice      False\n",
       "CustomerID      True\n",
       "Country        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop all records having nan CustomerId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 406829 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      "InvoiceNo      406829 non-null object\n",
      "StockCode      406829 non-null object\n",
      "Description    406829 non-null object\n",
      "Quantity       406829 non-null int64\n",
      "InvoiceDate    406829 non-null datetime64[ns]\n",
      "UnitPrice      406829 non-null float64\n",
      "CustomerID     406829 non-null float64\n",
      "Country        406829 non-null object\n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 27.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df = df[~df.CustomerID.isna()]\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find number of unique values for each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      22190\n",
       "StockCode       3684\n",
       "Description     3896\n",
       "Quantity         436\n",
       "InvoiceDate    20460\n",
       "UnitPrice        620\n",
       "CustomerID      4372\n",
       "Country           37\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use describe to find range of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>406829.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.061303</td>\n",
       "      <td>3.460471</td>\n",
       "      <td>15287.690570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>248.693370</td>\n",
       "      <td>69.315162</td>\n",
       "      <td>1713.600303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>15152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.750000</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  406829.000000  406829.000000  406829.000000\n",
       "mean       12.061303       3.460471   15287.690570\n",
       "std       248.693370      69.315162    1713.600303\n",
       "min    -80995.000000       0.000000   12346.000000\n",
       "25%         2.000000       1.250000   13953.000000\n",
       "50%         5.000000       1.950000   15152.000000\n",
       "75%        12.000000       3.750000   16791.000000\n",
       "max     80995.000000   38970.000000   18287.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see quantity column has a large negative value. Probably negative values are not valid for this analysis. Drop the records having negative values in frequency. Verify that there is not more nagative values in the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 397924 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      "InvoiceNo      397924 non-null object\n",
      "StockCode      397924 non-null object\n",
      "Description    397924 non-null object\n",
      "Quantity       397924 non-null int64\n",
      "InvoiceDate    397924 non-null datetime64[ns]\n",
      "UnitPrice      397924 non-null float64\n",
      "CustomerID     397924 non-null float64\n",
      "Country        397924 non-null object\n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 27.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df = df[df.Quantity>0]\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>397924.000000</td>\n",
       "      <td>397924.000000</td>\n",
       "      <td>397924.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.021823</td>\n",
       "      <td>3.116174</td>\n",
       "      <td>15294.315171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>180.420210</td>\n",
       "      <td>22.096788</td>\n",
       "      <td>1713.169877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>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  397924.000000  397924.000000  397924.000000\n",
       "mean       13.021823       3.116174   15294.315171\n",
       "std       180.420210      22.096788    1713.169877\n",
       "min         1.000000       0.000000   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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the InvoiceDate to datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.InvoiceDate = pd.to_datetime(df.InvoiceDate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create caculated field to computee TotalPrice "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>TotalPrice</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
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       "      <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",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"TotalPrice\"] = df.Quantity * df.UnitPrice\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For calculating rececency, use max for InvoiceDate as point of reference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-09 12:50:00')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_date = df.InvoiceDate.max()\n",
    "last_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the R-F-M."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "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>InvoiceDate</th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>309</td>\n",
       "      <td>17</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            InvoiceDate  InvoiceNo  TotalPrice\n",
       "CustomerID                                    \n",
       "12346.0             325          1    77183.60\n",
       "12347.0               1        182     4310.00\n",
       "12348.0              74         31     1797.24\n",
       "12349.0              18         73     1757.55\n",
       "12350.0             309         17      334.40"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm = df.groupby(\"CustomerID\").agg({\n",
    "    \"InvoiceDate\": lambda values: (last_date - values.max()).days,\n",
    "    \"InvoiceNo\" : lambda values: len(values),\n",
    "    \"TotalPrice\": lambda values: np.sum(values)\n",
    "})\n",
    "\n",
    "rfm.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the columns - \"recency\", \"frequency\", \"monetary\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>74</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>12349.0</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary\n",
       "CustomerID                              \n",
       "12346.0         325          1  77183.60\n",
       "12347.0           1        182   4310.00\n",
       "12348.0          74         31   1797.24\n",
       "12349.0          18         73   1757.55\n",
       "12350.0         309         17    334.40"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.columns = [\"recency\", \"frequency\", \"monetary\"]\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Digitize the columns for R-F-M into 5 equal buckets. To achieve this, find percentile values as bucket boundaries. These will create 5 buckets of equal sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20,  40,  60,  80, 100])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles = np.arange(1, 6) * 20\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>r_score</th>\n",
       "      <th>m_score</th>\n",
       "      <th>f_score</th>\n",
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       "    <tr>\n",
       "      <th>CustomerID</th>\n",
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       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>18</td>\n",
       "      <td>73</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>309</td>\n",
       "      <td>17</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary  r_score  m_score  f_score\n",
       "CustomerID                                                         \n",
       "12346.0         325          1  77183.60        1        5        1\n",
       "12347.0           1        182   4310.00        5        5        5\n",
       "12348.0          74         31   1797.24        2        4        3\n",
       "12349.0          18         73   1757.55        4        4        4\n",
       "12350.0         309         17    334.40        1        2        2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm[\"r_score\"] = np.digitize(rfm.recency, bins = np.percentile(rfm.recency, quantiles)\n",
    "                           , right=True)\n",
    "\n",
    "rfm[\"m_score\"] = np.digitize(rfm.monetary, bins = np.percentile(rfm.monetary, quantiles)\n",
    "                           , right=True)\n",
    "\n",
    "rfm[\"f_score\"] = np.digitize(rfm.frequency, bins = np.percentile(rfm.frequency, quantiles)\n",
    "                           , right=True)\n",
    "\n",
    "rfm[\"r_score\"] = 4 - rfm[\"r_score\"]\n",
    "\n",
    "rfm[\"r_score\"] = rfm[\"r_score\"] + 1\n",
    "rfm[\"f_score\"] = rfm[\"f_score\"] + 1\n",
    "rfm[\"m_score\"] = rfm[\"m_score\"] + 1\n",
    "\n",
    "\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>r_score</th>\n",
       "      <th>m_score</th>\n",
       "      <th>f_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13037.0</th>\n",
       "      <td>137</td>\n",
       "      <td>48</td>\n",
       "      <td>881.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15864.0</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>1936.32</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13848.0</th>\n",
       "      <td>91</td>\n",
       "      <td>5</td>\n",
       "      <td>1255.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15274.0</th>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>716.57</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17865.0</th>\n",
       "      <td>30</td>\n",
       "      <td>370</td>\n",
       "      <td>10526.32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17398.0</th>\n",
       "      <td>114</td>\n",
       "      <td>38</td>\n",
       "      <td>653.38</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12945.0</th>\n",
       "      <td>287</td>\n",
       "      <td>23</td>\n",
       "      <td>462.95</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16737.0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>417.60</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14796.0</th>\n",
       "      <td>0</td>\n",
       "      <td>1141</td>\n",
       "      <td>8022.49</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16657.0</th>\n",
       "      <td>9</td>\n",
       "      <td>208</td>\n",
       "      <td>933.62</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary  r_score  m_score  f_score\n",
       "CustomerID                                                         \n",
       "13037.0         137         48    881.07        2        3        3\n",
       "15864.0          21         20   1936.32        4        4        2\n",
       "13848.0          91          5   1255.00        2        4        1\n",
       "15274.0           3         47    716.57        5        3        3\n",
       "17865.0          30        370  10526.32        4        5        5\n",
       "17398.0         114         38    653.38        2        3        3\n",
       "12945.0         287         23    462.95        1        2        2\n",
       "16737.0          52          1    417.60        3        2        1\n",
       "14796.0           0       1141   8022.49        5        5        5\n",
       "16657.0           9        208    933.62        5        3        5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.sample(10, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.4252185 , -1.3840666 ,  1.4140507 ],\n",
       "       [ 1.4030564 ,  1.4140533 ,  1.4140507 ],\n",
       "       [-0.7181498 ,  0.01499338,  0.70702535],\n",
       "       ...,\n",
       "       [ 1.4030564 , -1.3840666 , -1.4140507 ],\n",
       "       [ 1.4030564 ,  1.4140533 ,  1.4140507 ],\n",
       "       [-0.01108105,  0.7145233 ,  0.70702535]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = preprocessing.StandardScaler()\n",
    "X = rfm[[\"r_score\", \"f_score\", \"m_score\"]].values\n",
    "X = scaler.fit_transform(X.astype(\"float32\"))\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find what could be an optimal number of clusters using elbow plot. As we see in the plot below, we can use 5 or 6 number of clusters (K) for KMeans algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Inertia Score')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "inertias = {}\n",
    "for k in range(2, 10): \n",
    "    kmeans = cluster.KMeans(n_clusters=k, random_state=1)\n",
    "    kmeans.fit(X)\n",
    "    inertias[k] = kmeans.inertia_\n",
    "    \n",
    "pd.Series(inertias).plot()\n",
    "plt.xlabel(\"K (num of clusters)\")\n",
    "plt.ylabel(\"Inertia Score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 5\n",
    "kmeans = cluster.KMeans(n_clusters=k, random_state = 1)\n",
    "rfm[\"cluster\"] = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1163\n",
       "1    1136\n",
       "0     777\n",
       "3     668\n",
       "4     595\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.cluster.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>r_score</th>\n",
       "      <th>m_score</th>\n",
       "      <th>f_score</th>\n",
       "      <th>cluster</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13591.0</th>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>1117.13</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.835705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13630.0</th>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>2031.78</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.763178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16658.0</th>\n",
       "      <td>367</td>\n",
       "      <td>8</td>\n",
       "      <td>123.24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.692686</td>\n",
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       "    <tr>\n",
       "      <th>14346.0</th>\n",
       "      <td>120</td>\n",
       "      <td>60</td>\n",
       "      <td>1021.58</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.352840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17119.0</th>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>606.91</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.712960</td>\n",
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       "    <tr>\n",
       "      <th>14340.0</th>\n",
       "      <td>218</td>\n",
       "      <td>6</td>\n",
       "      <td>134.70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.692686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14880.0</th>\n",
       "      <td>76</td>\n",
       "      <td>47</td>\n",
       "      <td>706.19</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.645417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15503.0</th>\n",
       "      <td>362</td>\n",
       "      <td>70</td>\n",
       "      <td>147.09</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.784894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13721.0</th>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>524.14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.973406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15355.0</th>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>277.05</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.581352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13089.0</th>\n",
       "      <td>2</td>\n",
       "      <td>1818</td>\n",
       "      <td>58825.83</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.595008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13199.0</th>\n",
       "      <td>3</td>\n",
       "      <td>119</td>\n",
       "      <td>9817.12</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.697552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14727.0</th>\n",
       "      <td>273</td>\n",
       "      <td>10</td>\n",
       "      <td>268.58</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.534157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15683.0</th>\n",
       "      <td>116</td>\n",
       "      <td>10</td>\n",
       "      <td>193.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.769721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13447.0</th>\n",
       "      <td>22</td>\n",
       "      <td>59</td>\n",
       "      <td>1129.14</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.662144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14555.0</th>\n",
       "      <td>56</td>\n",
       "      <td>21</td>\n",
       "      <td>324.14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.652340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12890.0</th>\n",
       "      <td>23</td>\n",
       "      <td>82</td>\n",
       "      <td>380.47</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.002155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16561.0</th>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>511.12</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.712960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16363.0</th>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>109.36</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.728848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17865.0</th>\n",
       "      <td>30</td>\n",
       "      <td>370</td>\n",
       "      <td>10526.32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.458289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary  r_score  m_score  f_score  cluster  \\\n",
       "CustomerID                                                                     \n",
       "13591.0          64         58   1117.13        3        4        3        0   \n",
       "13630.0           4         71   2031.78        5        4        4        2   \n",
       "16658.0         367          8    123.24        1        1        1        1   \n",
       "14346.0         120         60   1021.58        2        4        4        0   \n",
       "17119.0           2         35    606.91        5        3        3        3   \n",
       "14340.0         218          6    134.70        1        1        1        1   \n",
       "14880.0          76         47    706.19        2        3        3        0   \n",
       "15503.0         362         70    147.09        1        1        4        1   \n",
       "13721.0          35         28    524.14        3        3        2        3   \n",
       "15355.0          28         14    277.05        4        2        1        4   \n",
       "13089.0           2       1818  58825.83        5        5        5        2   \n",
       "13199.0           3        119   9817.12        5        5        4        2   \n",
       "14727.0         273         10    268.58        1        2        1        1   \n",
       "15683.0         116         10    193.75        2        1        1        1   \n",
       "13447.0          22         59   1129.14        4        4        4        2   \n",
       "14555.0          56         21    324.14        3        2        2        4   \n",
       "12890.0          23         82    380.47        4        2        4        3   \n",
       "16561.0           5         31    511.12        5        3        3        3   \n",
       "16363.0          52          5    109.36        3        1        1        4   \n",
       "17865.0          30        370  10526.32        4        5        5        2   \n",
       "\n",
       "            distance  \n",
       "CustomerID            \n",
       "13591.0     0.835705  \n",
       "13630.0     0.763178  \n",
       "16658.0     0.692686  \n",
       "14346.0     0.352840  \n",
       "17119.0     0.712960  \n",
       "14340.0     0.692686  \n",
       "14880.0     0.645417  \n",
       "15503.0     1.784894  \n",
       "13721.0     0.973406  \n",
       "15355.0     0.581352  \n",
       "13089.0     0.595008  \n",
       "13199.0     0.697552  \n",
       "14727.0     0.534157  \n",
       "15683.0     0.769721  \n",
       "13447.0     0.662144  \n",
       "14555.0     0.652340  \n",
       "12890.0     1.002155  \n",
       "16561.0     0.712960  \n",
       "16363.0     0.728848  \n",
       "17865.0     0.458289  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm[\"distance\"] = 0.0\n",
    "for i in range(k):\n",
    "    centroid = kmeans.cluster_centers_[i].reshape(1, -1)\n",
    "    cluster_points = X[rfm.cluster == i]\n",
    "    rfm[\"distance\"][rfm.cluster == i] = metrics.euclidean_distances(centroid, cluster_points).flatten()\n",
    "rfm.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.869923</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.755233</td>\n",
       "      <td>1136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.705951</td>\n",
       "      <td>1163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.835333</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.773771</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean  count\n",
       "cluster                 \n",
       "0        0.869923    777\n",
       "1        0.755233   1136\n",
       "2        0.705951   1163\n",
       "3        0.835333    668\n",
       "4        0.773771    595"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.groupby(\"cluster\").distance.agg([\"mean\", \"count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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