DC2C_Timothy_Divoll.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Credit Card User Segmentation\n",
    "\n",
    "Timothy Divoll - Insight Data Fellow, Boston"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In this problem, we want to segement credit card users for more targeted marketing strategies. A general approach would be to target three classes: Low-, Medium-, and High-Value customers. To do this, I'll first try an RFM approach to cluster customers by Recency, Frequency, and Monetary Value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know that there are 8950 rows of data, one per customer, with data on balances, spending, limits, tenure, cash advances, and tenure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metadata\n",
    "\n",
    "- CUST_ID : Identification of Credit Card holder (Categorical)\n",
    "- BALANCE : Balance amount left in their account to make purchases\n",
    "- BALANCE_FREQUENCY : How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated)\n",
    "- PURCHASES : Amount of purchases made from account\n",
    "- ONEOFF_PURCHASES : Maximum purchase amount done in one-go\n",
    "- INSTALLMENTS_PURCHASES : Amount of purchase done in installment\n",
    "- CASH_ADVANCE : Cash in advance given by the user\n",
    "- PURCHASES_FREQUENCY : How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased)\n",
    "- ONEOFFPURCHASESFREQUENCY : How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased)\n",
    "- PURCHASESINSTALLMENTSFREQUENCY : How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done)\n",
    "- CASHADVANCEFREQUENCY : How frequently the cash in advance being paid\n",
    "- CASHADVANCETRX : Number of Transactions made with \"Cash in Advanced\"\n",
    "- PURCHASES_TRX : Number of purchase transactions made\n",
    "- CREDIT_LIMIT : Limit of Credit Card for user\n",
    "- PAYMENTS : Amount of Payment done by user\n",
    "- MINIMUM_PAYMENTS : Minimum amount of payments made by user\n",
    "- PRCFULLPAYMENT : Percent of full payment paid by user\n",
    "- TENURE : Tenure of credit card service for user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>CUST_ID</th>\n",
       "      <th>BALANCE</th>\n",
       "      <th>BALANCE_FREQUENCY</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>ONEOFF_PURCHASES</th>\n",
       "      <th>INSTALLMENTS_PURCHASES</th>\n",
       "      <th>CASH_ADVANCE</th>\n",
       "      <th>PURCHASES_FREQUENCY</th>\n",
       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_TRX</th>\n",
       "      <th>PURCHASES_TRX</th>\n",
       "      <th>CREDIT_LIMIT</th>\n",
       "      <th>PAYMENTS</th>\n",
       "      <th>MINIMUM_PAYMENTS</th>\n",
       "      <th>PRC_FULL_PAYMENT</th>\n",
       "      <th>TENURE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C10001</td>\n",
       "      <td>40.900749</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>95.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>95.40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>201.802084</td>\n",
       "      <td>139.509787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C10002</td>\n",
       "      <td>3202.467416</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6442.945483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>4103.032597</td>\n",
       "      <td>1072.340217</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C10003</td>\n",
       "      <td>2495.148862</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>773.17</td>\n",
       "      <td>773.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>622.066742</td>\n",
       "      <td>627.284787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C10004</td>\n",
       "      <td>1666.670542</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>205.788017</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C10005</td>\n",
       "      <td>817.714335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>678.334763</td>\n",
       "      <td>244.791237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C10006</td>\n",
       "      <td>1809.828751</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1333.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1333.28</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1400.057770</td>\n",
       "      <td>2407.246035</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C10007</td>\n",
       "      <td>627.260806</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7091.01</td>\n",
       "      <td>6402.63</td>\n",
       "      <td>688.38</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>6354.314328</td>\n",
       "      <td>198.065894</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CUST_ID      BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "0  C10001    40.900749           0.818182      95.40              0.00   \n",
       "1  C10002  3202.467416           0.909091       0.00              0.00   \n",
       "2  C10003  2495.148862           1.000000     773.17            773.17   \n",
       "3  C10004  1666.670542           0.636364    1499.00           1499.00   \n",
       "4  C10005   817.714335           1.000000      16.00             16.00   \n",
       "5  C10006  1809.828751           1.000000    1333.28              0.00   \n",
       "6  C10007   627.260806           1.000000    7091.01           6402.63   \n",
       "\n",
       "   INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "0                   95.40      0.000000             0.166667   \n",
       "1                    0.00   6442.945483             0.000000   \n",
       "2                    0.00      0.000000             1.000000   \n",
       "3                    0.00    205.788017             0.083333   \n",
       "4                    0.00      0.000000             0.083333   \n",
       "5                 1333.28      0.000000             0.666667   \n",
       "6                  688.38      0.000000             1.000000   \n",
       "\n",
       "   ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "0                    0.000000                          0.083333   \n",
       "1                    0.000000                          0.000000   \n",
       "2                    1.000000                          0.000000   \n",
       "3                    0.083333                          0.000000   \n",
       "4                    0.083333                          0.000000   \n",
       "5                    0.000000                          0.583333   \n",
       "6                    1.000000                          1.000000   \n",
       "\n",
       "   CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
       "0                0.000000                 0              2        1000.0   \n",
       "1                0.250000                 4              0        7000.0   \n",
       "2                0.000000                 0             12        7500.0   \n",
       "3                0.083333                 1              1        7500.0   \n",
       "4                0.000000                 0              1        1200.0   \n",
       "5                0.000000                 0              8        1800.0   \n",
       "6                0.000000                 0             64       13500.0   \n",
       "\n",
       "      PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  \n",
       "0   201.802084        139.509787          0.000000      12  \n",
       "1  4103.032597       1072.340217          0.222222      12  \n",
       "2   622.066742        627.284787          0.000000      12  \n",
       "3     0.000000               NaN          0.000000      12  \n",
       "4   678.334763        244.791237          0.000000      12  \n",
       "5  1400.057770       2407.246035          0.000000      12  \n",
       "6  6354.314328        198.065894          1.000000      12  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "CCdf = pd.read_csv(\"/home/tjd/InsightFiles/cc_info.csv\")\n",
    "CCdf.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are not necessarily data columns for recency, frequency, and monetary value, so I will engineer some proxy features to represent each of these important categories. But first, I'll inspect the data to see if it needs any cleaning or if anything is amiss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8949"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CCdf.CUST_ID.nunique() # there are the same number of rows as expected customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "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>BALANCE</th>\n",
       "      <th>TENURE</th>\n",
       "      <th>PURCHASES_TRX</th>\n",
       "      <th>CREDIT_LIMIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8949.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1564.474828</td>\n",
       "      <td>11.517318</td>\n",
       "      <td>14.709832</td>\n",
       "      <td>4494.449450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2081.531879</td>\n",
       "      <td>1.338331</td>\n",
       "      <td>24.857649</td>\n",
       "      <td>3638.815725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>128.281915</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>873.385231</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2054.140036</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>6500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19043.138560</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>358.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BALANCE       TENURE  PURCHASES_TRX  CREDIT_LIMIT\n",
       "count   8950.000000  8950.000000    8950.000000   8949.000000\n",
       "mean    1564.474828    11.517318      14.709832   4494.449450\n",
       "std     2081.531879     1.338331      24.857649   3638.815725\n",
       "min        0.000000     6.000000       0.000000     50.000000\n",
       "25%      128.281915    12.000000       1.000000   1600.000000\n",
       "50%      873.385231    12.000000       7.000000   3000.000000\n",
       "75%     2054.140036    12.000000      17.000000   6500.000000\n",
       "max    19043.138560    12.000000     358.000000  30000.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descrip = CCdf[[\"BALANCE\", \"TENURE\", \"PURCHASES_TRX\", \"CREDIT_LIMIT\"]]\n",
    "descrip.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like some customers have zero balance and purchases while others have tens of thousands in balance and limit; there should be signal in the data to cluster into segments. Also, the tenure values are assumed to be either months or years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CUST_ID                               0\n",
       "BALANCE                               0\n",
       "BALANCE_FREQUENCY                     0\n",
       "PURCHASES                             0\n",
       "ONEOFF_PURCHASES                      0\n",
       "INSTALLMENTS_PURCHASES                0\n",
       "CASH_ADVANCE                          0\n",
       "PURCHASES_FREQUENCY                   0\n",
       "ONEOFF_PURCHASES_FREQUENCY            0\n",
       "PURCHASES_INSTALLMENTS_FREQUENCY      0\n",
       "CASH_ADVANCE_FREQUENCY                0\n",
       "CASH_ADVANCE_TRX                      0\n",
       "PURCHASES_TRX                         0\n",
       "CREDIT_LIMIT                          1\n",
       "PAYMENTS                              0\n",
       "MINIMUM_PAYMENTS                    313\n",
       "PRC_FULL_PAYMENT                      0\n",
       "TENURE                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "CCdf.replace([np.inf, -np.inf], np.nan)\n",
    "CCdf.isna().sum() # check for NA values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I don't plan on initially using the Minimum_Payments feature to cluster so I won't worry about that but will drop the one customer without limit data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "CCdf = CCdf[CCdf[\"CREDIT_LIMIT\"].notna()]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I'll plot the columns to see what outliers and distributions of data look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 3 is out of bounds for axis 0 with size 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-f82a960c3b82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mboxplots\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mboxplots\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: index 3 is out of bounds for axis 0 with size 3"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "boxplots = CCdf.select_dtypes(include='number')\n",
    "fig, axes = plt.subplots(nrows=3, ncols=5, figsize = (20,18))\n",
    "for i, column in enumerate(boxplots.columns):\n",
    "    sns.boxplot(boxplots[column],ax=axes[i//5,i%5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 3 is out of bounds for axis 0 with size 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-111-57812c3ab5ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistplots\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistplots\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: index 3 is out of bounds for axis 0 with size 3"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplots = CCdf.select_dtypes(include='number')\n",
    "distplots = distplots.dropna()\n",
    "fig, axes = plt.subplots(nrows=3, ncols=5, figsize = (20,18))\n",
    "for i, column in enumerate(distplots.columns):\n",
    "    sns.distplot(distplots[column], ax=axes[i//5,i%5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the columns really vary in scale and all are skewed. This will need to be accounted for if I decide to use any type of regression modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering for RFM clustering\n",
    "### Proxy for Recency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>CUST_ID</th>\n",
       "      <th>BALANCE</th>\n",
       "      <th>BALANCE_FREQUENCY</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>ONEOFF_PURCHASES</th>\n",
       "      <th>INSTALLMENTS_PURCHASES</th>\n",
       "      <th>CASH_ADVANCE</th>\n",
       "      <th>PURCHASES_FREQUENCY</th>\n",
       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_TRX</th>\n",
       "      <th>PURCHASES_TRX</th>\n",
       "      <th>CREDIT_LIMIT</th>\n",
       "      <th>PAYMENTS</th>\n",
       "      <th>MINIMUM_PAYMENTS</th>\n",
       "      <th>PRC_FULL_PAYMENT</th>\n",
       "      <th>TENURE</th>\n",
       "      <th>actproxy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C10001</td>\n",
       "      <td>40.900749</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>95.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>95.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>201.802084</td>\n",
       "      <td>139.509787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C10002</td>\n",
       "      <td>3202.467416</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6442.945483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>4103.032597</td>\n",
       "      <td>1072.340217</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C10003</td>\n",
       "      <td>2495.148862</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>773.17</td>\n",
       "      <td>773.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>622.066742</td>\n",
       "      <td>627.284787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C10004</td>\n",
       "      <td>1666.670542</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.788017</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C10005</td>\n",
       "      <td>817.714335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>678.334763</td>\n",
       "      <td>244.791237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CUST_ID      BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "0  C10001    40.900749           0.818182      95.40              0.00   \n",
       "1  C10002  3202.467416           0.909091       0.00              0.00   \n",
       "2  C10003  2495.148862           1.000000     773.17            773.17   \n",
       "3  C10004  1666.670542           0.636364    1499.00           1499.00   \n",
       "4  C10005   817.714335           1.000000      16.00             16.00   \n",
       "\n",
       "   INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "0                    95.4      0.000000             0.166667   \n",
       "1                     0.0   6442.945483             0.000000   \n",
       "2                     0.0      0.000000             1.000000   \n",
       "3                     0.0    205.788017             0.083333   \n",
       "4                     0.0      0.000000             0.083333   \n",
       "\n",
       "   ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "0                    0.000000                          0.083333   \n",
       "1                    0.000000                          0.000000   \n",
       "2                    1.000000                          0.000000   \n",
       "3                    0.083333                          0.000000   \n",
       "4                    0.083333                          0.000000   \n",
       "\n",
       "   CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
       "0                0.000000                 0              2        1000.0   \n",
       "1                0.250000                 4              0        7000.0   \n",
       "2                0.000000                 0             12        7500.0   \n",
       "3                0.083333                 1              1        7500.0   \n",
       "4                0.000000                 0              1        1200.0   \n",
       "\n",
       "      PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  actproxy  \n",
       "0   201.802084        139.509787          0.000000      12  0.166667  \n",
       "1  4103.032597       1072.340217          0.222222      12  0.000000  \n",
       "2   622.066742        627.284787          0.000000      12  1.000000  \n",
       "3     0.000000               NaN          0.000000      12  0.083333  \n",
       "4   678.334763        244.791237          0.000000      12  0.083333  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First divide the number of purchases by tenure to get a sense of how active each user is, as a proxy of recency. \n",
    "# I make the assumption here that higher values are more likely to indicate recent purchases.\n",
    "\n",
    "CCdf['actproxy'] = CCdf[\"PURCHASES_TRX\"]/CCdf[\"TENURE\"]\n",
    "CCdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8949.000000\n",
       "mean        1.259072\n",
       "std         2.090575\n",
       "min         0.000000\n",
       "25%         0.083333\n",
       "50%         0.666667\n",
       "75%         1.500000\n",
       "max        29.833333\n",
       "Name: actproxy, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CCdf.actproxy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count of Customers')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "params = {'axes.titlesize':'28',\n",
    "          'xtick.labelsize':'23',\n",
    "          'ytick.labelsize':'23'}\n",
    "matplotlib.rcParams.update(params)\n",
    "CCdf['actproxy'].hist(figsize=(20, 10), range=[0, 20])\n",
    "plt.xlabel('Activity Over Tenure', fontsize=24)\n",
    "plt.ylabel('Count of Customers', fontsize=24)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most users have activity scores between zero and two, but some have scores up to 29; this should lend well to segmentation of customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "from sklearn.cluster import KMeans\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "\n",
    "sse={}\n",
    "cc_recency = CCdf[['actproxy']]\n",
    "for k in range(1, 10):\n",
    "    kmeans = KMeans(n_clusters=k, max_iter=1000).fit(cc_recency)\n",
    "    cc_recency[\"clusters\"] = kmeans.labels_\n",
    "    sse[k] = kmeans.inertia_ \n",
    "plt.figure()\n",
    "plt.plot(list(sse.keys()), list(sse.values()))\n",
    "plt.xlabel(\"Number of clusters\", fontsize=24)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use this function to order the new clusters\n",
    "def order_cluster(cluster_field_name, target_field_name,df,ascending):\n",
    "    new_cluster_field_name = 'new_' + cluster_field_name\n",
    "    df_new = df.groupby(cluster_field_name)[target_field_name].mean().reset_index()\n",
    "    df_new = df_new.sort_values(by=target_field_name,ascending=ascending).reset_index(drop=True)\n",
    "    df_new['index'] = df_new.index\n",
    "    df_final = pd.merge(df,df_new[[cluster_field_name,'index']], on=cluster_field_name)\n",
    "    df_final = df_final.drop([cluster_field_name],axis=1)\n",
    "    df_final = df_final.rename(columns={\"index\":cluster_field_name})\n",
    "    return df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecencyClusters</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>0</th>\n",
       "      <td>6775.0</td>\n",
       "      <td>0.448503</td>\n",
       "      <td>0.450948</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1717.0</td>\n",
       "      <td>2.624668</td>\n",
       "      <td>0.852428</td>\n",
       "      <td>1.545455</td>\n",
       "      <td>1.916667</td>\n",
       "      <td>2.416667</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>4.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>409.0</td>\n",
       "      <td>7.038825</td>\n",
       "      <td>1.840916</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>8.166667</td>\n",
       "      <td>12.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>17.570707</td>\n",
       "      <td>4.641742</td>\n",
       "      <td>12.333333</td>\n",
       "      <td>13.954545</td>\n",
       "      <td>16.541667</td>\n",
       "      <td>19.145833</td>\n",
       "      <td>29.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count       mean       std        min        25%        50%  \\\n",
       "RecencyClusters                                                                 \n",
       "0                6775.0   0.448503  0.450948   0.000000   0.000000   0.333333   \n",
       "1                1717.0   2.624668  0.852428   1.545455   1.916667   2.416667   \n",
       "2                 409.0   7.038825  1.840916   4.833333   5.500000   6.666667   \n",
       "3                  48.0  17.570707  4.641742  12.333333  13.954545  16.541667   \n",
       "\n",
       "                       75%        max  \n",
       "RecencyClusters                        \n",
       "0                 0.857143   1.500000  \n",
       "1                 3.250000   4.800000  \n",
       "2                 8.166667  12.250000  \n",
       "3                19.145833  29.833333  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(CCdf[['actproxy']])\n",
    "CCdf['RecencyClusters'] = kmeans.predict(CCdf[['actproxy']])\n",
    "CCdf = order_cluster('RecencyClusters', 'actproxy', CCdf, True)\n",
    "CCdf.groupby('RecencyClusters')['actproxy'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Great, cluster 3 represents more active users whereas cluster 0 represents rather inactive users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proxy for Frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'll make a feature for frquency by summing the frequencies of purchases, one-time, installments, and cash advances to derive a score that indicates how frequently customers uses their card."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8949.000000\n",
       "mean        1.057364\n",
       "std         0.914971\n",
       "min         0.000000\n",
       "25%         0.166666\n",
       "50%         0.916667\n",
       "75%         1.999999\n",
       "max         3.000000\n",
       "Name: freqscore, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CCdf['freqscore'] = CCdf.iloc[:, 7:10].sum(axis=1)         \n",
    "CCdf.freqscore.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count of Customers')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CCdf['freqscore'].hist(figsize=(20, 10), range=[0, 5])\n",
    "plt.xlabel('Sum of Frequencies', fontsize=24)\n",
    "plt.ylabel('Count of Customers', fontsize=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "sse={}\n",
    "cc_freq = CCdf[['freqscore']]\n",
    "for k in range(1, 10):\n",
    "    kmeans = KMeans(n_clusters=k, max_iter=1000).fit(cc_freq)\n",
    "    cc_recency[\"clusters\"] = kmeans.labels_\n",
    "    sse[k] = kmeans.inertia_ \n",
    "plt.figure()\n",
    "plt.plot(list(sse.keys()), list(sse.values()))\n",
    "plt.xlabel(\"Number of clusters\", fontsize=24)\n",
    "plt.show()"
   ]
  },
  {
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FreqClusters</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>0</th>\n",
       "      <td>3644.0</td>\n",
       "      <td>0.126397</td>\n",
       "      <td>0.166051</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.555555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1874.0</td>\n",
       "      <td>0.988086</td>\n",
       "      <td>0.251590</td>\n",
       "      <td>0.571428</td>\n",
       "      <td>0.750001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.444445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2582.0</td>\n",
       "      <td>1.903430</td>\n",
       "      <td>0.201172</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.250001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>849.0</td>\n",
       "      <td>2.633013</td>\n",
       "      <td>0.228423</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>2.416667</td>\n",
       "      <td>2.583333</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count      mean       std       min       25%       50%  \\\n",
       "FreqClusters                                                             \n",
       "0             3644.0  0.126397  0.166051  0.000000  0.000000  0.000000   \n",
       "1             1874.0  0.988086  0.251590  0.571428  0.750001  1.000000   \n",
       "2             2582.0  1.903430  0.201172  1.454545  1.750000  2.000000   \n",
       "3              849.0  2.633013  0.228423  2.272727  2.416667  2.583333   \n",
       "\n",
       "                   75%       max  \n",
       "FreqClusters                      \n",
       "0             0.181818  0.555555  \n",
       "1             1.166667  1.444445  \n",
       "2             2.000000  2.250001  \n",
       "3             2.833333  3.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(CCdf[['freqscore']])\n",
    "CCdf['FreqClusters'] = kmeans.predict(CCdf[['freqscore']])\n",
    "CCdf = order_cluster('FreqClusters', 'freqscore', CCdf, True)\n",
    "CCdf.groupby('FreqClusters')['freqscore'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Great, cluster 3 represents greater frequency of card use whereas cluster 0 represents infrequent use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proxy for Monetary Value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Credit card companies make money on balances left on cards, and are likely to make money from users that spend more money. Hence, I will make a feature that represents balance on card and total dollar value. Add these makes sense becasue some customers will pay interest ona balance they carry, but not make new purchases and others will spend a lot of money, but never carry a balance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     8949.000000\n",
       "mean      3933.118794\n",
       "std       4246.164860\n",
       "min       -745.497557\n",
       "25%       1107.535558\n",
       "50%       2666.830678\n",
       "75%       5512.858039\n",
       "max      59992.049990\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CCdf['cardbalance'] = CCdf['CREDIT_LIMIT'] - CCdf['BALANCE'] # First calculate the balance held on card\n",
    "CCdf['revenue'] = CCdf['cardbalance'] + CCdf['PURCHASES']    # Make revenue proxy\n",
    "CCdf.revenue.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count of Customers')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CCdf['revenue'].hist(figsize=(20, 10), range=[0, 30000])\n",
    "plt.xlabel('Total Revenue', fontsize=24)\n",
    "plt.ylabel('Count of Customers', fontsize=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAE2CAYAAAB7gwUjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deZxcdZ3v/9enel+ydbo7DWQje+hmDxIQTMKWdpwRHXFYRiUzSgBHL+jM6NXRO4zOuDA/LjriD4iCGa6iMMpc3CCIkLAIgYRFCSSQlYSk093pdNJLeq3v/eOcoiudqk5VdVWfrur38/Gox+k6y/d8UlWpd53v2cw5h4iIyGChoAsQEZHRSQEhIiIxKSBERCQmBYSIiMSkgBARkZgUECIiEpMCQkREYsrJgDCzGjP7mJl918yeNbNOM3Nm9kqG1neKmX3azO41sz+aWZ+/vu8cZ7mQmb3fzL5qZr80s73+cs7MzshErSIiicoPuoAMuQq4fQTX9w3g8hSWGw/8Ns21iIikRa4GxGHgcWCD/5iH9yWeKXuB/45a30rgIwksFwZeiVou8hARCVxOBoRz7l7g3shzM1uR4fV9Ovq5mV2T4HKHgTMHLZvGykREUpeT+yCGw8wqzewbZvYnM2s3sw4ze8XMvmRmpUHXJyIyUnJyCyJVZvYe4NdAFdADbAfygDrgdOCjZnaxc+5gcFWKiIwMbUH4zKwa+CVeONwOVDnnFjrn5gFzgD/gdQd9L7gqRURGjgJiwN8DU4CfOuc+7+8fAMA5txP4KNAOXG1mU4MpUURk5CggBlzhD++ONdE5txd4Ee81e99IFSUiEhTtgwDMrAyY5T+91cx648w6zx9qC0JEcp4CwjMx6u/3JDC/jmYSkZyngPC0R/19gnOuIbBKRERGCe2DAJxzh4A9/tNTg6xFRGS0UEAM+C9/+HnT6cwiIgqIKN8GGoB64P+Y2UnRE82s0MwuM7MHA6lORGSEmXMu6BrSzsymAS9HjSoCyoF+oDVq/K3OuVujljsT72S5qYAD3gQOAhOA2UAhgHPuqC0MM7sKuCNqVLm/zi6gI2r85c65Zwct+zDw3qhRk/1hq18vwNvOubOG/EeLiKRZru6kzmPgi3ao8UcdjeSce9nM6oAb8S7fvQA4GdiPdw7EE3hXbR2sOM76iv1HREGMeSbEWTb6yKr2GNNFRDIqJ7cgRERk+LQPQkREYsqpLqbKyko3c+bMoMsQEckqGzdubHbOVQ0en1MBMXPmTDZs0A3ZRESSYWa7Yo1XF5OIiMSkgBARkZgUECIiEpMCQkREYlJAiIhITAoIERGJSQEhIiIxKSCAX726l5+sj3kYsIjImKWAAB59rYHbf/cW/WFdl0pEJEIBASyvq6G5vZuX3z4YdCkiIqOGAgJYNr+KwrwQj76mW1GLiEQoIIBxxQVcMLeSNa83oMufi4h4FBC++toadrcc4fV9h4MuRURkVFBA+C45ZQohgzXqZhIRARQQ76ooK+Tckyfz6CYFhIgIJBEQZlZjZh8zs++a2bNm1mlmzsxeSXXlZnaL38ZQjxtSbT9Zy2un8Ob+drY16RbQIiLJ3DDoKuD2DNXRCLwVZ9q+DK3zGJfV1nDLr15nzaYGPr10zkitVkRkVEomIA4DjwMb/Mc84BtpquMR59yKNLWVshMnlnD6tImseU0BISKScBeTc+5e59ylzrkvOed+wQj+sh9J9bU1vLrnEO+0Hgm6FBGRQGkn9SDLa6cA8Jh2VovIGDdaAuJ0M7vfzJ4ws4fN7OtmVhtEIbOqypk/ZRxrFBAiMsaNloA4A7gaWAZ8EPgK8Cczu93M8ka6mOV1Nbywo4UD7d0jvWoRkVEj6IBoAG4FFgNVQDFwGnAXYMDNwDdHuqj62hrCDh5/Y/9Ir1pEZNQINCCcc3c5577onFvvnGt2znU75/7knLsR+KI/2+fMbGa8NsxspZltMLMNTU1Naalr4QnjmF5Rqov3iciYFvQWxFBuA/biHYr7wXgzOedWOecWOecWVVVVpWXFZsby2ik8u/UAh7t609KmiEi2GbUB4ZzrB9b7T+eO9Prr62ro6Q/z5ObGkV61iMioMGoDwtfjD5M5oS8tzpw2iapxRTqaSUTGrNEeEHX+cM9IrzgU8rqZntzcRFdv/0ivXkQkcKM2IMzsA0DkXIjHgqihvvYEjvT289Sb6dn5LSKSTTIeEGb2jJntNLObB42vNbO7zez0QeNDZnY1cL8/6tfOuRczXWcs586qYEJJAWs26XBXERl7Eu7bN7NpwMtRo4r8YZ2ZNUeNv9U5d2vU86nADGDioCYLgJXASjNrAXYBfcAcYJI/z9PAxxOtMd0K8kJcsnAKj7+xn97+MAV5o3aDS0Qk7ZL5xssDJkc9yuOML02wvZ14Z0z/BmjFC4Yz8HZMP4IXDMucc61J1Jh29XU1HDrSy/rtLUGWISIy4hLegnDO7cQ7uzkpzrmZcca3Av+WbHsj7cK5lZQW5vHopn1cMLcy6HJEREaM+kyOo7ggj6Xzq1izaT/hsAu6HBGREaOASMDy2hqa2rp5effBoEsRERkxCogEXLSgmsK8kK7NJCJjigIiAeOKC3jvnMk8uqkB59TNJCJjgwIiQfV1NexuOcLr+w4HXYqIyIhQQCTokoVTCBk6aU5ExgwFRIImlxfxnpMrWKP9ECIyRiggklBfW8OW/W1sb2oPuhQRkYxTQCThstoaQN1MIjI2KCCScOLEEk6fOoFHdY8IERkDFBBJWl5Xw6u7W9nbeiToUkREMkoBkaR6v5vpMW1FiEiOU0AkaVZVOfOmlKubSURyngIiBfW1Nbywo4UD7d1BlyIikjEKiBQsr6sh7OD3bzQGXYqISMYoIFJwygnjmVZRom4mEclpCogUmBn1tTU881YzbV29QZcjIpIRCogULa+toac/zJNbmoIuRUQkIxQQKTpr+iSqxhXp2kwikrMUECkKhYzLTpnCk1sa6ertD7ocEZG0U0AMQ31dDZ09/Tz9VnPQpYiIpJ0CYhgWz5rM+OJ83YpURHKSAmIYCvJCXHLKFB5/Yz+9/eGgyxERSSsFxDDV19Zw6Egv67e3BF2KiEhaKSCG6X3zqigpyGONTpoTkRyjgBim4oI8li2oYs2mBsJhF3Q5IiJpo4BIg+W1NTS2dfPy7tagSxERSRsFRBosW1BNQZ6pm0lEcooCIg3GFxfw3jmVPPpaA86pm0lEcoMCIk3qa2t4u6WTN/a1BV2KiEhaKCDS5JJTphAydAlwEckZCog0qSwv4pyZFbp4n4jkDAVEGtXX1bBlfxs7mjuCLkVEZNgUEGm0vLYGQEcziUhOUECk0YkTSzh96gRdvE9EcoICIs0uq63hld2t7Dt0JOhSRESGRQGRZvV1XjfTY5v2B1yJiMjwKCDSbHZVOXOry9XNJCJZTwGRAfV1NazfcYCWjp6gSxERSZkCIgOW19YQdvD46+pmEpHspYDIgNoTxzN1UonOqhaRrKaAyAAzo762hmfeaqatqzfockREUqKAyJD6uhp6+sOs3dIUdCkiIilRQGTIWdMnUTWuSN1MIpK1FBAZEgoZl54yhSc3N9LV2x90OSIiSVNAZFB9bQ2dPf0881Zz0KWIiCQt4YAwsxoz+5iZfdfMnjWzTjNzZvbKcIswswvN7GEzazSzLjPbama3mVnFcNsO0uJZkxlfnK9uJhHJSvlJzHsVcHu6CzCzG4E78MJqL7AJWAh8HvgrM7vAObcr3esdCYX5IS5ZOIXH39hPb3+YgjxtsIlI9kjmG+sw8DjwLeAK4MvDXbmZnQl8z6/js8BU59zZwFTg9/7wgeGuJ0jL62po7ezlhR0tQZciIpKUhAPCOXevc+5S59yXnHO/APalYf1fBfKA+51zdzjnnL+uFrwtljbgXDP7QBrWFYj3za2ipCBP12YSkawTWJ+HmZUD7/ef3jl4unOuGfi5//TKkaor3UoK81g6v4o1mxoIh13Q5YiIJCzITvEzgWKgB1gfZ551/vC8EakoQ+rramhs6+aVPa1BlyIikrAgA2KeP9zlnIt3PYpt/nCWmRWMQE0ZsWxBNQV5xhp1M4lIFgkyICKHsA619zYyLQSMjzWDma00sw1mtqGpaXRe1mJ8cQHnz67k0U0N+LtZRERGvSADotgfDnXThK6ov0tizeCcW+WcW+ScW1RVVZW24tKtvq6GXQc62dzQFnQpIiIJCTIgIl/+hUPMUxz1d1bf5PnSU6Zgho5mEpGsEWRAHPSHk4eYJ9INFcY7DyNrVZYXcc7MCtborGoRyRJBBsQWfzh9iB3Qs/3h9iF2ZGeN+toaNje0saO5I+hSRESOK8iAeBnoxutiWhxnniX+8LkRqSjDltfVAGgrQkSyQmAB4ZxrBx71n94weLqZVeJd0gPgwZGqK5NOmljCaVMnaD+EiGSFjAeEmT1jZjvN7OYYk7+Gt3/hGjP7jJmZv0wF8DNgHPAi8JtM1zlSltfW8MruVhoOdR1/ZhGRACVzue9pZtYceeBdZA+gLnq8mX1h0KJTgRnAxMFtOudeAm4CnN/eHjPbCOwBLsa7uuuVLodOHqj3u5kee11bESIyuiWzBZGHd8RR5FEeZ3xpMgU45+4AlgK/AoqAOrwLAX4HOM05tyOZ9ka72VXlzKkuVzeTiIx6Cd8Pwjm3E7BkV+Ccm5nAPE8BTyXbdraqr63hznXbaOnooaJsqNNARESCozvYBKC+rob+sOPxN/YHXYqISFwKiADUnjiekyaW6OJ9IjKqKSACYGbU19Xw9FvNtHf3BV2OiEhMCoiA1NfV0NMf5snNjUGXIiISkwIiIGdNn0RleRGP6qxqERmlFBAByQsZl9VOYe3mRrp6+4MuR0TkGAqIANXX1tDR08+zW5uDLkVE5BgKiAAtnjWZccX5OmlOREYlBUSACvNDXLJwCr97Yz99/eGgyxEROYoCImDLa2to7ezlhR1D3ZpbRGTkKSACtmReFcUFIR3NJCKjjgIiYCWFeSydV82aTQ2Ewzlz0VoRyQEKiFGgvq6G/Ye7eWVPa9CliIi8SwExCixbUE1BnunaTCIyqiggRoEJJQWcP7uSNZsayKF7I4lIllNAjBLLa2vYeaCTLfvbgi5FRARQQIwal54yBTN00pyIjBoKiFGialwR58yoUECIyKihgBhFltfVsLmhjZ3NHUGXIiKigBhNltdOAWCNTpoTkVFAATGKTJ1UyqknTdBZ1SIyKiggRpn6uhpefruVhkNdQZciImOcAmKUWV5bA8Bjr2srQkSCpYAYZeZUlzOnulz7IUQkcAqIUWh57RSe397CwY6eoEsRkTFMATEK1deeQH/Y8fgb+4MuRUTGMAXEKFR30nhOmliibiYRCZQCYhQyM5bX1vDUW820d/cFXY6IjFEKiFGqvq6Gnr4wa7c0Bl2KiIxRCohR6uwZk6gsL+TXr+4LuhQRGaMUEKNUXsi44uxpPLqpge/9/q2gyxGRMSg/6AIkvn9cPp/Gw13c9rs36Qs7br5kLmYWdFkiMkYoIEaxvJDx7x89nbyQ8d3fv0VfOMw/XDZfISEiI0IBMcrlhYxvf+Q08vOM7z+5jb5+x/98/wKFhIhknAIiC4RCxr996FTyQsbdT22nt9/x1T9fqJAQkYxSQGSJUMj4+uV15IdC3PvsDvrDYW75YK1CQkQyRgGRRcyMf/6LUyjIM37w9A76wo6vX15HKKSQEJH0U0BkGTPjy3+2kPy8EHeu3UZ/2PGND5+qkBCRtFNAZCEz4wvL55MfMr73xFZ6+x23XnEaeQoJEUkjBUSWMjP+/rL55IdC3P74m/SHw/x/Hz2d/Dyd+ygi6aGAyHI3XTKX/Dzj39dsoS/s+M6VZygkRCQtFBA54O+WzSE/ZHzzkc30hx3/cfWZFCgkRGSY9C2SI65fMpuvfGAhj7zWwN/95CV6+sJBlyQiWU4BkUM+deEs/uWDtTz2+n5u/PFGuvv6gy5JRLKYAiLHXHv+TP71Q3X8fnMjK+/bSFevQkJEUpN0QJjZhWb2sJk1mlmXmW01s9vMrCKFtm4xM3ecxw3JtjvWfWzxDL71l6fy1FtNXHffBo70KCREJHlJ7aQ2sxuBO/CCZS+wCVgIfB74KzO7wDm3K4U6GoF4Nz3QHXNScNV7ppOfF+Iff/4qf7v6Re5ZsYjSQh2TICKJS/gbw8zOBL6HFw6fBb7vnHP+lsODwMXAA8DiFOp4xDm3IoXlZAhXnD2V/JDx+QdfYcWPXuTeFedQXqSQEJHEJNPF9FUgD7jfOXeHc84BOOdagKuANuBcM/tA+suUVH3ozJP47lVnsnHXQa699wXaunqDLklEskRCAWFm5cD7/ad3Dp7unGsGfu4/vTI9pUm6/MXpJ3LH1Wfy6u5WPn7PCxxWSIhIAhLdgjgTKAZ6gPVx5lnnD89LoY7Tzex+M3vC3wH+dTOrTaEdieP9p57A9//6LDbtPcTHfrieQ50KCREZWqIBMc8f7nLOxftm2eYPZ5lZQZJ1nAFcDSwDPgh8BfiTmd1uZnlJtiVxLK+t4a6Pnc3mfW1c88PnOdjRE3RJIjKKJRoQkUNYW4aYJzItBIxPsN0G4Fa8HdtVeFsppwF3AQbcDHxzqAbMbKWZbTCzDU1NTQmuduy6eOEUVn3ibN5qbOfqHzzPgfbuoEsSkVEq0YAo9odD/eTsivq7JJFGnXN3Oee+6Jxb75xrds51O+f+5Jy7EfiiP9vnzGzmEG2scs4tcs4tqqqqSmS1Y97S+dXcc+0idjR3cPUPnqepTSEhIsdKNCAiX/6FQ8xTHPX3kdTKOcpteOda5ON1O0kaXTi3ih/9zTnsbjnCVaueo/Fw1/EXEpExJdGAOOgPJw8xT6QbKgwcTrkin3Oun4Ed4nOH254c6/zZlaz+m3PYd6iLq1Y9T8MhhYSIDEg0ILb4w+lD7ICe7Q+3D7EjO1mRLi2d3ZUh586azH1/+x4a27q5ctVz7G1Nx8afiOSCRAPiZaAbr4sp3pnSS/zhc8MtKkqdP9yTxjZlkEUzK7jvk++hpb2HK1c9x+6WzqBLEpFRIKGAcM61A4/6T4+5eJ6ZVQJX+E8fTEdh/hnZkXMhHktHmxLfWdMn8ZPrzuVQZy9XrXqetw8oJETGumQutfE1vP0L15jZZ8zMAPxrMf0MGAe8CPwmeiEze8bMdprZzYPG15rZ3WZ2+qDxITO7GrjfH/Vr59yLSf2rJCWnTZ3I/dctpqOnjytXPcfO5o6gSxKRACUcEM65l4CbAId30b49ZrYRr/vnYrwjjq6MXKMpylRgBjBx0PgCYCXwipkdMLOXzOwFoBkvHMYDTwMfT/pfJSmrO2kC939qMd19Ya5c9RzbmtqDLklEApLU/SCcc3cAS4FfAUV4+wj2Ad8BTnPO7UiiuZ14Z0z/BmgF5uCdUd0DPIIXDMucc63J1CjDd8qJ4/npdYvpDzuuWvU8b+1vC7okEQmAHfuDP3stWrTIbdiwIegycsbWxjau/sF6wmHH/dctZn7NuKBLEpEMMLONzrlFg8frlqMS15zqcfxs5WLy84yrVj3H63uHfXqLiGQRBYQMaXZVOQ+sPI+Sgjyu+eHzvPbOoaBLEpERooCQ45pZWcYD159HWWE+1/zgeV7drd1CImOBAkISMq2ilAeuX8yE0gI+9sP1bNx18PgLiUhWU0BIwqZOKuWBlecxubyQj971B/7uJy/xpz3qchLJVQoIScqJE0t46NPv5YYls3nqzSb+4o5n+Pg963l2azO5dESciOgwVxmGw1293L/+be55ZgdNbd2cNnUCNy6ZzWW1NeSFLOjyRCRB8Q5zVUDIsHX19vPQS++w6qlt7DzQycmVZVz/vll8+KyTKMrXHWNFRjsFhGRcf9jx6GsN3LluK6+9c5jqcUV88oKTuebc6YwrTvY25SIyUhQQMmKcczy79QB3rtvKs1sPMK44n0+cN4MV559M1biioMsTkUEUEBKIP+5p5a5123jktQYK80J8dNFUVl44m+mTS4MuTUR8CggJ1PamdlY9tZ2HXnqHvnCYPz/tRG5YMptTThwfdGkiY54CQkaF/Ye7uPeZHfz4+V109PSzZF4VNy6dzbknV+DfYkRERpgCQkaVQ529/Hj9Ln707A6a23s4c/pEblgym0sXTiGkQ2RFRpQCQkalrt5+/mvjHlY9tY3dLUeYXVXGDUtmc/kZJ1GYr/M4RUaCAkJGtb7+ML99rYE7127jjX2HOWFCMZ+84GSufs90yorygy5PJKcpICQrOOdY92YTd63bxvPbW5hQUsC1583g2vNnMrlch8iKZIICQrLOS28f5K6123js9f0UF4S46pzpfOrCk5k6SYfIiqSTAkKy1tbGNu5et53/+8o7hB188PQTuX7JLBbU6BBZkXRQQEjW23foCPc8vYP7X3ibzp5+Ll5QzQ1LZ3POzIqgSxPJagoIyRmtnT3c99wuVv9hJy0dPSyaMYkbl85m2fxqHSIrkgIFhOScIz39PLhhN6ue2s47rUeYN6Wcv33vyVy8cIqu+SSSBAWE5Kze/jC//uNe7lq7nS372wA4feoEls6v5qIF1Zx60gRtWYgMQQEhOc85x6a9h3lycyNPbGnkld2tOAeV5YUsmVfNsgVVXDi3igkluvS4SDQFhIw5LR09rHuzkSc3N7HuzSYOHeklL2QsmjGJZQu8rYu51eW6BpSMeQoIGdP6+sO8sruVJzY38uSWJt7YdxiAkyaWsGxBFcvmV3P+7EpKCnUHPBl7FBAiUfYdOsLaLU08sbmRZ7c209nTT2F+iPNmTeYif+tiWoVOyJOxQQEhEkd3Xz8v7Gjhic2NrN3SxI7mDgBmV5Vx0YJqls2vZtHMCl08UHKWAkIkQTuaO3hycyNPbmlk/fYWevrDlBflc8GcSi5aUM3S+VVUjy8OukyRtFFAiKSgo7uPZ7c28+SWJp7c3EjD4S4A6k4az0Xzq1m6oJrTp04kT4fRShZTQIgMk3OOzQ1tfldUIxt3HSTsoKKskCXzqli2oJr3za1kYmlh0KWKJEUBIZJmrZ09rHuzibVbmli7pZGDnb2EDM72D6NdNr+aBTXjdBitjHoKCJEM6g87Xt3T+u6+i9fe8Q6jPWFCMUvnV3PBnErmTilnxuRSivJ1KK2MLgoIkRG0/3AXa7d4J+k9s7WZ9u4+AEIG0ypKmV1VzqzKMmZXDwwnlxVqa0MCoYAQCUhPX5jNDYfZ3tTB9qZ2tjV1sK2pnR3NHXT3hd+db3xxvh8Y5cyuLmNWZTlzqsuYXlGmQ2wlo+IFhG72K5JhhfkhTps6kdOmTjxqfDjseKf1CNua2tnuh8b2pg6efquJX7y059358kLGtEkl3lZHVZk/LGd2VRkV2uqQDFJAiAQkFDKmVZQyraKUpfOPntbW1cuO5o5jw2NrMz1RWx0TSgqYXVXmB8ZAgMyYXEpBnrY6ZHgUECKj0LjigphbHf1hx97WI2w9KjjaWfdmEz/fePRWx4yK0qgtjoEtj4oyHYYriVFAiGSRvKitjmWDtjoOd/VG7ecYCJCn3mymp39gq2NSaQGz/J3kMyvLmFRayKTSAiaVFb7798TSQu33EAWESK4YX1zAGdMmcsa0Y7c63jno7evY5u8k397UzpNbmmiO2uoYrLwon4mlBUwqLXx3WFFWeNS4Cj9UIuNKC/O0TySHKCBEclxeyJg+uZTpk0tZtqD6qGldvf0c7OzhYEevN+zs4WBnL60dPbR09tDa2fvuuLdbOmnp6KGtqy/uugrzQ97WSFSATIxsoZR6f1eUFfjjvPHjiwt0x79RSgEhMoYVF+RxwoQSTphQkvAyff1hWo/00trZQ4sfLK1+iHhh44dMZw9v7m/nYEcPrUd66Q/HPqQ+ZDAxaiskEi7lRfmMK86nvCifcn84vrjg3b8j08uK8rVDPkMUECKSlPy8EJXlRVSWFyW8TDjsaOvuGwiSjqitlc4eWjoGtlbeaT3C63sP0dbdR3t3H4mcqlVcEKK8qGAgUPxQGRcVLkc/jx1ARfkhdZFFUUCISMaFQsaEkgImlBQwY3Liyznn6Ozpp727j7YuLzDau/po7+4d9LzPC5SocXsOHqG9u5f2Lm/ZvjhbMNEK8iwqMAqOCZjyonxKC/P8R/5Rw7KiPEoK8r1hYR5lhfmUFORldfeZAkJERi0zo6zI60aaMj71dpxzdPeFjwqVNj882v0tlWMCxw+ixrYutjcNjIs++z0RJQV+oBTlUVqQ7w1jBUxhHiWFfsAU5FFWlP9u0BwVSkV5lBbkkT8C3WpJB4SZXQj8A3AeMB7YAzwM/JtzriWVIjLRpohIhJlRXJBHcUEeVeMS7xqLpT/s6Ozp40hPPx09/XT29NHZ0+89uiN/e8OOnn6O9PT5w346uvs40usND7T3DCznz5+MwvyQHyxekPzwE4uYWVk2rH/bYEkFhJndCNwBhIC9wCZgIfB54K/M7ALn3K6g2xQRyZS8kDGuuIBxxQVpbTccdnT1RYKmn87ePjq6/WB5N5D8oT+9s3sgYEoL03+V4IQDwszOBL6H90X+WeD7zjlnZhXAg8DFwAPA4iDbFBHJRqGQ+d1N+VAedDWeZDqxvgrkAfc75+5w/mVg/S6gq4A24Fwz+0DAbYqISBokFBBmVg6833965+Dpzrlm4Of+0yuDalNERNIn0S2IM4FioAdYH2eedf7wvADbFBGRNEk0IOb5w13Oud4482zzh7PMLJG9N5loU0RE0iTRgKjwh0MdchqZFsI7VHVE2jSzlWa2wcw2NDU1JbBaERFJRKIBUewPe4aYpyvq70Qu7JKWNp1zq5xzi5xzi6qqqhJYrYiIJCLRgIh8UQ91p5HiqL+PBNSmiIikSaIBcdAfDnUVlUiXURg4HFCbIiKSJomeKLfFH043s4I4O5Vn+8PtQ+x0zmibGzdubDYznXUdXyXQHHQRY5Re++DotT++GbFGJhoQLwPdQBHeWc1Px5hniT98Lqg2nXPaCTEEM9vgnFsUdB1jkV774Oi1T11CXUzOuXbgUf/pDYOnm1klcIX/9MGg2hQRkfRJ5lIbX8PbF3CNmX3G/Ltq+NdN+hkwDngR+E30Qmb2jJntNBItXhMAAA9XSURBVLOb09WmiIhkXsIB4Zx7CbgJcHgX2NtjZhvxLs19Md6VWK+MXE8pylS8/q2Jg8YPp01JzaqgCxjD9NoHR699iizZ714zex/evRvOx/uFvwf4JfCvzrkDMebfiRcQ/+KcuyUdbYqISOYlHRAiIjI2ZP6edSIikpUUECIiEpMCIkuY53wz+5Z/ZNgBM+s1syYze8zM/jpyFFiS7a42M3ecR30m/k3ZxsxuSeC1OuaQ7QTbvtDMHjazRjPrMrOtZnabf0TfmGZmMxN43SOPHyXRbsbez1yR1D2pJVAXAY9HPd8O7ABOBi71H1eb2Uecc90ptL8beDvOtINxxo9VjcBbcabtS7Yx3Zf9uLqAZ4eYXgyc7f/9hxTaT+v7mUsUENnD8ALhO8DPnHON704w+zjwA+ADeOeWfDGF9u+Nd5SZHOMR59yKdDSk+7Ifn3OuAbgg3nQzuxZYjXdBzwdSWEXa3s9coy6m7PECMN859x/R4QDgnPs/eMEA8Ckz0/uaPXRf9uFb4Q8fcs7pop5ppC+SLOGcO3ycCxY+4g8rAF2TKgvovuzDZ2YzGbhm2+rACslR6mLKHdE3VErl3hnLzKwW7/LrrcBG4MdjvO87ntPN7H6gBu8X/h/xuv02JdlOovdl/xt0X/Z4rsXrfn0beCLFNtL1fuYcBUTuuNofvpriZvb7Bj3/MPDPZvYV59ytwyst55zhPyI+CPyTmX0X+AfnXH+C7SR9X/YEL6U/JvhH7X3Cf3qfcy6cYlPpej9zjrqYcoCZnc3AFXG/leTi24H/hfdrdhJQirdD9L+AAuDbZvbpNJWa7RqAW/Fenyq8X/+nAXfh/Yq9GfhmEu1l4l7vY8kSYJb/9+oUlk/3+5lzdKmNLGdmU/B2YE8H/ts595dpbPv7wKfxupym+ZdolxjM7AvAt4E+YK5zbmcCy3wV7+CCp51zg7fgIvPMYmArYppzbk96Ks5+ZrYar4sp7us3jLaTfj9zkbYgspiZTcDbOT0db5/BijSv4st4/eMT8c7DkPhuwzuHIR+viyIRui97isysDPiI/3R1BlaRyvuZcxQQWco/AuZRvK6hTcDydB/i55w75LcNMDedbecav586sqM50ddK92VP3UeBcqATrzs0rVJ8P3OOAiILmVkp3k2UFgNvApdk8LLoPf5QBzQcX7Kv1VH3ZY8zT7L3eh8rVvjDnzvn2jK0jjH/2VdAZBkzKwYexjvqaCdwsX+maSbWlQ8s8J+q7/v46vxhoq9V5L7shcQ/UzrZe73nPDM7mYGj7lZncFXJvp85RwGRRfxfmb8ALsH70F6U4Z2W1wMT8HbUpXqM+Zjgn+lc6z99LJFldF/2lEXOfdgJrM3EClJ5P3ORAiJLmFke8BPgz/AOz7vIObcjwWV3+o8rBo2/1My+bWZzB40vNLPPAv/bH3WXc25MX7TMzGrN7G4zO33Q+JCZXQ3c74/6tXPuxUHz6L7saTLo3If/PN7tiOO99sN5P8eSMdu3loX+Cm/HHHhHv/xoiKt7f9Y593LU8xn+sHzQfGXAF4AvmNl+Bjal50fN+wvg74dRd64oAFYCK82sBdiFt2U1B+/8EYCngY/HWHbI+7Kb2U3Af+BdtO9LZtaAdzXXEnRf9sGW4F3B2AH/mcD88V774byfY4YCInsURf0903/EMyHBNjcCX8e7jMNcvP0NBUAT3mb1aufcr5ItNEftBL6C91otxPsiKcY7ke0RvF+cP03lrFvn3B1m9kcG7steh+7LHs8Kf/hUolvQcewkQ+9nLtGJciIiEpP2QYiISEwKCBERiUkBISIiMSkgREQkJgWEiIjEpIAQEZGYFBAiIhKTAkJERGJSQMioYGa3mJnz7xI25pjZOWb2KzNrNrOw/1rcksH17fTXsTRT65Dsp4DIEma22v8P7cxsgw1xISYz+/FY/rLNNv7FEtcCf453HaBmYD+Q87d4NbMP+T8OlgZdixxL12LKTmcDHwYeCroQSYuVQCnexeE+6JxrDbiekfQhvMt3Q4Yu3S2p0xZE9vqamen9yw2R+w48OMbCQUY5fcFkn3V49+GtBa4JuBZJjxJ/mPNdSpJdFBDZpwG4w//7Fv+2oAmL2o8xM870mZF5Ykxb609bYWbjzexWM9tmZkfMbLuZfc2/JWpk/ovNbI2/47XDzJ4yswsTqDFkZp8zs1f95Q6Y2S/N7D0JLPdxM/udmTWZWY+Z7TWzB8zs3DjLvLtz3F/+M2b2gpm1+uPPOF69g9b/STNbZ2YtZtZlZjvMbJWZzYkx/07/dV7qj/pR1PuzM9H1+m2ZmV1pZr8xswYz6zazd/zX/HNmNjnBdo57sEDU/rBbYkw72czuNLM3/c9Fp5nt8j87XzLvLnmY2VL/3x7pXvrnqH97vM9fRt9fM7vczH5rZvvNrNd/D7eY2U/N7MpEXr+c45zTIwseePfedXh3GasADvnPr4sx74/9aatjTHP+Y2ac9cyMzBNj2lp/2ueAN/y/2/Fu7h5p95f+vJ/Gu1Naf1StDu8ezO+N0fYtDNwE5hf+371Aa9SyfXg3z4lV9zjgd1Hzhgettx/4zHHW+3+j1nPQ//uMBN+fUmBN1Pp6BtV+BLh80DIv4gV+5PU75D9vAF5M4rMxIca//aD/b46MWzFomZ3++KVxXo9jPjsxPou3DBp/FnB40GtwMOq5A+r9ec/3/51Hoj5HDdGPkXx/gX8bVOfhqNrc4HrGyiPwAvRI8I2KCgj/eeSD/zZQNGjeTAdEK7AZuMAfXwh8Cu8L3QFf9b8cvgFM9OeZAfzBn/5CjLZviWq7Dy+ESvxps/FuYOTwutdmx1j+v/3pr+LdljWy7ETgS3jB1M+gcIpabxvenfpuBEr9adXA+ATfn7v8drrw7uVd5I+fBzzpT+sA5g3xuq5I8bPx66jX5n9EveaFeDcf+heODaedpD8gnvDHPw+cGTW+FFgE3A6cl0hbI/n+4n3mI2H6DaAyavlq4CPAPUF/BwTxCLwAPRJ8o44NiPHAAX/cTYPmzXRA9AJzYky/J6r9e2NMn4H3y88B0wdNuyVq2X+KsWwxXig54IeDpl3ij98BVMT5d33Bn+fXQ6x3ZYrvzYyoL5jrY0wvBbb60+8b4nVdkcK6/4yBX9T1SSy3k/QHRKc//twUPte3DDFPRt9fvNv5OuCNVN7/XH5oH0SWcs4dBm71n37ZzMpGcPX/5ZzbGmP841F/f3PwROfcLrwvSvB+2cbSCXwnxrJdwG3+04+YHXUeyLX+cLVzriVOu5Gb0C8zs7wY0w8A98ZZ9nj+Em9/XgPww8ETnXOdDLxXfxln/an6hD9c45x7NI3tpuKwPzwhze1m+v2N1D3BzEpTrDEnKSCy2/fwTqiqxutaGCl/ijO+0R92MRAEg+33h5PiTN/gnOuIM22dP5yId+P6iPP94ef8HbTHPIAN/jylQKwdthucc31x1ns8Z/nDp138exg/4Q/LgPkprieWxf7wt2lsM1WRGu4zs2+Z2WIzK0hDu5l+f9fj3Yv6BOA5M1tpZifHmXdMUUBkMf+X6Tf8p/9oZhNGaNX74oyPfDnud/62+xDzxPvieGeI9UZPq4r6O/KLdQIwZYhHRKxfiU1DrPd4IrUMVfueGPOnQ+Tf9XYa20zVP+LtZxoHfBF4DjhsZk+Y2Y1mVjLk0vFl9P11zh0EPo63/+s04G5gu5ntM7P/NLMlKdad9RQQ2e9uYDfeL/K/D7iWTIt3eZHI5/hy55wl8NgZo414v/yTUTTEtHiBmTOccweAC4BLgf8AXsbbUb4M+P+B18xsagpNZ/z9dc79Fm//20rgQWAvUIPXhbfWzFalUHfWU0BkOedcN/B1/+nNkePMhxD5j1IcZ/pIbYXEc+IQ06L7tqN/EUa6rU5JfzkJidQyY4h5psWYPx0i//ah1p2MSDdMvM8HDPEZcZ7HnXM3OefOAirxjupqAWbhHcmUrBF5f51zh5xzP3DOXemcOwnvZNQf+JOvM7MPZHL9o5ECIjf8CNiGt2n/P48zb+RSDvF+yZ2TrqJSdM4QOwojm/qteEe0RDznDz+SsaqG9pI/PHeI2i/yhx3AljSu+3l/+Gdpam/Iz4d/cMDZiTbmnDvonFsFfNkfNbi7JhxpeohmAnl/nXOvO+dWMvAaj7muJgVEDvB3vt3iP/00Q/8Kj+xgvnzwBDMrAm5Oa3HJKwVuGjzSr+3z/tOfD9rHsdofLjKzTzAEM4u3c3w4HsL7opuM10UxeJ2leP3zAA8NsSM7Fff5w8vMrD4N7UU+H+eYWayjkf6ao7eGgHfPch7qrP4j/nBwN1zkCKKJQyy72h9m5P01s8LjzBKv9pyngMgd9wOv413XZ9kQ8z3oD68zs7/xv3gxs1q8o1CGCpeRcAj4upndFNmpaWazgIeBhXhHSH0regH/8M7IlW3vNbN/if5yM7NJ/mUUHgb+d7oL9g/fjfRRf8s/Cibyus4DfgPMwTuE91/TvPpH/IcBvzCzz5rZRH/dhWZ2qpndZmYfSrC9Z/H63wuBn0aO5jGzUjO7Hq/L5WCM5cYDW83sn/x15vnLhczsYrwzlcE72zzaJn9YHyeQRuL9vdG8S8JcM6jdiWb2ZQYuhTK49twX9IkYeiT2YNCJcnHm+QhHXy5gdYx5CvA2mSPz9DJwyYIDeFsWxztRbkWc9S/1p+8cosaYbXD0JREeIvalGvqAq+K0W8bA2baRRytHX47BAT+Ks95jXqsk359SBs72jlV7F4POZk70dU1g3ROj2nB4+5laSOFSG/60Dw9a9hADZ8nfQ4yT2/waol/nHv/z1Bc1bhswddC6Khk44bMf7wi5nYM/Q5l8f/G2mqPbaOfYS4TcHfR3QBAPbUHklocY6A+PyTnXi3eUyb/j/UcM4/WLr8brW341oxUenwM+ited9AbeL9mDeJeTON8597OYCznX4Zz7MN5Ndx7CO+S0xF9+K94W1hV4XXDpL9o75Pj9eJcceRpva6EU2IV38typzrmHM7TuVrx9HNfinazYApTjfdmuw/sC/GUS7f03cBneJULagDzgFeBTzrlPxlnsMN5r/x3gBbwd8ePwPlsvAv+Ed92j6MN9cc41423xPuQvU4W3w33GoPky+f7eD1wHPID3metl4PX7JV6wX59Cu1nP/AQVERE5irYgREQkJgWEiIjEpIAQEZGYFBAiIhKTAkJERGJSQIiISEwKCBERiUkBISIiMSkgREQkJgWEiIjE9P8AYQjfy1utySkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sse={}\n",
    "cc_revenue= CCdf[['revenue']]\n",
    "for k in range(1, 10):\n",
    "    kmeans = KMeans(n_clusters=k, max_iter=1000).fit(cc_revenue)\n",
    "    cc_recency[\"clusters\"] = kmeans.labels_\n",
    "    sse[k] = kmeans.inertia_ \n",
    "plt.figure()\n",
    "plt.plot(list(sse.keys()), list(sse.values()))\n",
    "plt.xlabel(\"Number of clusters\", fontsize=24)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RevenueClusters</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>0</th>\n",
       "      <td>5579.0</td>\n",
       "      <td>1552.341268</td>\n",
       "      <td>1075.471788</td>\n",
       "      <td>-745.497557</td>\n",
       "      <td>602.819753</td>\n",
       "      <td>1393.573650</td>\n",
       "      <td>2427.242176</td>\n",
       "      <td>3831.709001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2633.0</td>\n",
       "      <td>6110.096696</td>\n",
       "      <td>1594.137809</td>\n",
       "      <td>3835.858142</td>\n",
       "      <td>4693.380301</td>\n",
       "      <td>5927.162403</td>\n",
       "      <td>7340.911546</td>\n",
       "      <td>9617.828160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>702.0</td>\n",
       "      <td>13127.964536</td>\n",
       "      <td>3198.308654</td>\n",
       "      <td>9625.217999</td>\n",
       "      <td>10707.333553</td>\n",
       "      <td>12072.127814</td>\n",
       "      <td>14794.987003</td>\n",
       "      <td>23953.150132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>35235.784194</td>\n",
       "      <td>9813.273060</td>\n",
       "      <td>24796.809970</td>\n",
       "      <td>27347.320592</td>\n",
       "      <td>33160.393777</td>\n",
       "      <td>39583.753501</td>\n",
       "      <td>59992.049990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count          mean          std           min  \\\n",
       "RevenueClusters                                                    \n",
       "0                5579.0   1552.341268  1075.471788   -745.497557   \n",
       "1                2633.0   6110.096696  1594.137809   3835.858142   \n",
       "2                 702.0  13127.964536  3198.308654   9625.217999   \n",
       "3                  35.0  35235.784194  9813.273060  24796.809970   \n",
       "\n",
       "                          25%           50%           75%           max  \n",
       "RevenueClusters                                                          \n",
       "0                  602.819753   1393.573650   2427.242176   3831.709001  \n",
       "1                 4693.380301   5927.162403   7340.911546   9617.828160  \n",
       "2                10707.333553  12072.127814  14794.987003  23953.150132  \n",
       "3                27347.320592  33160.393777  39583.753501  59992.049990  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(CCdf[['revenue']])\n",
    "CCdf['RevenueClusters'] = kmeans.predict(CCdf[['revenue']])\n",
    "CCdf = order_cluster('RevenueClusters', 'revenue', CCdf, True)\n",
    "CCdf.groupby('RevenueClusters')['revenue'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, cluster 3 represents greater greater total revenue, as a representation of card balance held and total dollar value of purchases; cluster 0 represents lower revenue customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scoring Clusters for Segmentation\n",
    "Now that we have clusters for activity, frequency, and revenue, we can add up the cluster indexes per customer to get overall scores that will be useful when grouping customers into Low-, Mid-, and High-Value groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>actproxy</th>\n",
       "      <th>revenue</th>\n",
       "      <th>freqscore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.078380</td>\n",
       "      <td>1248.641478</td>\n",
       "      <td>0.108675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.467240</td>\n",
       "      <td>3390.173558</td>\n",
       "      <td>0.652222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.944254</td>\n",
       "      <td>3696.373772</td>\n",
       "      <td>1.448557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.753610</td>\n",
       "      <td>4653.347843</td>\n",
       "      <td>1.784170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.605382</td>\n",
       "      <td>6566.198651</td>\n",
       "      <td>2.010691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.435715</td>\n",
       "      <td>8327.254824</td>\n",
       "      <td>2.348034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.565334</td>\n",
       "      <td>10174.826573</td>\n",
       "      <td>2.575756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.974678</td>\n",
       "      <td>14181.458006</td>\n",
       "      <td>2.757524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.684275</td>\n",
       "      <td>24853.297619</td>\n",
       "      <td>2.749591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.763889</td>\n",
       "      <td>40642.529236</td>\n",
       "      <td>2.902778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        actproxy       revenue  freqscore\n",
       "Score                                    \n",
       "0       0.078380   1248.641478   0.108675\n",
       "1       0.467240   3390.173558   0.652222\n",
       "2       0.944254   3696.373772   1.448557\n",
       "3       1.753610   4653.347843   1.784170\n",
       "4       2.605382   6566.198651   2.010691\n",
       "5       3.435715   8327.254824   2.348034\n",
       "6       5.565334  10174.826573   2.575756\n",
       "7       7.974678  14181.458006   2.757524\n",
       "8      13.684275  24853.297619   2.749591\n",
       "9      20.763889  40642.529236   2.902778"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CCdf['Score'] = CCdf['RevenueClusters'] + CCdf['FreqClusters'] + CCdf['RecencyClusters']\n",
    "CCdf.groupby('Score')['actproxy', 'revenue', 'freqscore'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table above contains a lot of information about the behavior of different users: clearly some are of High Value and others are of Low Value, which really helps to target strategies and incentives for each type of user. To group the users there seems to be natural splits in the groups that we can use to catgeorize:\n",
    "- 0 to 2 = Low Value\n",
    "- 3 to 5 = Mid Value\n",
    "- 6 to 9 = High Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "CCdf['Segments'] = 'Low-Value'\n",
    "CCdf.loc[CCdf['Score'] >3, 'Segments'] = 'Mid-Value'\n",
    "CCdf.loc[CCdf['Score'] >6, 'Segments'] = 'High-Value'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can plot the segments and ivestigate the data with tooltips. When we hover over any of the points, altair returns info on the customer ID, their tenure in months, and their credit limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-903b419ae9454f3898c1088c85ca3f3c\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-903b419ae9454f3898c1088c85ca3f3c\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.2?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-3a4d846ad242a80bc15046b0b32e1c1e.csv\", \"format\": {\"type\": \"csv\"}}, \"mark\": {\"type\": \"circle\", \"size\": 60}, \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"Segments\"}, \"tooltip\": [{\"type\": \"nominal\", \"field\": \"CUST_ID\"}, {\"type\": \"quantitative\", \"field\": \"CREDIT_LIMIT\"}, {\"type\": \"quantitative\", \"field\": \"TENURE\"}], \"x\": {\"type\": \"quantitative\", \"field\": \"freqscore\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"revenue\"}}, \"selection\": {\"selector001\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.2.json\"}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "\n",
    "alt.data_transformers.enable('csv')\n",
    "alt.Chart(CCdf).mark_circle(size=60).encode(\n",
    "    x='freqscore',\n",
    "    y='revenue',\n",
    "    color='Segments',\n",
    "    tooltip=['CUST_ID', 'CREDIT_LIMIT', 'TENURE']\n",
    ").interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-953b5bfc97b344f39882cfd96d8c6faf\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-953b5bfc97b344f39882cfd96d8c6faf\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.2?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-3a4d846ad242a80bc15046b0b32e1c1e.csv\", \"format\": {\"type\": \"csv\"}}, \"mark\": {\"type\": \"circle\", \"size\": 60}, \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"Segments\"}, \"tooltip\": [{\"type\": \"nominal\", \"field\": \"CUST_ID\"}, {\"type\": \"quantitative\", \"field\": \"CREDIT_LIMIT\"}, {\"type\": \"quantitative\", \"field\": \"TENURE\"}], \"x\": {\"type\": \"quantitative\", \"field\": \"freqscore\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"actproxy\"}}, \"selection\": {\"selector002\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.2.json\"}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(CCdf).mark_circle(size=60).encode(\n",
    "    x='freqscore',\n",
    "    y='actproxy',\n",
    "    color='Segments',\n",
    "    tooltip=['CUST_ID', 'CREDIT_LIMIT', 'TENURE']\n",
    ").interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-ca380afd112c46f9906e4ee4ed3e075a\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-ca380afd112c46f9906e4ee4ed3e075a\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.2?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-3a4d846ad242a80bc15046b0b32e1c1e.csv\", \"format\": {\"type\": \"csv\"}}, \"mark\": {\"type\": \"circle\", \"size\": 60}, \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"Segments\"}, \"tooltip\": [{\"type\": \"nominal\", \"field\": \"CUST_ID\"}, {\"type\": \"quantitative\", \"field\": \"CREDIT_LIMIT\"}, {\"type\": \"quantitative\", \"field\": \"TENURE\"}], \"x\": {\"type\": \"quantitative\", \"field\": \"revenue\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"actproxy\"}}, \"selection\": {\"selector003\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.2.json\"}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(CCdf).mark_circle(size=60).encode(\n",
    "    x='revenue',\n",
    "    y='actproxy',\n",
    "    color='Segments',\n",
    "    tooltip=['CUST_ID', 'CREDIT_LIMIT', 'TENURE']\n",
    ").interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visually, the segments have some overlap but the splits seem reasonable as a first approach at targeting different strategies for each segment. Some suggested actions for each are:\n",
    "\n",
    "### Low -> try to increase frequency of card use\n",
    "### Mid -> try to balance frequency and retention\n",
    "### High -> focus on retaining these customers!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}