10/ds_10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Card: segmentation of clients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We develop a model that segments clients according to their credit card usage. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the dataset and explore some of its statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "     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",
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       "8947  C19188    23.398673           0.833333     144.40              0.00   \n",
       "8948  C19189    13.457564           0.833333       0.00              0.00   \n",
       "8949  C19190   372.708075           0.666667    1093.25           1093.25   \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",
       "...                      ...           ...                  ...   \n",
       "8945                  291.12      0.000000             1.000000   \n",
       "8946                  300.00      0.000000             1.000000   \n",
       "8947                  144.40      0.000000             0.833333   \n",
       "8948                    0.00     36.558778             0.000000   \n",
       "8949                    0.00    127.040008             0.666667   \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",
       "8945                    0.000000                          0.833333   \n",
       "8946                    0.000000                          0.833333   \n",
       "8947                    0.000000                          0.666667   \n",
       "8948                    0.000000                          0.000000   \n",
       "8949                    0.666667                          0.000000   \n",
       "\n",
       "      CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
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       "...                      ...               ...            ...           ...   \n",
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       "8948                0.166667                 2              0         500.0   \n",
       "8949                0.333333                 2             23        1200.0   \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",
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       "3        0.000000               NaN          0.000000      12  \n",
       "4      678.334763        244.791237          0.000000      12  \n",
       "...           ...               ...               ...     ...  \n",
       "8945   325.594462         48.886365          0.500000       6  \n",
       "8946   275.861322               NaN          0.000000       6  \n",
       "8947    81.270775         82.418369          0.250000       6  \n",
       "8948    52.549959         55.755628          0.250000       6  \n",
       "8949    63.165404         88.288956          0.000000       6  \n",
       "\n",
       "[8950 rows x 18 columns]"
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     "execution_count": 2,
     "metadata": {},
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    "data = pd.read_table('cc_general.csv', sep=',')\n",
    "data"
   ]
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   "execution_count": 3,
   "metadata": {},
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
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      "INSTALLMENTS_PURCHASES              8950 non-null float64\n",
      "CASH_ADVANCE                        8950 non-null float64\n",
      "PURCHASES_FREQUENCY                 8950 non-null float64\n",
      "ONEOFF_PURCHASES_FREQUENCY          8950 non-null float64\n",
      "PURCHASES_INSTALLMENTS_FREQUENCY    8950 non-null float64\n",
      "CASH_ADVANCE_FREQUENCY              8950 non-null float64\n",
      "CASH_ADVANCE_TRX                    8950 non-null int64\n",
      "PURCHASES_TRX                       8950 non-null int64\n",
      "CREDIT_LIMIT                        8949 non-null float64\n",
      "PAYMENTS                            8950 non-null float64\n",
      "MINIMUM_PAYMENTS                    8637 non-null float64\n",
      "PRC_FULL_PAYMENT                    8950 non-null float64\n",
      "TENURE                              8950 non-null int64\n",
      "dtypes: float64(14), int64(3), object(1)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>8949.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8637.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1564.474828</td>\n",
       "      <td>0.877271</td>\n",
       "      <td>1003.204834</td>\n",
       "      <td>592.437371</td>\n",
       "      <td>411.067645</td>\n",
       "      <td>978.871112</td>\n",
       "      <td>0.490351</td>\n",
       "      <td>0.202458</td>\n",
       "      <td>0.364437</td>\n",
       "      <td>0.135144</td>\n",
       "      <td>3.248827</td>\n",
       "      <td>14.709832</td>\n",
       "      <td>4494.449450</td>\n",
       "      <td>1733.143852</td>\n",
       "      <td>864.206542</td>\n",
       "      <td>0.153715</td>\n",
       "      <td>11.517318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2081.531879</td>\n",
       "      <td>0.236904</td>\n",
       "      <td>2136.634782</td>\n",
       "      <td>1659.887917</td>\n",
       "      <td>904.338115</td>\n",
       "      <td>2097.163877</td>\n",
       "      <td>0.401371</td>\n",
       "      <td>0.298336</td>\n",
       "      <td>0.397448</td>\n",
       "      <td>0.200121</td>\n",
       "      <td>6.824647</td>\n",
       "      <td>24.857649</td>\n",
       "      <td>3638.815725</td>\n",
       "      <td>2895.063757</td>\n",
       "      <td>2372.446607</td>\n",
       "      <td>0.292499</td>\n",
       "      <td>1.338331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019163</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>128.281915</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>39.635000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>383.276166</td>\n",
       "      <td>169.123707</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>873.385231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>361.280000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>856.901546</td>\n",
       "      <td>312.343947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2054.140036</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1110.130000</td>\n",
       "      <td>577.405000</td>\n",
       "      <td>468.637500</td>\n",
       "      <td>1113.821139</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>1901.134317</td>\n",
       "      <td>825.485459</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>19043.138560</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>49039.570000</td>\n",
       "      <td>40761.250000</td>\n",
       "      <td>22500.000000</td>\n",
       "      <td>47137.211760</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>358.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>50721.483360</td>\n",
       "      <td>76406.207520</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BALANCE  BALANCE_FREQUENCY     PURCHASES  ONEOFF_PURCHASES  \\\n",
       "count   8950.000000        8950.000000   8950.000000       8950.000000   \n",
       "mean    1564.474828           0.877271   1003.204834        592.437371   \n",
       "std     2081.531879           0.236904   2136.634782       1659.887917   \n",
       "min        0.000000           0.000000      0.000000          0.000000   \n",
       "25%      128.281915           0.888889     39.635000          0.000000   \n",
       "50%      873.385231           1.000000    361.280000         38.000000   \n",
       "75%     2054.140036           1.000000   1110.130000        577.405000   \n",
       "max    19043.138560           1.000000  49039.570000      40761.250000   \n",
       "\n",
       "       INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "count             8950.000000   8950.000000          8950.000000   \n",
       "mean               411.067645    978.871112             0.490351   \n",
       "std                904.338115   2097.163877             0.401371   \n",
       "min                  0.000000      0.000000             0.000000   \n",
       "25%                  0.000000      0.000000             0.083333   \n",
       "50%                 89.000000      0.000000             0.500000   \n",
       "75%                468.637500   1113.821139             0.916667   \n",
       "max              22500.000000  47137.211760             1.000000   \n",
       "\n",
       "       ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "count                 8950.000000                       8950.000000   \n",
       "mean                     0.202458                          0.364437   \n",
       "std                      0.298336                          0.397448   \n",
       "min                      0.000000                          0.000000   \n",
       "25%                      0.000000                          0.000000   \n",
       "50%                      0.083333                          0.166667   \n",
       "75%                      0.300000                          0.750000   \n",
       "max                      1.000000                          1.000000   \n",
       "\n",
       "       CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
       "count             8950.000000       8950.000000    8950.000000   8949.000000   \n",
       "mean                 0.135144          3.248827      14.709832   4494.449450   \n",
       "std                  0.200121          6.824647      24.857649   3638.815725   \n",
       "min                  0.000000          0.000000       0.000000     50.000000   \n",
       "25%                  0.000000          0.000000       1.000000   1600.000000   \n",
       "50%                  0.000000          0.000000       7.000000   3000.000000   \n",
       "75%                  0.222222          4.000000      17.000000   6500.000000   \n",
       "max                  1.500000        123.000000     358.000000  30000.000000   \n",
       "\n",
       "           PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT       TENURE  \n",
       "count   8950.000000       8637.000000       8950.000000  8950.000000  \n",
       "mean    1733.143852        864.206542          0.153715    11.517318  \n",
       "std     2895.063757       2372.446607          0.292499     1.338331  \n",
       "min        0.000000          0.019163          0.000000     6.000000  \n",
       "25%      383.276166        169.123707          0.000000    12.000000  \n",
       "50%      856.901546        312.343947          0.000000    12.000000  \n",
       "75%     1901.134317        825.485459          0.142857    12.000000  \n",
       "max    50721.483360      76406.207520          1.000000    12.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perfom a preprocess step. This is done in preparation for the later training step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "      <td>0</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",
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       "    <tr>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "0    40.900749           0.818182      95.40              0.00   \n",
       "1  3202.467416           0.909091       0.00              0.00   \n",
       "2  2495.148862           1.000000     773.17            773.17   \n",
       "3  1666.670542           0.636364    1499.00           1499.00   \n",
       "4   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  \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  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop('CUST_ID', axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>40.900749</td>\n",
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       "      <td>95.40</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3202.467416</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <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",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</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>312.343947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "0    40.900749           0.818182      95.40              0.00   \n",
       "1  3202.467416           0.909091       0.00              0.00   \n",
       "2  2495.148862           1.000000     773.17            773.17   \n",
       "3  1666.670542           0.636364    1499.00           1499.00   \n",
       "4   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  \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        312.343947          0.000000      12  \n",
       "4   678.334763        244.791237          0.000000      12  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.fillna(data.median())\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define the dataset for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = np.mean(X, axis=0)\n",
    "sigma = np.std(X, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the dataset is multivariate with several components, the segmentation via KMeans will be affected by 'the curse of dimensionality'. It is then necessary to perform a dimensional reduction step previous to that one. To do so, we first use PCA to pick the best reduction dimension and then we use an Autoencoder as our dimensional reduction final algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_grid = {}\n",
    "for i in range(1,len(data.columns)):\n",
    "    n_components = i\n",
    "    pca = PCA(n_components=n_components)\n",
    "    pca.fit(X)\n",
    "    X_red = pca.transform(X)\n",
    "    X_pred = pca.inverse_transform(X_red)\n",
    "    loss = np.mean(np.mean(np.square(X-X_pred), axis=1), axis=0)\n",
    "    pca_grid[n_components] = [pca, loss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd6500f86a0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = [pca_grid[i][1] for i in range(1,len(data.columns))]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title('PCA')\n",
    "ax.set_xlabel('n_components')\n",
    "ax.set_ylabel('loss')\n",
    "ax.plot(loss, marker='.', color='#0F00FF', markersize=10, linestyle='-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see the best reduction dimension to use. We retrain PCA in this dimension to have a baseline score to beat with our Autoencoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=6, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=6)\n",
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_red = pca.transform(X)\n",
    "X_pred = pca.inverse_transform(X_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46682.1205840837"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.mean(np.square(X-X_pred), axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.70374710820222"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.mean(np.abs(X-X_pred), axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([8.751e+03, 1.550e+02, 2.300e+01, 1.200e+01, 4.000e+00, 3.000e+00,\n",
       "        1.000e+00, 0.000e+00, 0.000e+00, 1.000e+00]),\n",
       " array([3.61837664e+00, 4.27195463e+03, 8.54029088e+03, 1.28086271e+04,\n",
       "        1.70769634e+04, 2.13452996e+04, 2.56136359e+04, 2.98819721e+04,\n",
       "        3.41503084e+04, 3.84186446e+04, 4.26869809e+04]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.hist(np.sum(np.abs(X-X_pred), axis=1), bins=10, alpha=0.5, histtype='stepfilled', color='steelblue', edgecolor='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define our Autoencoder for dimensional reduction. We use the dimension obtained in previous steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def AE():\n",
    "    model = tf.keras.models.Sequential()\n",
    "    f1 = tf.keras.layers.Lambda(function=lambda x: (x-mu)/sigma)\n",
    "    f2 = tf.keras.layers.Dense(units=128,\n",
    "                               activation='tanh', \n",
    "                               kernel_initializer='he_uniform', \n",
    "                               bias_initializer='zeros')\n",
    "    f3 = tf.keras.layers.Dense(units=128,\n",
    "                               activation='tanh', \n",
    "                               kernel_initializer='he_uniform', \n",
    "                               bias_initializer='zeros')\n",
    "    f4 = tf.keras.layers.Dense(units=6,\n",
    "                               activation='tanh', \n",
    "                               kernel_initializer='he_uniform', \n",
    "                               bias_initializer='zeros')\n",
    "    f5 = tf.keras.layers.Dense(units=128,\n",
    "                               activation='tanh', \n",
    "                               kernel_initializer='he_uniform',\n",
    "                               bias_initializer='zeros')\n",
    "    f6 = tf.keras.layers.Dense(units=128,\n",
    "                               activation='tanh', \n",
    "                               kernel_initializer='he_uniform', \n",
    "                               bias_initializer='zeros')\n",
    "    f7 = tf.keras.layers.Dense(units=X.shape[1], \n",
    "                               activation='linear',\n",
    "                               kernel_initializer='he_uniform',\n",
    "                               bias_initializer='zeros')\n",
    "    f8 = tf.keras.layers.Lambda(function=lambda x: x*sigma+mu)\n",
    "    x0 = tf.keras.Input(shape=(X.shape[1]))\n",
    "    x1 = f1(x0)\n",
    "    x2 = f2(x1)\n",
    "    x3 = f3(x2)\n",
    "    x4 = f4(x3)\n",
    "    x5 = f5(x4)\n",
    "    x6 = f6(x5)\n",
    "    x7 = f7(x6)\n",
    "    x8 = f8(x7)\n",
    "    ae = tf.keras.Model(x0, x8) \n",
    "    y0 = tf.keras.Input(shape=(X.shape[1]))\n",
    "    y1 = f1(y0)\n",
    "    y2 = f2(y1)\n",
    "    y3 = f3(y2)\n",
    "    y4 = f4(y3)\n",
    "    encoder = tf.keras.Model(y0, y4)\n",
    "    z0 = tf.keras.Input(shape=(6))\n",
    "    z1 = f5(z0)\n",
    "    z2 = f6(z1)\n",
    "    z3 = f7(z2)\n",
    "    z4 = f8(z3)\n",
    "    decoder = tf.keras.Model(z0, z4)\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, \n",
    "                                         beta_1=0.9, \n",
    "                                         beta_2=0.999, \n",
    "                                         epsilon=1e-07)\n",
    "    ae.compile(loss='mse', metrics=['mae'], optimizer=optimizer)\n",
    "    ae.summary()\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', \n",
    "                                                     patience=100,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1, \n",
    "                                                     min_lr=0.00001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=200,\n",
    "                                                 min_delta=0.001)\n",
    "    ae.fit(X, X, epochs=1000, batch_size=64, callbacks=[callback1, callback2])\n",
    "    return ae, encoder, decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 17)]              0         \n",
      "_________________________________________________________________\n",
      "lambda (Lambda)              (None, 17)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               2304      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 6)                 774       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               896       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 17)                2193      \n",
      "_________________________________________________________________\n",
      "lambda_1 (Lambda)            (None, 17)                0         \n",
      "=================================================================\n",
      "Total params: 39,191\n",
      "Trainable params: 39,191\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 8950 samples\n",
      "Epoch 1/1000\n",
      "8950/8950 [==============================] - 1s 79us/sample - loss: 1233471.7114 - mae: 385.0002\n",
      "Epoch 2/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 641126.1891 - mae: 263.0101\n",
      "Epoch 3/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 480305.7775 - mae: 230.5283\n",
      "Epoch 4/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 402808.9595 - mae: 215.5770\n",
      "Epoch 5/1000\n",
      "8950/8950 [==============================] - 0s 28us/sample - loss: 343975.4278 - mae: 202.8883\n",
      "Epoch 6/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 300242.1897 - mae: 189.8344\n",
      "Epoch 7/1000\n",
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
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     "text": [
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     ]
    },
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     "text": [
      "Epoch 587/1000\n",
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     ]
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     ]
    },
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     "text": [
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     ]
    },
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     "text": [
      "Epoch 888/1000\n",
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     ]
    },
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     "text": [
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      "8950/8950 [==============================] - 0s 25us/sample - loss: 1750.8025 - mae: 14.9948\n",
      "Epoch 965/1000\n",
      "8950/8950 [==============================] - 0s 25us/sample - loss: 1781.1877 - mae: 15.2654\n",
      "Epoch 966/1000\n",
      "8950/8950 [==============================] - 0s 25us/sample - loss: 1783.7409 - mae: 15.3219\n",
      "Epoch 967/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1764.4284 - mae: 15.1390\n",
      "Epoch 968/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1772.0185 - mae: 15.1374\n",
      "Epoch 969/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 1767.6871 - mae: 15.1037\n",
      "Epoch 970/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1777.8377 - mae: 15.2602\n",
      "Epoch 971/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1751.6399 - mae: 14.9859\n",
      "Epoch 972/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 1745.0270 - mae: 14.9167\n",
      "Epoch 973/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 1762.6630 - mae: 15.1291\n",
      "Epoch 974/1000\n",
      "8950/8950 [==============================] - 0s 28us/sample - loss: 1754.0294 - mae: 15.0247\n",
      "Epoch 975/1000\n",
      "8950/8950 [==============================] - 0s 28us/sample - loss: 1738.5550 - mae: 14.8453\n",
      "Epoch 976/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 1767.1371 - mae: 15.2412\n",
      "Epoch 977/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1749.2304 - mae: 15.0596\n",
      "Epoch 978/1000\n",
      "8950/8950 [==============================] - 0s 25us/sample - loss: 1757.1146 - mae: 15.0218\n",
      "Epoch 979/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1771.6396 - mae: 15.2829\n",
      "Epoch 980/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1745.6328 - mae: 14.9682\n",
      "Epoch 981/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1771.1410 - mae: 15.2098\n",
      "Epoch 982/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1765.7614 - mae: 15.2379\n",
      "Epoch 983/1000\n",
      "8950/8950 [==============================] - 0s 25us/sample - loss: 1754.2950 - mae: 15.0118\n",
      "Epoch 984/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1754.5336 - mae: 15.0260\n",
      "Epoch 985/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 1763.8407 - mae: 15.2352\n",
      "Epoch 986/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1757.4798 - mae: 15.1185\n",
      "Epoch 987/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1746.3326 - mae: 14.8896\n",
      "Epoch 988/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 1747.0656 - mae: 15.0702\n",
      "Epoch 989/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 1765.5457 - mae: 15.3031\n",
      "Epoch 990/1000\n",
      "8950/8950 [==============================] - 0s 28us/sample - loss: 1773.9818 - mae: 15.3119\n",
      "Epoch 991/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 1749.4493 - mae: 14.9768\n",
      "Epoch 992/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 1740.7666 - mae: 14.9823\n",
      "Epoch 993/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1745.3771 - mae: 15.0850\n",
      "Epoch 994/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1751.3266 - mae: 15.0421\n",
      "Epoch 995/1000\n",
      "8950/8950 [==============================] - 0s 25us/sample - loss: 1757.7486 - mae: 15.1045\n",
      "Epoch 996/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1750.0537 - mae: 14.9595\n",
      "Epoch 997/1000\n",
      "8950/8950 [==============================] - 0s 25us/sample - loss: 1749.8233 - mae: 15.0402\n",
      "Epoch 998/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1738.7669 - mae: 14.8658\n",
      "Epoch 999/1000\n",
      "8950/8950 [==============================] - 0s 27us/sample - loss: 1742.7991 - mae: 15.0374\n",
      "Epoch 1000/1000\n",
      "8950/8950 [==============================] - 0s 26us/sample - loss: 1740.2520 - mae: 15.0096\n"
     ]
    }
   ],
   "source": [
    "ae, encoder, decoder = AE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8950/8950 [==============================] - 0s 35us/sample - loss: 1711.8777 - mae: 15.0720\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1711.8776738854094, 15.071975]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ae.evaluate(X,X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_red = encoder.predict(X)\n",
    "X_pred = ae.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([8.721e+03, 1.960e+02, 2.200e+01, 6.000e+00, 2.000e+00, 1.000e+00,\n",
       "        1.000e+00, 0.000e+00, 0.000e+00, 1.000e+00]),\n",
       " array([  38.31845201,  845.15879677, 1651.99914152, 2458.83948628,\n",
       "        3265.67983104, 4072.5201758 , 4879.36052056, 5686.20086532,\n",
       "        6493.04121007, 7299.88155483, 8106.72189959]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.hist(np.sum(np.abs(X-X_pred), axis=1), bins=10, alpha=0.5, histtype='stepfilled', color='steelblue', edgecolor='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that our Autoencoder beats PCA, so we decide to use it as our dimensional reduction algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we perform a grid search to select the number of clusters to use in KMeans. The grid used is constrained by common sense specifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "kmeans_grid = {}\n",
    "for i in range(1,20):\n",
    "    n_clusters = 5*i\n",
    "    kmeans = KMeans(n_clusters=n_clusters)\n",
    "    kmeans.fit(X_red)\n",
    "    X_clusters = kmeans.predict(X_red)\n",
    "    X_centroids = kmeans.cluster_centers_[X_clusters,:]\n",
    "    loss = np.mean(np.sum(np.square(X_red-X_centroids), axis=1))\n",
    "    kmeans_grid[n_clusters] = [kmeans, loss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd650416048>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = [kmeans_grid[5*i][1] for i in range(1,20)]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title('KMeans')\n",
    "ax.set_xlabel('n_clusters')\n",
    "ax.set_ylabel('loss')\n",
    "ax.plot(5*np.array(range(1,20)), loss, marker='.', color='#0F00FF', markersize=10, linestyle='-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here and by common sense, we pick the number of clusters to use. We retrain KMeans with this information and pick this model as our final segmentation algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=30, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=30)\n",
    "kmeans.fit(X_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>CLUSTERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</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>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</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>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</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>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</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>312.343947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "0    40.900749           0.818182      95.40              0.00   \n",
       "1  3202.467416           0.909091       0.00              0.00   \n",
       "2  2495.148862           1.000000     773.17            773.17   \n",
       "3  1666.670542           0.636364    1499.00           1499.00   \n",
       "4   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  CLUSTERS  \n",
       "0   201.802084        139.509787          0.000000      12         5  \n",
       "1  4103.032597       1072.340217          0.222222      12        23  \n",
       "2   622.066742        627.284787          0.000000      12        20  \n",
       "3     0.000000        312.343947          0.000000      12        20  \n",
       "4   678.334763        244.791237          0.000000      12         0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:,'CLUSTERS'] = kmeans.predict(X_red)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we show some clusters statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>count</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>941.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1451.167413</td>\n",
       "      <td>0.973730</td>\n",
       "      <td>245.749841</td>\n",
       "      <td>165.542168</td>\n",
       "      <td>80.364166</td>\n",
       "      <td>345.997688</td>\n",
       "      <td>0.254894</td>\n",
       "      <td>0.126662</td>\n",
       "      <td>0.151295</td>\n",
       "      <td>0.148979</td>\n",
       "      <td>2.620616</td>\n",
       "      <td>5.352816</td>\n",
       "      <td>1798.459086</td>\n",
       "      <td>819.612133</td>\n",
       "      <td>532.895450</td>\n",
       "      <td>0.023748</td>\n",
       "      <td>11.669501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>666.921811</td>\n",
       "      <td>0.106319</td>\n",
       "      <td>374.060746</td>\n",
       "      <td>282.484343</td>\n",
       "      <td>187.559254</td>\n",
       "      <td>432.805374</td>\n",
       "      <td>0.336481</td>\n",
       "      <td>0.215289</td>\n",
       "      <td>0.289595</td>\n",
       "      <td>0.155870</td>\n",
       "      <td>3.502516</td>\n",
       "      <td>8.932064</td>\n",
       "      <td>638.440808</td>\n",
       "      <td>635.961939</td>\n",
       "      <td>334.547431</td>\n",
       "      <td>0.103268</td>\n",
       "      <td>1.068014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.271854</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.221665</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1026.220651</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>397.388693</td>\n",
       "      <td>302.185679</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1376.244070</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>159.189426</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>587.264565</td>\n",
       "      <td>449.473659</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1794.531623</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>372.160000</td>\n",
       "      <td>216.170000</td>\n",
       "      <td>62.100000</td>\n",
       "      <td>549.062884</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1018.197785</td>\n",
       "      <td>666.121142</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>3406.473855</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2102.130000</td>\n",
       "      <td>1312.350000</td>\n",
       "      <td>1635.690000</td>\n",
       "      <td>2131.824562</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>4300.000000</td>\n",
       "      <td>3597.140559</td>\n",
       "      <td>2898.375147</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           BALANCE  BALANCE_FREQUENCY    PURCHASES  ONEOFF_PURCHASES  \\\n",
       "count   941.000000         941.000000   941.000000        941.000000   \n",
       "mean   1451.167413           0.973730   245.749841        165.542168   \n",
       "std     666.921811           0.106319   374.060746        282.484343   \n",
       "min       0.271854           0.181818     0.000000          0.000000   \n",
       "25%    1026.220651           1.000000     0.000000          0.000000   \n",
       "50%    1376.244070           1.000000    50.000000          0.000000   \n",
       "75%    1794.531623           1.000000   372.160000        216.170000   \n",
       "max    3406.473855           1.000000  2102.130000       1312.350000   \n",
       "\n",
       "       INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "count              941.000000    941.000000           941.000000   \n",
       "mean                80.364166    345.997688             0.254894   \n",
       "std                187.559254    432.805374             0.336481   \n",
       "min                  0.000000      0.000000             0.000000   \n",
       "25%                  0.000000      0.000000             0.000000   \n",
       "50%                  0.000000    159.189426             0.083333   \n",
       "75%                 62.100000    549.062884             0.416667   \n",
       "max               1635.690000   2131.824562             1.000000   \n",
       "\n",
       "       ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "count                  941.000000                        941.000000   \n",
       "mean                     0.126662                          0.151295   \n",
       "std                      0.215289                          0.289595   \n",
       "min                      0.000000                          0.000000   \n",
       "25%                      0.000000                          0.000000   \n",
       "50%                      0.000000                          0.000000   \n",
       "75%                      0.166667                          0.166667   \n",
       "max                      1.000000                          1.000000   \n",
       "\n",
       "       CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
       "count              941.000000        941.000000     941.000000    941.000000   \n",
       "mean                 0.148979          2.620616       5.352816   1798.459086   \n",
       "std                  0.155870          3.502516       8.932064    638.440808   \n",
       "min                  0.000000          0.000000       0.000000    300.000000   \n",
       "25%                  0.000000          0.000000       0.000000   1200.000000   \n",
       "50%                  0.083333          2.000000       1.000000   1600.000000   \n",
       "75%                  0.250000          4.000000       8.000000   2000.000000   \n",
       "max                  0.916667         38.000000      69.000000   4300.000000   \n",
       "\n",
       "          PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT      TENURE  \n",
       "count   941.000000        941.000000        941.000000  941.000000  \n",
       "mean    819.612133        532.895450          0.023748   11.669501  \n",
       "std     635.961939        334.547431          0.103268    1.068014  \n",
       "min       0.000000          7.221665          0.000000    6.000000  \n",
       "25%     397.388693        302.185679          0.000000   12.000000  \n",
       "50%     587.264565        449.473659          0.000000   12.000000  \n",
       "75%    1018.197785        666.121142          0.000000   12.000000  \n",
       "max    3597.140559       2898.375147          1.000000   12.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.query('CLUSTERS == 0').iloc[:,:-1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>6135.330204</td>\n",
       "      <td>0.998819</td>\n",
       "      <td>6961.808701</td>\n",
       "      <td>4796.423506</td>\n",
       "      <td>2165.385195</td>\n",
       "      <td>377.584099</td>\n",
       "      <td>0.932704</td>\n",
       "      <td>0.742129</td>\n",
       "      <td>0.785616</td>\n",
       "      <td>0.086580</td>\n",
       "      <td>1.818182</td>\n",
       "      <td>86.220779</td>\n",
       "      <td>12886.363636</td>\n",
       "      <td>4641.618378</td>\n",
       "      <td>1838.726739</td>\n",
       "      <td>0.077561</td>\n",
       "      <td>11.974026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2874.486176</td>\n",
       "      <td>0.010360</td>\n",
       "      <td>3015.460552</td>\n",
       "      <td>3128.429339</td>\n",
       "      <td>1536.871420</td>\n",
       "      <td>803.611851</td>\n",
       "      <td>0.151908</td>\n",
       "      <td>0.250297</td>\n",
       "      <td>0.313351</td>\n",
       "      <td>0.180075</td>\n",
       "      <td>4.630355</td>\n",
       "      <td>57.359812</td>\n",
       "      <td>2660.117681</td>\n",
       "      <td>3261.062687</td>\n",
       "      <td>1191.306005</td>\n",
       "      <td>0.241461</td>\n",
       "      <td>0.160101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>627.260806</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>3050.480000</td>\n",
       "      <td>507.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8500.000000</td>\n",
       "      <td>508.797444</td>\n",
       "      <td>198.065894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>3879.064446</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4795.490000</td>\n",
       "      <td>2396.600000</td>\n",
       "      <td>1084.770000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>11100.000000</td>\n",
       "      <td>2190.466875</td>\n",
       "      <td>1133.733957</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>5970.705432</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6141.580000</td>\n",
       "      <td>3959.810000</td>\n",
       "      <td>1910.930000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>12500.000000</td>\n",
       "      <td>3318.260207</td>\n",
       "      <td>1659.775075</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>8281.348085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8710.720000</td>\n",
       "      <td>6402.630000</td>\n",
       "      <td>3018.640000</td>\n",
       "      <td>266.710051</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>14500.000000</td>\n",
       "      <td>6436.518476</td>\n",
       "      <td>2351.640064</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>13673.079610</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20421.590000</td>\n",
       "      <td>16864.560000</td>\n",
       "      <td>6866.690000</td>\n",
       "      <td>4134.316549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>274.000000</td>\n",
       "      <td>21000.000000</td>\n",
       "      <td>13002.303930</td>\n",
       "      <td>6340.898794</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BALANCE  BALANCE_FREQUENCY     PURCHASES  ONEOFF_PURCHASES  \\\n",
       "count     77.000000          77.000000     77.000000         77.000000   \n",
       "mean    6135.330204           0.998819   6961.808701       4796.423506   \n",
       "std     2874.486176           0.010360   3015.460552       3128.429339   \n",
       "min      627.260806           0.909091   3050.480000        507.000000   \n",
       "25%     3879.064446           1.000000   4795.490000       2396.600000   \n",
       "50%     5970.705432           1.000000   6141.580000       3959.810000   \n",
       "75%     8281.348085           1.000000   8710.720000       6402.630000   \n",
       "max    13673.079610           1.000000  20421.590000      16864.560000   \n",
       "\n",
       "       INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "count               77.000000     77.000000            77.000000   \n",
       "mean              2165.385195    377.584099             0.932704   \n",
       "std               1536.871420    803.611851             0.151908   \n",
       "min                  0.000000      0.000000             0.250000   \n",
       "25%               1084.770000      0.000000             0.916667   \n",
       "50%               1910.930000      0.000000             1.000000   \n",
       "75%               3018.640000    266.710051             1.000000   \n",
       "max               6866.690000   4134.316549             1.000000   \n",
       "\n",
       "       ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "count                   77.000000                         77.000000   \n",
       "mean                     0.742129                          0.785616   \n",
       "std                      0.250297                          0.313351   \n",
       "min                      0.083333                          0.000000   \n",
       "25%                      0.500000                          0.666667   \n",
       "50%                      0.833333                          1.000000   \n",
       "75%                      1.000000                          1.000000   \n",
       "max                      1.000000                          1.000000   \n",
       "\n",
       "       CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
       "count               77.000000         77.000000      77.000000     77.000000   \n",
       "mean                 0.086580          1.818182      86.220779  12886.363636   \n",
       "std                  0.180075          4.630355      57.359812   2660.117681   \n",
       "min                  0.000000          0.000000       5.000000   8500.000000   \n",
       "25%                  0.000000          0.000000      45.000000  11100.000000   \n",
       "50%                  0.000000          0.000000      73.000000  12500.000000   \n",
       "75%                  0.083333          1.000000     111.000000  14500.000000   \n",
       "max                  0.833333         26.000000     274.000000  21000.000000   \n",
       "\n",
       "           PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT     TENURE  \n",
       "count     77.000000         77.000000         77.000000  77.000000  \n",
       "mean    4641.618378       1838.726739          0.077561  11.974026  \n",
       "std     3261.062687       1191.306005          0.241461   0.160101  \n",
       "min      508.797444        198.065894          0.000000  11.000000  \n",
       "25%     2190.466875       1133.733957          0.000000  12.000000  \n",
       "50%     3318.260207       1659.775075          0.000000  12.000000  \n",
       "75%     6436.518476       2351.640064          0.000000  12.000000  \n",
       "max    13002.303930       6340.898794          1.000000  12.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.query('CLUSTERS == 1').iloc[:,:-1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>count</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>792.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>575.490831</td>\n",
       "      <td>0.836010</td>\n",
       "      <td>622.008674</td>\n",
       "      <td>238.552008</td>\n",
       "      <td>383.875795</td>\n",
       "      <td>209.585095</td>\n",
       "      <td>0.522602</td>\n",
       "      <td>0.196385</td>\n",
       "      <td>0.381226</td>\n",
       "      <td>0.051381</td>\n",
       "      <td>0.938131</td>\n",
       "      <td>12.491162</td>\n",
       "      <td>6094.702709</td>\n",
       "      <td>906.500719</td>\n",
       "      <td>241.795708</td>\n",
       "      <td>0.237976</td>\n",
       "      <td>11.789141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>777.901879</td>\n",
       "      <td>0.263952</td>\n",
       "      <td>657.692407</td>\n",
       "      <td>288.330709</td>\n",
       "      <td>571.842093</td>\n",
       "      <td>507.835828</td>\n",
       "      <td>0.377532</td>\n",
       "      <td>0.275904</td>\n",
       "      <td>0.388261</td>\n",
       "      <td>0.121287</td>\n",
       "      <td>2.474131</td>\n",
       "      <td>18.066597</td>\n",
       "      <td>1036.065500</td>\n",
       "      <td>806.694715</td>\n",
       "      <td>192.333990</td>\n",
       "      <td>0.341273</td>\n",
       "      <td>0.886610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.117036</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>45.979111</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>132.780000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>376.952556</td>\n",
       "      <td>132.674692</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>157.789605</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>427.790000</td>\n",
       "      <td>121.450000</td>\n",
       "      <td>144.970000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>667.044827</td>\n",
       "      <td>179.932205</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>900.307424</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>905.037500</td>\n",
       "      <td>406.017500</td>\n",
       "      <td>511.680000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>1185.058396</td>\n",
       "      <td>306.785104</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>3543.905366</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4185.940000</td>\n",
       "      <td>1527.300000</td>\n",
       "      <td>3827.240000</td>\n",
       "      <td>3254.666530</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>8500.000000</td>\n",
       "      <td>5321.022187</td>\n",
       "      <td>1584.520204</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           BALANCE  BALANCE_FREQUENCY    PURCHASES  ONEOFF_PURCHASES  \\\n",
       "count   792.000000         792.000000   792.000000        792.000000   \n",
       "mean    575.490831           0.836010   622.008674        238.552008   \n",
       "std     777.901879           0.263952   657.692407        288.330709   \n",
       "min       0.000000           0.000000     0.000000          0.000000   \n",
       "25%      45.979111           0.727273   132.780000          0.000000   \n",
       "50%     157.789605           1.000000   427.790000        121.450000   \n",
       "75%     900.307424           1.000000   905.037500        406.017500   \n",
       "max    3543.905366           1.000000  4185.940000       1527.300000   \n",
       "\n",
       "       INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "count              792.000000    792.000000           792.000000   \n",
       "mean               383.875795    209.585095             0.522602   \n",
       "std                571.842093    507.835828             0.377532   \n",
       "min                  0.000000      0.000000             0.000000   \n",
       "25%                  0.000000      0.000000             0.166667   \n",
       "50%                144.970000      0.000000             0.500000   \n",
       "75%                511.680000      0.000000             1.000000   \n",
       "max               3827.240000   3254.666530             1.000000   \n",
       "\n",
       "       ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "count                  792.000000                        792.000000   \n",
       "mean                     0.196385                          0.381226   \n",
       "std                      0.275904                          0.388261   \n",
       "min                      0.000000                          0.000000   \n",
       "25%                      0.000000                          0.000000   \n",
       "50%                      0.083333                          0.250000   \n",
       "75%                      0.250000                          0.750000   \n",
       "max                      1.000000                          1.000000   \n",
       "\n",
       "       CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
       "count              792.000000        792.000000     792.000000    792.000000   \n",
       "mean                 0.051381          0.938131      12.491162   6094.702709   \n",
       "std                  0.121287          2.474131      18.066597   1036.065500   \n",
       "min                  0.000000          0.000000       0.000000   4000.000000   \n",
       "25%                  0.000000          0.000000       2.000000   5000.000000   \n",
       "50%                  0.000000          0.000000       7.000000   6000.000000   \n",
       "75%                  0.000000          0.000000      17.000000   7000.000000   \n",
       "max                  0.800000         20.000000     232.000000   8500.000000   \n",
       "\n",
       "          PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT      TENURE  \n",
       "count   792.000000        792.000000        792.000000  792.000000  \n",
       "mean    906.500719        241.795708          0.237976   11.789141  \n",
       "std     806.694715        192.333990          0.341273    0.886610  \n",
       "min       0.000000          0.117036          0.000000    6.000000  \n",
       "25%     376.952556        132.674692          0.000000   12.000000  \n",
       "50%     667.044827        179.932205          0.000000   12.000000  \n",
       "75%    1185.058396        306.785104          0.416667   12.000000  \n",
       "max    5321.022187       1584.520204          1.000000   12.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.query('CLUSTERS == 2').iloc[:,:-1].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additional exploration of each clusters would be necessary to make a final conclusion about their relevance and their real meaning."
   ]
  }
 ],
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