ML_5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Title = 5. Implement K-Means clustering/ hierarchical clustering on sales_data_sample.csv dataset. Determine the number of clusters using the elbow method.\n",
    "# Name = Tanmay Shrikrishna Badhe\n",
    "# Div = B \n",
    "# Roll No. 01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('sales_data_sample.csv', encoding='unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of       ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0           10107               30      95.70                2  2871.00   \n",
       "1           10121               34      81.35                5  2765.90   \n",
       "2           10134               41      94.74                2  3884.34   \n",
       "3           10145               45      83.26                6  3746.70   \n",
       "4           10159               49     100.00               14  5205.27   \n",
       "...           ...              ...        ...              ...      ...   \n",
       "2818        10350               20     100.00               15  2244.40   \n",
       "2819        10373               29     100.00                1  3978.51   \n",
       "2820        10386               43     100.00                4  5417.57   \n",
       "2821        10397               34      62.24                1  2116.16   \n",
       "2822        10414               47      65.52                9  3079.44   \n",
       "\n",
       "            ORDERDATE    STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...  \\\n",
       "0      2/24/2003 0:00   Shipped       1         2     2003  ...   \n",
       "1       5/7/2003 0:00   Shipped       2         5     2003  ...   \n",
       "2       7/1/2003 0:00   Shipped       3         7     2003  ...   \n",
       "3      8/25/2003 0:00   Shipped       3         8     2003  ...   \n",
       "4     10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "...               ...       ...     ...       ...      ...  ...   \n",
       "2818   12/2/2004 0:00   Shipped       4        12     2004  ...   \n",
       "2819   1/31/2005 0:00   Shipped       1         1     2005  ...   \n",
       "2820    3/1/2005 0:00  Resolved       1         3     2005  ...   \n",
       "2821   3/28/2005 0:00   Shipped       1         3     2005  ...   \n",
       "2822    5/6/2005 0:00   On Hold       2         5     2005  ...   \n",
       "\n",
       "                       ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue           NaN            NYC    NY   \n",
       "1                59 rue de l'Abbaye           NaN          Reims   NaN   \n",
       "2     27 rue du Colonel Pierre Avia           NaN          Paris   NaN   \n",
       "3                78934 Hillside Dr.           NaN       Pasadena    CA   \n",
       "4                   7734 Strong St.           NaN  San Francisco    CA   \n",
       "...                             ...           ...            ...   ...   \n",
       "2818             C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "2819                    Torikatu 38           NaN           Oulu   NaN   \n",
       "2820             C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "2821          1 rue Alsace-Lorraine           NaN       Toulouse   NaN   \n",
       "2822             8616 Spinnaker Dr.           NaN         Boston    MA   \n",
       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         10022      USA       NaN              Yu             Kwai    Small  \n",
       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA       NaN           Young            Julie   Medium  \n",
       "4           NaN      USA       NaN           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA       NaN         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 25 columns]>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0           10107               30      95.70                2  2871.00   \n",
       "1           10121               34      81.35                5  2765.90   \n",
       "2           10134               41      94.74                2  3884.34   \n",
       "3           10145               45      83.26                6  3746.70   \n",
       "4           10159               49     100.00               14  5205.27   \n",
       "...           ...              ...        ...              ...      ...   \n",
       "2818        10350               20     100.00               15  2244.40   \n",
       "2819        10373               29     100.00                1  3978.51   \n",
       "2820        10386               43     100.00                4  5417.57   \n",
       "2821        10397               34      62.24                1  2116.16   \n",
       "2822        10414               47      65.52                9  3079.44   \n",
       "\n",
       "            ORDERDATE    STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...  \\\n",
       "0      2/24/2003 0:00   Shipped       1         2     2003  ...   \n",
       "1       5/7/2003 0:00   Shipped       2         5     2003  ...   \n",
       "2       7/1/2003 0:00   Shipped       3         7     2003  ...   \n",
       "3      8/25/2003 0:00   Shipped       3         8     2003  ...   \n",
       "4     10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "...               ...       ...     ...       ...      ...  ...   \n",
       "2818   12/2/2004 0:00   Shipped       4        12     2004  ...   \n",
       "2819   1/31/2005 0:00   Shipped       1         1     2005  ...   \n",
       "2820    3/1/2005 0:00  Resolved       1         3     2005  ...   \n",
       "2821   3/28/2005 0:00   Shipped       1         3     2005  ...   \n",
       "2822    5/6/2005 0:00   On Hold       2         5     2005  ...   \n",
       "\n",
       "                       ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue           NaN            NYC    NY   \n",
       "1                59 rue de l'Abbaye           NaN          Reims   NaN   \n",
       "2     27 rue du Colonel Pierre Avia           NaN          Paris   NaN   \n",
       "3                78934 Hillside Dr.           NaN       Pasadena    CA   \n",
       "4                   7734 Strong St.           NaN  San Francisco    CA   \n",
       "...                             ...           ...            ...   ...   \n",
       "2818             C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "2819                    Torikatu 38           NaN           Oulu   NaN   \n",
       "2820             C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "2821          1 rue Alsace-Lorraine           NaN       Toulouse   NaN   \n",
       "2822             8616 Spinnaker Dr.           NaN         Boston    MA   \n",
       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         10022      USA       NaN              Yu             Kwai    Small  \n",
       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA       NaN           Young            Julie   Medium  \n",
       "4           NaN      USA       NaN           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA       NaN         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 25 columns]>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Columns to Remove\n",
    "to_drop = ['ADDRESSLINE1', 'ADDRESSLINE2', 'STATE', 'POSTALCODE', 'PHONE']\n",
    "df = df.drop(to_drop, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ORDERNUMBER            0\n",
       "QUANTITYORDERED        0\n",
       "PRICEEACH              0\n",
       "ORDERLINENUMBER        0\n",
       "SALES                  0\n",
       "ORDERDATE              0\n",
       "STATUS                 0\n",
       "QTR_ID                 0\n",
       "MONTH_ID               0\n",
       "YEAR_ID                0\n",
       "PRODUCTLINE            0\n",
       "MSRP                   0\n",
       "PRODUCTCODE            0\n",
       "CUSTOMERNAME           0\n",
       "CITY                   0\n",
       "COUNTRY                0\n",
       "TERRITORY           1074\n",
       "CONTACTLASTNAME        0\n",
       "CONTACTFIRSTNAME       0\n",
       "DEALSIZE               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bhai bhai look at territory\n",
    "#But territory does not have significant impact on analysis, let it be "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ORDERNUMBER           int64\n",
       "QUANTITYORDERED       int64\n",
       "PRICEEACH           float64\n",
       "ORDERLINENUMBER       int64\n",
       "SALES               float64\n",
       "ORDERDATE            object\n",
       "STATUS               object\n",
       "QTR_ID                int64\n",
       "MONTH_ID              int64\n",
       "YEAR_ID               int64\n",
       "PRODUCTLINE          object\n",
       "MSRP                  int64\n",
       "PRODUCTCODE          object\n",
       "CUSTOMERNAME         object\n",
       "CITY                 object\n",
       "COUNTRY              object\n",
       "TERRITORY            object\n",
       "CONTACTLASTNAME      object\n",
       "CONTACTFIRSTNAME     object\n",
       "DEALSIZE             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ORDERDATE Should be in date time\n",
    "df['ORDERDATE'] = pd.to_datetime(df['ORDERDATE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We need to create some features in order to create cluseters\n",
    "#Recency: Number of days between customer's latest order and today's date\n",
    "#Frequency : Number of purchases by the customers\n",
    "#MonetaryValue : Revenue generated by the customers\n",
    "import datetime as dt\n",
    "snapshot_date = df['ORDERDATE'].max() + dt.timedelta(days = 1)\n",
    "df_RFM = df.groupby(['CUSTOMERNAME']).agg({\n",
    "    'ORDERDATE' : lambda x : (snapshot_date - x.max()).days,\n",
    "    'ORDERNUMBER' : 'count',\n",
    "    'SALES' : 'sum'\n",
    "})\n",
    "\n",
    "#Rename the columns\n",
    "df_RFM.rename(columns = {\n",
    "    'ORDERDATE' : 'Recency',\n",
    "    'ORDERNUMBER' : 'Frequency',\n",
    "    'SALES' : 'MonetaryValue'\n",
    "}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AV Stores, Co.</th>\n",
       "      <td>196</td>\n",
       "      <td>51</td>\n",
       "      <td>157807.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>65</td>\n",
       "      <td>20</td>\n",
       "      <td>70488.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>265</td>\n",
       "      <td>26</td>\n",
       "      <td>94117.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>84</td>\n",
       "      <td>46</td>\n",
       "      <td>153996.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>188</td>\n",
       "      <td>7</td>\n",
       "      <td>24179.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Recency  Frequency  MonetaryValue\n",
       "CUSTOMERNAME                                              \n",
       "AV Stores, Co.               196         51      157807.81\n",
       "Alpha Cognac                  65         20       70488.44\n",
       "Amica Models & Co.           265         26       94117.26\n",
       "Anna's Decorations, Ltd       84         46      153996.13\n",
       "Atelier graphique            188          7       24179.96"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_RFM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>M</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AV Stores, Co.</th>\n",
       "      <td>196</td>\n",
       "      <td>51</td>\n",
       "      <td>157807.81</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>65</td>\n",
       "      <td>20</td>\n",
       "      <td>70488.44</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>265</td>\n",
       "      <td>26</td>\n",
       "      <td>94117.26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>84</td>\n",
       "      <td>46</td>\n",
       "      <td>153996.13</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>188</td>\n",
       "      <td>7</td>\n",
       "      <td>24179.96</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                         Recency  Frequency  MonetaryValue  M  R  F\n",
       "CUSTOMERNAME                                                       \n",
       "AV Stores, Co.               196         51      157807.81  4  2  4\n",
       "Alpha Cognac                  65         20       70488.44  2  4  2\n",
       "Amica Models & Co.           265         26       94117.26  3  1  2\n",
       "Anna's Decorations, Ltd       84         46      153996.13  4  3  4\n",
       "Atelier graphique            188          7       24179.96  1  2  1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Divide into segments\n",
    "# We create 4 quartile ranges\n",
    "df_RFM['M'] = pd.qcut(df_RFM['MonetaryValue'], q = 4, labels = range(1,5))\n",
    "df_RFM['R'] = pd.qcut(df_RFM['Recency'], q = 4, labels = list(range(4,0,-1)))\n",
    "df_RFM['F'] = pd.qcut(df_RFM['Frequency'], q = 4, labels = range(1,5))\n",
    "\n",
    "df_RFM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Frequency</th>\n",
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       "      <th>M</th>\n",
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       "      <th>F</th>\n",
       "      <th>RFM_Score</th>\n",
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       "    <tr>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th></th>\n",
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       "      <th>AV Stores, Co.</th>\n",
       "      <td>196</td>\n",
       "      <td>51</td>\n",
       "      <td>157807.81</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>65</td>\n",
       "      <td>20</td>\n",
       "      <td>70488.44</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>265</td>\n",
       "      <td>26</td>\n",
       "      <td>94117.26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>84</td>\n",
       "      <td>46</td>\n",
       "      <td>153996.13</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>188</td>\n",
       "      <td>7</td>\n",
       "      <td>24179.96</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Recency  Frequency  MonetaryValue  M  R  F  RFM_Score\n",
       "CUSTOMERNAME                                                                  \n",
       "AV Stores, Co.               196         51      157807.81  4  2  4         10\n",
       "Alpha Cognac                  65         20       70488.44  2  4  2          8\n",
       "Amica Models & Co.           265         26       94117.26  3  1  2          6\n",
       "Anna's Decorations, Ltd       84         46      153996.13  4  3  4         11\n",
       "Atelier graphique            188          7       24179.96  1  2  1          4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create another column for RFM score\n",
    "df_RFM['RFM_Score'] = df_RFM[['R', 'M', 'F']].sum(axis=1)\n",
    "df_RFM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We create levels for our Customers\n",
    "### RFM Score > 10 : High Value Customers\n",
    "### RFM Score < 10 and RFM Score >= 6 : Mid Value Customers\n",
    "### RFM Score < 6 : Low Value Customers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\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>MonetaryValue</th>\n",
       "      <th>M</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>RFM_Score</th>\n",
       "      <th>RFM_Level</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</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>AV Stores, Co.</th>\n",
       "      <td>196</td>\n",
       "      <td>51</td>\n",
       "      <td>157807.81</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>High Value Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>65</td>\n",
       "      <td>20</td>\n",
       "      <td>70488.44</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>Mid Value Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>265</td>\n",
       "      <td>26</td>\n",
       "      <td>94117.26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Mid Value Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>84</td>\n",
       "      <td>46</td>\n",
       "      <td>153996.13</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>High Value Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>188</td>\n",
       "      <td>7</td>\n",
       "      <td>24179.96</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Low Value Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Recency  Frequency  MonetaryValue  M  R  F  \\\n",
       "CUSTOMERNAME                                                          \n",
       "AV Stores, Co.               196         51      157807.81  4  2  4   \n",
       "Alpha Cognac                  65         20       70488.44  2  4  2   \n",
       "Amica Models & Co.           265         26       94117.26  3  1  2   \n",
       "Anna's Decorations, Ltd       84         46      153996.13  4  3  4   \n",
       "Atelier graphique            188          7       24179.96  1  2  1   \n",
       "\n",
       "                         RFM_Score            RFM_Level  \n",
       "CUSTOMERNAME                                             \n",
       "AV Stores, Co.                  10  High Value Customer  \n",
       "Alpha Cognac                     8   Mid Value Customer  \n",
       "Amica Models & Co.               6   Mid Value Customer  \n",
       "Anna's Decorations, Ltd         11  High Value Customer  \n",
       "Atelier graphique                4   Low Value Customer  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rfm_level(df):\n",
    "    if bool(df['RFM_Score'] >= 10):\n",
    "        return 'High Value Customer'\n",
    "    \n",
    "    elif bool(df['RFM_Score'] < 10) and bool(df['RFM_Score'] >= 6):\n",
    "        return 'Mid Value Customer'\n",
    "    else:\n",
    "        return 'Low Value Customer'\n",
    "df_RFM['RFM_Level'] = df_RFM.apply(rfm_level, axis = 1)\n",
    "df_RFM.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>MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AV Stores, Co.</th>\n",
       "      <td>196</td>\n",
       "      <td>51</td>\n",
       "      <td>157807.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>65</td>\n",
       "      <td>20</td>\n",
       "      <td>70488.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>265</td>\n",
       "      <td>26</td>\n",
       "      <td>94117.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>84</td>\n",
       "      <td>46</td>\n",
       "      <td>153996.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>188</td>\n",
       "      <td>7</td>\n",
       "      <td>24179.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Recency  Frequency  MonetaryValue\n",
       "CUSTOMERNAME                                              \n",
       "AV Stores, Co.               196         51      157807.81\n",
       "Alpha Cognac                  65         20       70488.44\n",
       "Amica Models & Co.           265         26       94117.26\n",
       "Anna's Decorations, Ltd       84         46      153996.13\n",
       "Atelier graphique            188          7       24179.96"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Time to perform KMeans\n",
    "data = df_RFM[['Recency', 'Frequency', 'MonetaryValue']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AV Stores, Co.</th>\n",
       "      <td>5.278115</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>11.969133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>4.174387</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>11.163204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>5.579730</td>\n",
       "      <td>3.258097</td>\n",
       "      <td>11.452297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>4.430817</td>\n",
       "      <td>3.828641</td>\n",
       "      <td>11.944683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>5.236442</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>10.093279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Recency  Frequency  MonetaryValue\n",
       "CUSTOMERNAME                                               \n",
       "AV Stores, Co.           5.278115   3.931826      11.969133\n",
       "Alpha Cognac             4.174387   2.995732      11.163204\n",
       "Amica Models & Co.       5.579730   3.258097      11.452297\n",
       "Anna's Decorations, Ltd  4.430817   3.828641      11.944683\n",
       "Atelier graphique        5.236442   1.945910      10.093279"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our data is skewed we must remove it by performing log transformation\n",
    "data_log = np.log(data)\n",
    "data_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>92.00</td>\n",
       "      <td>92.00</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.51</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.12</td>\n",
       "      <td>4.03</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recency  Frequency  MonetaryValue\n",
       "count    92.00      92.00          92.00\n",
       "mean      0.00      -0.00           0.00\n",
       "std       1.01       1.01           1.01\n",
       "min      -3.51      -3.67          -3.82\n",
       "25%      -0.24      -0.41          -0.39\n",
       "50%       0.37       0.06          -0.04\n",
       "75%       0.53       0.45           0.52\n",
       "max       1.12       4.03           3.92"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Standardization \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(data_log)\n",
    "data_normalized = scaler.transform(data_log)\n",
    "data_normalized = pd.DataFrame(data_normalized, index = data_log.index, columns=data_log.columns)\n",
    "data_normalized.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#Fit KMeans and use elbow method to choose the number of clusters\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "sse = {}\n",
    "\n",
    "for k in range(1, 21):\n",
    "    kmeans = KMeans(n_clusters = k, random_state = 1)\n",
    "    kmeans.fit(data_normalized)\n",
    "    sse[k] = kmeans.inertia_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.title('The Elbow Method')\n",
    "\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('SSE')\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "sns.pointplot(x=list(sse.keys()), y = list(sse.values()))\n",
    "plt.text(4.5, 60, \"Largest Angle\", bbox = dict(facecolor = 'lightgreen', alpha = 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Coding\\jupyterNotebook\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AV Stores, Co.</th>\n",
       "      <td>196</td>\n",
       "      <td>51</td>\n",
       "      <td>157807.81</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>65</td>\n",
       "      <td>20</td>\n",
       "      <td>70488.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>265</td>\n",
       "      <td>26</td>\n",
       "      <td>94117.26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>84</td>\n",
       "      <td>46</td>\n",
       "      <td>153996.13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>188</td>\n",
       "      <td>7</td>\n",
       "      <td>24179.96</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Recency  Frequency  MonetaryValue  Cluster\n",
       "CUSTOMERNAME                                                       \n",
       "AV Stores, Co.               196         51      157807.81        3\n",
       "Alpha Cognac                  65         20       70488.44        0\n",
       "Amica Models & Co.           265         26       94117.26        0\n",
       "Anna's Decorations, Ltd       84         46      153996.13        3\n",
       "Atelier graphique            188          7       24179.96        2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 number of clusters seems good\n",
    "kmeans = KMeans(n_clusters=5, random_state=1)\n",
    "kmeans.fit(data_normalized)\n",
    "cluster_labels = kmeans.labels_\n",
    "\n",
    "data_rfm = data.assign(Cluster = cluster_labels)\n",
    "data_rfm.head()"
   ]
  }
 ],
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