Desj_default_prediction_v7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_facturation = pd.read_csv('data/facturation_train.csv')\n",
    "df_performance = pd.read_csv('data/performance_train.csv')\n",
    "df_paiements = pd.read_csv('data/paiements_train.csv')\n",
    "df_transactions = pd.read_csv('data/transactions_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_CPTE</th>\n",
       "      <th>TRANSACTION_AMT</th>\n",
       "      <th>TRANSACTION_DTTM</th>\n",
       "      <th>PAYMENT_REVERSAL_XFLG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID_CPTE, TRANSACTION_AMT, TRANSACTION_DTTM, PAYMENT_REVERSAL_XFLG]\n",
       "Index: []"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_paiements['TRANSACTION_AMT'] = df_paiements['TRANSACTION_AMT'].fillna(0)\n",
    "df_paiements['TRANSACTION_DTTM'] = df_paiements['TRANSACTION_DTTM'].fillna('9999-99-99')\n",
    "df_paiements['PAYMENT_REVERSAL_XFLG'] = df_paiements['PAYMENT_REVERSAL_XFLG'].fillna('Q')\n",
    "\n",
    "df_paiements[df_paiements.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_paiements = df_paiements.dropna()\n",
    "\n",
    "df_paiements['YEAR_MONTH'] = df_paiements['TRANSACTION_DTTM'].map(lambda x: str(x)[:7])\n",
    "df_paiements[\"CPTE_PERIOD_ID\"] = df_paiements[\"ID_CPTE\"].map(str) + '_' + df_paiements[\"YEAR_MONTH\"]\n",
    "\n",
    "df_remboursements = df_paiements[df_paiements.PAYMENT_REVERSAL_XFLG == 'N']\n",
    "df_paiements = df_paiements[df_paiements.PAYMENT_REVERSAL_XFLG == 'Q']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_reversed = df_remboursements.groupby(['CPTE_PERIOD_ID'])\n",
    "group_paid = df_paiements.groupby(['CPTE_PERIOD_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reversed_ammount = pd.DataFrame(group_reversed.sum()['TRANSACTION_AMT'])\n",
    "df_reversed_ammount['COUNT_XFLG-N'] = group_reversed.count()['PAYMENT_REVERSAL_XFLG']\n",
    "df_reversed_ammount.rename(columns={'TRANSACTION_AMT': 'TRANSACTION_AMT_REFUNDED'}, inplace=True)\n",
    "df_reversed_ammount.reset_index(level=0, inplace=True)\n",
    "\n",
    "df_paid_ammount = pd.DataFrame(group_paid.sum()['TRANSACTION_AMT'])\n",
    "df_paid_ammount['COUNT_XFLG-Q'] = group_paid.count()['PAYMENT_REVERSAL_XFLG']\n",
    "df_paid_ammount.rename(columns={'TRANSACTION_AMT': 'TRANSACTION_AMT_PAID'}, inplace=True)\n",
    "df_paid_ammount.reset_index(level=0, inplace=True)\n",
    "\n",
    "df_paie_remb = pd.merge(df_paid_ammount, df_reversed_ammount[['CPTE_PERIOD_ID','TRANSACTION_AMT_REFUNDED','COUNT_XFLG-N']], how = 'outer', left_on = 'CPTE_PERIOD_ID', right_on = 'CPTE_PERIOD_ID')\n",
    "df_paie_remb.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_facturation['YEAR_MONTH'] = df_facturation['PERIODID_MY'].map(lambda x: str(x)[:7])\n",
    "df_facturation[\"CPTE_PERIOD_ID\"] = df_facturation[\"ID_CPTE\"].map(str) + '_' + df_facturation[\"YEAR_MONTH\"]\n",
    "df_facturation.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_CPTE</th>\n",
       "      <th>PERIODID_MY</th>\n",
       "      <th>StatementDate</th>\n",
       "      <th>CurrentTotalBalance</th>\n",
       "      <th>CashBalance</th>\n",
       "      <th>CreditLimit</th>\n",
       "      <th>DelqCycle</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th>CPTE_PERIOD_ID</th>\n",
       "      <th>TRANSACTION_AMT_PAID</th>\n",
       "      <th>COUNT_XFLG-Q</th>\n",
       "      <th>TRANSACTION_AMT_REFUNDED</th>\n",
       "      <th>COUNT_XFLG-N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>866.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>99690111_2014-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2014-12-03</td>\n",
       "      <td>1151.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-12</td>\n",
       "      <td>99690111_2014-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2012-11-29</td>\n",
       "      <td>266.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>57427180_2012-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>2012-03-03</td>\n",
       "      <td>809.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-03</td>\n",
       "      <td>57427180_2012-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>2012-10-31</td>\n",
       "      <td>689.52</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-11</td>\n",
       "      <td>57427180_2012-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>2012-02-03</td>\n",
       "      <td>234.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-02</td>\n",
       "      <td>57427180_2012-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>2011-11-04</td>\n",
       "      <td>392.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-11</td>\n",
       "      <td>57427180_2011-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-12-02</td>\n",
       "      <td>219.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>57427180_2011-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>29617912.0</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2014-11-29</td>\n",
       "      <td>301.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-12</td>\n",
       "      <td>29617912_2014-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>29617912.0</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>468.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>29617912_2014-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>61632809.0</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>2015-08-13</td>\n",
       "      <td>661.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-08</td>\n",
       "      <td>61632809_2015-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>61632809.0</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>2015-06-17</td>\n",
       "      <td>766.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-06</td>\n",
       "      <td>61632809_2015-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>61632809.0</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>2014-11-15</td>\n",
       "      <td>348.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>61632809_2014-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>61632809.0</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2014-12-13</td>\n",
       "      <td>914.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-12</td>\n",
       "      <td>61632809_2014-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>14117855.0</td>\n",
       "      <td>2013-02-01</td>\n",
       "      <td>2013-02-13</td>\n",
       "      <td>884.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-02</td>\n",
       "      <td>14117855_2013-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>14117855.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>2012-11-15</td>\n",
       "      <td>1249.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-11</td>\n",
       "      <td>14117855_2012-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>14117855.0</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2012-12-13</td>\n",
       "      <td>833.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>14117855_2012-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>14117855.0</td>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>2013-05-18</td>\n",
       "      <td>1180.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-05</td>\n",
       "      <td>14117855_2013-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>14117855.0</td>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>2013-09-18</td>\n",
       "      <td>1275.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-09</td>\n",
       "      <td>14117855_2013-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>23700394.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>2012-11-15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-11</td>\n",
       "      <td>23700394_2012-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>23700394.0</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2012-12-13</td>\n",
       "      <td>77.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>23700394_2012-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>27881705.0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-12-16</td>\n",
       "      <td>994.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>27881705_2011-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>27881705.0</td>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>2011-11-19</td>\n",
       "      <td>721.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-11</td>\n",
       "      <td>27881705_2011-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>46100731.0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-12-14</td>\n",
       "      <td>729.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>46100731_2011-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>46100731.0</td>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>2011-11-18</td>\n",
       "      <td>803.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-11</td>\n",
       "      <td>46100731_2011-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>46100731.0</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012-01-20</td>\n",
       "      <td>1559.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-01</td>\n",
       "      <td>46100731_2012-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>46100731.0</td>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>577.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05</td>\n",
       "      <td>46100731_2012-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>58512689.0</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2013-12-13</td>\n",
       "      <td>2904.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-12</td>\n",
       "      <td>58512689_2013-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>58512689.0</td>\n",
       "      <td>2013-11-01</td>\n",
       "      <td>2013-11-15</td>\n",
       "      <td>2533.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-11</td>\n",
       "      <td>58512689_2013-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>58512689.0</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>781.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>58512689_2014-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167467</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96571043_2016-01</td>\n",
       "      <td>834.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167468</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96709031_2015-01</td>\n",
       "      <td>50.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167469</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96857733_9999-99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167470</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96862919_2015-01</td>\n",
       "      <td>32.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167471</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97022474_2017-01</td>\n",
       "      <td>1378.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167472</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97031597_2015-01</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167473</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97320752_2016-01</td>\n",
       "      <td>219.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167474</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97380236_2013-01</td>\n",
       "      <td>26.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167475</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97463348_2015-01</td>\n",
       "      <td>209.07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167476</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97684201_2013-01</td>\n",
       "      <td>557.47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167477</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97721182_2014-01</td>\n",
       "      <td>490.78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167478</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97837487_2014-01</td>\n",
       "      <td>1010.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167479</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97889965_2014-01</td>\n",
       "      <td>500.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167480</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97977582_2014-01</td>\n",
       "      <td>2040.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167481</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98092244_2016-01</td>\n",
       "      <td>169.05</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167482</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98356667_2016-01</td>\n",
       "      <td>35.00</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167483</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98412906_2013-01</td>\n",
       "      <td>206.51</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167484</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98609254_2013-01</td>\n",
       "      <td>160.50</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167485</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98692556_2016-01</td>\n",
       "      <td>309.00</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167486</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98890086_2014-01</td>\n",
       "      <td>202.00</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167487</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98975426_9999-99</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167488</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99087451_2017-01</td>\n",
       "      <td>154.50</td>\n",
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       "    <tr>\n",
       "      <th>167489</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99196871_2015-01</td>\n",
       "      <td>53.50</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167490</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99229176_2013-01</td>\n",
       "      <td>82.40</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167491</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99366285_2015-01</td>\n",
       "      <td>119.78</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167492</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99445050_2017-01</td>\n",
       "      <td>101.00</td>\n",
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       "    <tr>\n",
       "      <th>167493</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99604336_2013-01</td>\n",
       "      <td>1650.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167494</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99678331_2014-01</td>\n",
       "      <td>127.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167495</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99697225_2015-01</td>\n",
       "      <td>238.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167496</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99821890_2014-01</td>\n",
       "      <td>208.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38383 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID_CPTE PERIODID_MY StatementDate  CurrentTotalBalance  \\\n",
       "1       99690111.0  2014-11-01    2014-11-03               866.00   \n",
       "7       99690111.0  2014-12-01    2014-12-03              1151.85   \n",
       "17      57427180.0  2012-12-01    2012-11-29               266.06   \n",
       "21      57427180.0  2012-03-01    2012-03-03               809.12   \n",
       "22      57427180.0  2012-11-01    2012-10-31               689.52   \n",
       "25      57427180.0  2012-02-01    2012-02-03               234.00   \n",
       "26      57427180.0  2011-11-01    2011-11-04               392.20   \n",
       "27      57427180.0  2011-12-01    2011-12-02               219.00   \n",
       "35      29617912.0  2014-12-01    2014-11-29               301.04   \n",
       "40      29617912.0  2014-11-01    2014-10-31               468.64   \n",
       "44      61632809.0  2015-08-01    2015-08-13               661.26   \n",
       "48      61632809.0  2015-06-01    2015-06-17               766.48   \n",
       "49      61632809.0  2014-11-01    2014-11-15               348.84   \n",
       "53      61632809.0  2014-12-01    2014-12-13               914.64   \n",
       "59      14117855.0  2013-02-01    2013-02-13               884.77   \n",
       "61      14117855.0  2012-11-01    2012-11-15              1249.04   \n",
       "64      14117855.0  2012-12-01    2012-12-13               833.27   \n",
       "65      14117855.0  2013-05-01    2013-05-18              1180.21   \n",
       "67      14117855.0  2013-09-01    2013-09-18              1275.14   \n",
       "74      23700394.0  2012-11-01    2012-11-15                 0.00   \n",
       "79      23700394.0  2012-12-01    2012-12-13                77.70   \n",
       "91      27881705.0  2011-12-01    2011-12-16               994.00   \n",
       "97      27881705.0  2011-11-01    2011-11-19               721.18   \n",
       "101     46100731.0  2011-12-01    2011-12-14               729.75   \n",
       "103     46100731.0  2011-11-01    2011-11-18               803.40   \n",
       "104     46100731.0  2012-01-01    2012-01-20              1559.26   \n",
       "108     46100731.0  2012-05-01    2012-05-14               577.32   \n",
       "112     58512689.0  2013-12-01    2013-12-13              2904.72   \n",
       "113     58512689.0  2013-11-01    2013-11-15              2533.40   \n",
       "116     58512689.0  2014-11-01    2014-11-18               781.77   \n",
       "...            ...         ...           ...                  ...   \n",
       "167467         NaN         NaN           NaN                  NaN   \n",
       "167468         NaN         NaN           NaN                  NaN   \n",
       "167469         NaN         NaN           NaN                  NaN   \n",
       "167470         NaN         NaN           NaN                  NaN   \n",
       "167471         NaN         NaN           NaN                  NaN   \n",
       "167472         NaN         NaN           NaN                  NaN   \n",
       "167473         NaN         NaN           NaN                  NaN   \n",
       "167474         NaN         NaN           NaN                  NaN   \n",
       "167475         NaN         NaN           NaN                  NaN   \n",
       "167476         NaN         NaN           NaN                  NaN   \n",
       "167477         NaN         NaN           NaN                  NaN   \n",
       "167478         NaN         NaN           NaN                  NaN   \n",
       "167479         NaN         NaN           NaN                  NaN   \n",
       "167480         NaN         NaN           NaN                  NaN   \n",
       "167481         NaN         NaN           NaN                  NaN   \n",
       "167482         NaN         NaN           NaN                  NaN   \n",
       "167483         NaN         NaN           NaN                  NaN   \n",
       "167484         NaN         NaN           NaN                  NaN   \n",
       "167485         NaN         NaN           NaN                  NaN   \n",
       "167486         NaN         NaN           NaN                  NaN   \n",
       "167487         NaN         NaN           NaN                  NaN   \n",
       "167488         NaN         NaN           NaN                  NaN   \n",
       "167489         NaN         NaN           NaN                  NaN   \n",
       "167490         NaN         NaN           NaN                  NaN   \n",
       "167491         NaN         NaN           NaN                  NaN   \n",
       "167492         NaN         NaN           NaN                  NaN   \n",
       "167493         NaN         NaN           NaN                  NaN   \n",
       "167494         NaN         NaN           NaN                  NaN   \n",
       "167495         NaN         NaN           NaN                  NaN   \n",
       "167496         NaN         NaN           NaN                  NaN   \n",
       "\n",
       "        CashBalance  CreditLimit  DelqCycle YEAR_MONTH    CPTE_PERIOD_ID  \\\n",
       "1               0.0      12000.0        0.0    2014-11  99690111_2014-11   \n",
       "7               0.0      12000.0        0.0    2014-12  99690111_2014-12   \n",
       "17              0.0       2800.0        0.0    2012-12  57427180_2012-12   \n",
       "21              0.0       2800.0        1.0    2012-03  57427180_2012-03   \n",
       "22             20.0       2800.0        0.0    2012-11  57427180_2012-11   \n",
       "25              0.0       2800.0        0.0    2012-02  57427180_2012-02   \n",
       "26              0.0       2800.0        0.0    2011-11  57427180_2011-11   \n",
       "27              0.0       2800.0        0.0    2011-12  57427180_2011-12   \n",
       "35              0.0       5800.0        0.0    2014-12  29617912_2014-12   \n",
       "40              0.0       5800.0        0.0    2014-11  29617912_2014-11   \n",
       "44              0.0       1600.0        0.0    2015-08  61632809_2015-08   \n",
       "48              0.0       1600.0        0.0    2015-06  61632809_2015-06   \n",
       "49              0.0       1600.0        0.0    2014-11  61632809_2014-11   \n",
       "53              0.0       1600.0        0.0    2014-12  61632809_2014-12   \n",
       "59              0.0       3800.0        0.0    2013-02  14117855_2013-02   \n",
       "61              0.0       3800.0        0.0    2012-11  14117855_2012-11   \n",
       "64              0.0       3800.0        0.0    2012-12  14117855_2012-12   \n",
       "65              0.0       3800.0        0.0    2013-05  14117855_2013-05   \n",
       "67              0.0       3800.0        0.0    2013-09  14117855_2013-09   \n",
       "74              0.0       1200.0        0.0    2012-11  23700394_2012-11   \n",
       "79              0.0       1200.0        0.0    2012-12  23700394_2012-12   \n",
       "91              0.0       3000.0        0.0    2011-12  27881705_2011-12   \n",
       "97              0.0       3000.0        0.0    2011-11  27881705_2011-11   \n",
       "101             0.0       2600.0        0.0    2011-12  46100731_2011-12   \n",
       "103             0.0       2600.0        0.0    2011-11  46100731_2011-11   \n",
       "104             0.0       2600.0        1.0    2012-01  46100731_2012-01   \n",
       "108             0.0       2600.0        0.0    2012-05  46100731_2012-05   \n",
       "112             0.0       8800.0        0.0    2013-12  58512689_2013-12   \n",
       "113             0.0       8800.0        0.0    2013-11  58512689_2013-11   \n",
       "116             0.0       8800.0        0.0    2014-11  58512689_2014-11   \n",
       "...             ...          ...        ...        ...               ...   \n",
       "167467          NaN          NaN        NaN        NaN  96571043_2016-01   \n",
       "167468          NaN          NaN        NaN        NaN  96709031_2015-01   \n",
       "167469          NaN          NaN        NaN        NaN  96857733_9999-99   \n",
       "167470          NaN          NaN        NaN        NaN  96862919_2015-01   \n",
       "167471          NaN          NaN        NaN        NaN  97022474_2017-01   \n",
       "167472          NaN          NaN        NaN        NaN  97031597_2015-01   \n",
       "167473          NaN          NaN        NaN        NaN  97320752_2016-01   \n",
       "167474          NaN          NaN        NaN        NaN  97380236_2013-01   \n",
       "167475          NaN          NaN        NaN        NaN  97463348_2015-01   \n",
       "167476          NaN          NaN        NaN        NaN  97684201_2013-01   \n",
       "167477          NaN          NaN        NaN        NaN  97721182_2014-01   \n",
       "167478          NaN          NaN        NaN        NaN  97837487_2014-01   \n",
       "167479          NaN          NaN        NaN        NaN  97889965_2014-01   \n",
       "167480          NaN          NaN        NaN        NaN  97977582_2014-01   \n",
       "167481          NaN          NaN        NaN        NaN  98092244_2016-01   \n",
       "167482          NaN          NaN        NaN        NaN  98356667_2016-01   \n",
       "167483          NaN          NaN        NaN        NaN  98412906_2013-01   \n",
       "167484          NaN          NaN        NaN        NaN  98609254_2013-01   \n",
       "167485          NaN          NaN        NaN        NaN  98692556_2016-01   \n",
       "167486          NaN          NaN        NaN        NaN  98890086_2014-01   \n",
       "167487          NaN          NaN        NaN        NaN  98975426_9999-99   \n",
       "167488          NaN          NaN        NaN        NaN  99087451_2017-01   \n",
       "167489          NaN          NaN        NaN        NaN  99196871_2015-01   \n",
       "167490          NaN          NaN        NaN        NaN  99229176_2013-01   \n",
       "167491          NaN          NaN        NaN        NaN  99366285_2015-01   \n",
       "167492          NaN          NaN        NaN        NaN  99445050_2017-01   \n",
       "167493          NaN          NaN        NaN        NaN  99604336_2013-01   \n",
       "167494          NaN          NaN        NaN        NaN  99678331_2014-01   \n",
       "167495          NaN          NaN        NaN        NaN  99697225_2015-01   \n",
       "167496          NaN          NaN        NaN        NaN  99821890_2014-01   \n",
       "\n",
       "        TRANSACTION_AMT_PAID  COUNT_XFLG-Q  TRANSACTION_AMT_REFUNDED  \\\n",
       "1                        NaN           NaN                       NaN   \n",
       "7                        NaN           NaN                       NaN   \n",
       "17                       NaN           NaN                       NaN   \n",
       "21                       NaN           NaN                       NaN   \n",
       "22                       NaN           NaN                       NaN   \n",
       "25                       NaN           NaN                       NaN   \n",
       "26                       NaN           NaN                       NaN   \n",
       "27                       NaN           NaN                       NaN   \n",
       "35                       NaN           NaN                       NaN   \n",
       "40                       NaN           NaN                       NaN   \n",
       "44                       NaN           NaN                       NaN   \n",
       "48                       NaN           NaN                       NaN   \n",
       "49                       NaN           NaN                       NaN   \n",
       "53                       NaN           NaN                       NaN   \n",
       "59                       NaN           NaN                       NaN   \n",
       "61                       NaN           NaN                       NaN   \n",
       "64                       NaN           NaN                       NaN   \n",
       "65                       NaN           NaN                       NaN   \n",
       "67                       NaN           NaN                       NaN   \n",
       "74                       NaN           NaN                       NaN   \n",
       "79                       NaN           NaN                       NaN   \n",
       "91                       NaN           NaN                       NaN   \n",
       "97                       NaN           NaN                       NaN   \n",
       "101                      NaN           NaN                       NaN   \n",
       "103                      NaN           NaN                       NaN   \n",
       "104                      NaN           NaN                       NaN   \n",
       "108                      NaN           NaN                       NaN   \n",
       "112                      NaN           NaN                       NaN   \n",
       "113                      NaN           NaN                       NaN   \n",
       "116                      NaN           NaN                       NaN   \n",
       "...                      ...           ...                       ...   \n",
       "167467                834.00           1.0                       0.0   \n",
       "167468                 50.50           1.0                       0.0   \n",
       "167469                  0.00           1.0                       0.0   \n",
       "167470                 32.10           1.0                       0.0   \n",
       "167471               1378.00           1.0                       0.0   \n",
       "167472                100.00           1.0                       0.0   \n",
       "167473                219.00           1.0                       0.0   \n",
       "167474                 26.75           1.0                       0.0   \n",
       "167475                209.07           1.0                       0.0   \n",
       "167476                557.47           1.0                       0.0   \n",
       "167477                490.78           1.0                       0.0   \n",
       "167478               1010.00           1.0                       0.0   \n",
       "167479                500.00           1.0                       0.0   \n",
       "167480               2040.00           1.0                       0.0   \n",
       "167481                169.05           1.0                       0.0   \n",
       "167482                 35.00           1.0                       0.0   \n",
       "167483                206.51           1.0                       0.0   \n",
       "167484                160.50           1.0                       0.0   \n",
       "167485                309.00           1.0                       0.0   \n",
       "167486                202.00           1.0                       0.0   \n",
       "167487                  0.00           1.0                       0.0   \n",
       "167488                154.50           1.0                       0.0   \n",
       "167489                 53.50           1.0                       0.0   \n",
       "167490                 82.40           1.0                       0.0   \n",
       "167491                119.78           1.0                       0.0   \n",
       "167492                101.00           1.0                       0.0   \n",
       "167493               1650.06           1.0                       0.0   \n",
       "167494                127.50           1.0                       0.0   \n",
       "167495                238.00           1.0                       0.0   \n",
       "167496                208.00           1.0                       0.0   \n",
       "\n",
       "        COUNT_XFLG-N  \n",
       "1                NaN  \n",
       "7                NaN  \n",
       "17               NaN  \n",
       "21               NaN  \n",
       "22               NaN  \n",
       "25               NaN  \n",
       "26               NaN  \n",
       "27               NaN  \n",
       "35               NaN  \n",
       "40               NaN  \n",
       "44               NaN  \n",
       "48               NaN  \n",
       "49               NaN  \n",
       "53               NaN  \n",
       "59               NaN  \n",
       "61               NaN  \n",
       "64               NaN  \n",
       "65               NaN  \n",
       "67               NaN  \n",
       "74               NaN  \n",
       "79               NaN  \n",
       "91               NaN  \n",
       "97               NaN  \n",
       "101              NaN  \n",
       "103              NaN  \n",
       "104              NaN  \n",
       "108              NaN  \n",
       "112              NaN  \n",
       "113              NaN  \n",
       "116              NaN  \n",
       "...              ...  \n",
       "167467           0.0  \n",
       "167468           0.0  \n",
       "167469           0.0  \n",
       "167470           0.0  \n",
       "167471           0.0  \n",
       "167472           0.0  \n",
       "167473           0.0  \n",
       "167474           0.0  \n",
       "167475           0.0  \n",
       "167476           0.0  \n",
       "167477           0.0  \n",
       "167478           0.0  \n",
       "167479           0.0  \n",
       "167480           0.0  \n",
       "167481           0.0  \n",
       "167482           0.0  \n",
       "167483           0.0  \n",
       "167484           0.0  \n",
       "167485           0.0  \n",
       "167486           0.0  \n",
       "167487           0.0  \n",
       "167488           0.0  \n",
       "167489           0.0  \n",
       "167490           0.0  \n",
       "167491           0.0  \n",
       "167492           0.0  \n",
       "167493           0.0  \n",
       "167494           0.0  \n",
       "167495           0.0  \n",
       "167496           0.0  \n",
       "\n",
       "[38383 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_facturation = pd.merge(df_facturation, df_paie_remb , right_on='CPTE_PERIOD_ID', left_on='CPTE_PERIOD_ID', how='outer')\n",
    "df_facturation[df_facturation.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>ID_CPTE</th>\n",
       "      <th>PERIODID_MY</th>\n",
       "      <th>StatementDate</th>\n",
       "      <th>CurrentTotalBalance</th>\n",
       "      <th>CashBalance</th>\n",
       "      <th>CreditLimit</th>\n",
       "      <th>DelqCycle</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th>CPTE_PERIOD_ID</th>\n",
       "      <th>TRANSACTION_AMT_PAID</th>\n",
       "      <th>COUNT_XFLG-Q</th>\n",
       "      <th>TRANSACTION_AMT_REFUNDED</th>\n",
       "      <th>COUNT_XFLG-N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID_CPTE, PERIODID_MY, StatementDate, CurrentTotalBalance, CashBalance, CreditLimit, DelqCycle, YEAR_MONTH, CPTE_PERIOD_ID, TRANSACTION_AMT_PAID, COUNT_XFLG-Q, TRANSACTION_AMT_REFUNDED, COUNT_XFLG-N]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_facturation[['TRANSACTION_AMT_PAID','COUNT_XFLG-Q','TRANSACTION_AMT_REFUNDED','COUNT_XFLG-N']] = df_facturation[['TRANSACTION_AMT_PAID','COUNT_XFLG-Q', 'TRANSACTION_AMT_REFUNDED', 'COUNT_XFLG-N']].fillna(0)\n",
    "df_facturation = df_facturation.dropna() # drop les lignes de transactions où le compte n'avait pas d'infos\n",
    "df_facturation[df_facturation.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_facturation = pd.merge(df_facturation, df_performance[['ID_CPTE','Default']], right_on='ID_CPTE', left_on='ID_CPTE', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_CPTE</th>\n",
       "      <th>CurrentTotalBalance</th>\n",
       "      <th>CashBalance</th>\n",
       "      <th>CreditLimit</th>\n",
       "      <th>DelqCycle</th>\n",
       "      <th>TRANSACTION_AMT_PAID</th>\n",
       "      <th>COUNT_XFLG-Q</th>\n",
       "      <th>TRANSACTION_AMT_REFUNDED</th>\n",
       "      <th>COUNT_XFLG-N</th>\n",
       "      <th>Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID_CPTE</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006811</td>\n",
       "      <td>-0.004087</td>\n",
       "      <td>-0.015966</td>\n",
       "      <td>0.008605</td>\n",
       "      <td>-0.007597</td>\n",
       "      <td>0.006847</td>\n",
       "      <td>-0.001781</td>\n",
       "      <td>-0.002287</td>\n",
       "      <td>0.016244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CurrentTotalBalance</th>\n",
       "      <td>-0.006811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.291153</td>\n",
       "      <td>0.494997</td>\n",
       "      <td>0.053045</td>\n",
       "      <td>0.184747</td>\n",
       "      <td>0.034397</td>\n",
       "      <td>-0.000769</td>\n",
       "      <td>-0.000631</td>\n",
       "      <td>0.175303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CashBalance</th>\n",
       "      <td>-0.004087</td>\n",
       "      <td>0.291153</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.128159</td>\n",
       "      <td>0.034813</td>\n",
       "      <td>0.031437</td>\n",
       "      <td>0.012622</td>\n",
       "      <td>-0.000807</td>\n",
       "      <td>0.003711</td>\n",
       "      <td>0.139300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreditLimit</th>\n",
       "      <td>-0.015966</td>\n",
       "      <td>0.494997</td>\n",
       "      <td>0.128159</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084344</td>\n",
       "      <td>0.304902</td>\n",
       "      <td>-0.067603</td>\n",
       "      <td>0.002625</td>\n",
       "      <td>-0.001748</td>\n",
       "      <td>-0.127098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DelqCycle</th>\n",
       "      <td>0.008605</td>\n",
       "      <td>0.053045</td>\n",
       "      <td>0.034813</td>\n",
       "      <td>-0.084344</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.067976</td>\n",
       "      <td>-0.095616</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>0.213422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSACTION_AMT_PAID</th>\n",
       "      <td>-0.007597</td>\n",
       "      <td>0.184747</td>\n",
       "      <td>0.031437</td>\n",
       "      <td>0.304902</td>\n",
       "      <td>-0.067976</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.259004</td>\n",
       "      <td>0.071124</td>\n",
       "      <td>0.033300</td>\n",
       "      <td>-0.084246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUNT_XFLG-Q</th>\n",
       "      <td>0.006847</td>\n",
       "      <td>0.034397</td>\n",
       "      <td>0.012622</td>\n",
       "      <td>-0.067603</td>\n",
       "      <td>-0.095616</td>\n",
       "      <td>0.259004</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>0.008354</td>\n",
       "      <td>0.023073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSACTION_AMT_REFUNDED</th>\n",
       "      <td>-0.001781</td>\n",
       "      <td>-0.000769</td>\n",
       "      <td>-0.000807</td>\n",
       "      <td>0.002625</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.071124</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.454583</td>\n",
       "      <td>-0.000709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUNT_XFLG-N</th>\n",
       "      <td>-0.002287</td>\n",
       "      <td>-0.000631</td>\n",
       "      <td>0.003711</td>\n",
       "      <td>-0.001748</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>0.033300</td>\n",
       "      <td>0.008354</td>\n",
       "      <td>0.454583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Default</th>\n",
       "      <td>0.016244</td>\n",
       "      <td>0.175303</td>\n",
       "      <td>0.139300</td>\n",
       "      <td>-0.127098</td>\n",
       "      <td>0.213422</td>\n",
       "      <td>-0.084246</td>\n",
       "      <td>0.023073</td>\n",
       "      <td>-0.000709</td>\n",
       "      <td>0.004358</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ID_CPTE  CurrentTotalBalance  CashBalance  \\\n",
       "ID_CPTE                   1.000000            -0.006811    -0.004087   \n",
       "CurrentTotalBalance      -0.006811             1.000000     0.291153   \n",
       "CashBalance              -0.004087             0.291153     1.000000   \n",
       "CreditLimit              -0.015966             0.494997     0.128159   \n",
       "DelqCycle                 0.008605             0.053045     0.034813   \n",
       "TRANSACTION_AMT_PAID     -0.007597             0.184747     0.031437   \n",
       "COUNT_XFLG-Q              0.006847             0.034397     0.012622   \n",
       "TRANSACTION_AMT_REFUNDED -0.001781            -0.000769    -0.000807   \n",
       "COUNT_XFLG-N             -0.002287            -0.000631     0.003711   \n",
       "Default                   0.016244             0.175303     0.139300   \n",
       "\n",
       "                          CreditLimit  DelqCycle  TRANSACTION_AMT_PAID  \\\n",
       "ID_CPTE                     -0.015966   0.008605             -0.007597   \n",
       "CurrentTotalBalance          0.494997   0.053045              0.184747   \n",
       "CashBalance                  0.128159   0.034813              0.031437   \n",
       "CreditLimit                  1.000000  -0.084344              0.304902   \n",
       "DelqCycle                   -0.084344   1.000000             -0.067976   \n",
       "TRANSACTION_AMT_PAID         0.304902  -0.067976              1.000000   \n",
       "COUNT_XFLG-Q                -0.067603  -0.095616              0.259004   \n",
       "TRANSACTION_AMT_REFUNDED     0.002625   0.000278              0.071124   \n",
       "COUNT_XFLG-N                -0.001748   0.000320              0.033300   \n",
       "Default                     -0.127098   0.213422             -0.084246   \n",
       "\n",
       "                          COUNT_XFLG-Q  TRANSACTION_AMT_REFUNDED  \\\n",
       "ID_CPTE                       0.006847                 -0.001781   \n",
       "CurrentTotalBalance           0.034397                 -0.000769   \n",
       "CashBalance                   0.012622                 -0.000807   \n",
       "CreditLimit                  -0.067603                  0.002625   \n",
       "DelqCycle                    -0.095616                  0.000278   \n",
       "TRANSACTION_AMT_PAID          0.259004                  0.071124   \n",
       "COUNT_XFLG-Q                  1.000000                  0.002646   \n",
       "TRANSACTION_AMT_REFUNDED      0.002646                  1.000000   \n",
       "COUNT_XFLG-N                  0.008354                  0.454583   \n",
       "Default                       0.023073                 -0.000709   \n",
       "\n",
       "                          COUNT_XFLG-N   Default  \n",
       "ID_CPTE                      -0.002287  0.016244  \n",
       "CurrentTotalBalance          -0.000631  0.175303  \n",
       "CashBalance                   0.003711  0.139300  \n",
       "CreditLimit                  -0.001748 -0.127098  \n",
       "DelqCycle                     0.000320  0.213422  \n",
       "TRANSACTION_AMT_PAID          0.033300 -0.084246  \n",
       "COUNT_XFLG-Q                  0.008354  0.023073  \n",
       "TRANSACTION_AMT_REFUNDED      0.454583 -0.000709  \n",
       "COUNT_XFLG-N                  1.000000  0.004358  \n",
       "Default                       0.004358  1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_facturation.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_CPTE</th>\n",
       "      <th>PERIODID_MY</th>\n",
       "      <th>StatementDate</th>\n",
       "      <th>CurrentTotalBalance</th>\n",
       "      <th>CashBalance</th>\n",
       "      <th>CreditLimit</th>\n",
       "      <th>DelqCycle</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th>CPTE_PERIOD_ID</th>\n",
       "      <th>TRANSACTION_AMT_PAID</th>\n",
       "      <th>COUNT_XFLG-Q</th>\n",
       "      <th>TRANSACTION_AMT_REFUNDED</th>\n",
       "      <th>COUNT_XFLG-N</th>\n",
       "      <th>Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>2015-05-03</td>\n",
       "      <td>8497.84</td>\n",
       "      <td>4293.12</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-05</td>\n",
       "      <td>99690111_2015-05</td>\n",
       "      <td>176.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>866.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>99690111_2014-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>10790.95</td>\n",
       "      <td>5224.44</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-06</td>\n",
       "      <td>99690111_2015-06</td>\n",
       "      <td>303.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>2015-10-04</td>\n",
       "      <td>12388.46</td>\n",
       "      <td>4786.08</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-10</td>\n",
       "      <td>99690111_2015-10</td>\n",
       "      <td>244.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>12746.50</td>\n",
       "      <td>4818.48</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-11</td>\n",
       "      <td>99690111_2015-11</td>\n",
       "      <td>250.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>10610.05</td>\n",
       "      <td>4753.35</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-08</td>\n",
       "      <td>99690111_2015-08</td>\n",
       "      <td>267.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>11417.12</td>\n",
       "      <td>4616.46</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-09</td>\n",
       "      <td>99690111_2015-09</td>\n",
       "      <td>226.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2014-12-03</td>\n",
       "      <td>1151.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-12</td>\n",
       "      <td>99690111_2014-12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>4045.67</td>\n",
       "      <td>1148.45</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-02</td>\n",
       "      <td>99690111_2015-02</td>\n",
       "      <td>303.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>13119.60</td>\n",
       "      <td>4791.44</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-12</td>\n",
       "      <td>99690111_2015-12</td>\n",
       "      <td>273.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>2298.96</td>\n",
       "      <td>350.00</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>99690111_2015-01</td>\n",
       "      <td>262.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>2015-03-03</td>\n",
       "      <td>5926.20</td>\n",
       "      <td>2567.25</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>99690111_2015-03</td>\n",
       "      <td>200.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>6916.62</td>\n",
       "      <td>3307.33</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-04</td>\n",
       "      <td>99690111_2015-04</td>\n",
       "      <td>288.80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>10560.00</td>\n",
       "      <td>5127.54</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-07</td>\n",
       "      <td>99690111_2015-07</td>\n",
       "      <td>618.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>2012-08-02</td>\n",
       "      <td>243.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-08</td>\n",
       "      <td>57427180_2012-08</td>\n",
       "      <td>252.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>2012-10-04</td>\n",
       "      <td>489.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-10</td>\n",
       "      <td>57427180_2012-10</td>\n",
       "      <td>476.89</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>2012-05-03</td>\n",
       "      <td>260.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05</td>\n",
       "      <td>57427180_2012-05</td>\n",
       "      <td>454.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2012-11-29</td>\n",
       "      <td>266.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>57427180_2012-12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-07-01</td>\n",
       "      <td>2012-07-06</td>\n",
       "      <td>416.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-07</td>\n",
       "      <td>57427180_2012-07</td>\n",
       "      <td>884.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-06-01</td>\n",
       "      <td>2012-06-02</td>\n",
       "      <td>210.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-06</td>\n",
       "      <td>57427180_2012-06</td>\n",
       "      <td>412.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>233.58</td>\n",
       "      <td>236.08</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-01</td>\n",
       "      <td>57427180_2012-01</td>\n",
       "      <td>494.60</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>2012-03-03</td>\n",
       "      <td>809.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-03</td>\n",
       "      <td>57427180_2012-03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>2012-10-31</td>\n",
       "      <td>689.52</td>\n",
       "      <td>20.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-11</td>\n",
       "      <td>57427180_2012-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>2012-08-31</td>\n",
       "      <td>410.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09</td>\n",
       "      <td>57427180_2012-09</td>\n",
       "      <td>393.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>2012-03-31</td>\n",
       "      <td>1058.84</td>\n",
       "      <td>1.01</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2012-04</td>\n",
       "      <td>57427180_2012-04</td>\n",
       "      <td>1076.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>2012-02-03</td>\n",
       "      <td>234.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-02</td>\n",
       "      <td>57427180_2012-02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>2011-11-04</td>\n",
       "      <td>392.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-11</td>\n",
       "      <td>57427180_2011-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>57427180.0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-12-02</td>\n",
       "      <td>219.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>57427180_2011-12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29617912.0</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>135.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-02</td>\n",
       "      <td>29617912_2015-02</td>\n",
       "      <td>144.45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29617912.0</td>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>229.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-09</td>\n",
       "      <td>29617912_2015-09</td>\n",
       "      <td>433.63</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166513</th>\n",
       "      <td>97548597.0</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2012-12-21</td>\n",
       "      <td>2945.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>97548597_2012-12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166514</th>\n",
       "      <td>97548597.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>2012-11-27</td>\n",
       "      <td>1114.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-11</td>\n",
       "      <td>97548597_2012-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166515</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2012-12-22</td>\n",
       "      <td>2267.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>12603910_2012-12</td>\n",
       "      <td>1715.64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166516</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>2012-11-21</td>\n",
       "      <td>1733.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-11</td>\n",
       "      <td>12603910_2012-11</td>\n",
       "      <td>1924.65</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166517</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>2012-10-22</td>\n",
       "      <td>1944.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-10</td>\n",
       "      <td>12603910_2012-10</td>\n",
       "      <td>4090.13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166518</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>2012-09-24</td>\n",
       "      <td>4091.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09</td>\n",
       "      <td>12603910_2012-09</td>\n",
       "      <td>2910.78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166519</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>2012-08-20</td>\n",
       "      <td>2995.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-08</td>\n",
       "      <td>12603910_2012-08</td>\n",
       "      <td>2504.78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166520</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-07-01</td>\n",
       "      <td>2012-07-20</td>\n",
       "      <td>2387.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-07</td>\n",
       "      <td>12603910_2012-07</td>\n",
       "      <td>2447.55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166521</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-06-01</td>\n",
       "      <td>2012-06-23</td>\n",
       "      <td>2424.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-06</td>\n",
       "      <td>12603910_2012-06</td>\n",
       "      <td>7868.64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166522</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>2012-05-21</td>\n",
       "      <td>7943.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05</td>\n",
       "      <td>12603910_2012-05</td>\n",
       "      <td>2052.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166523</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>2012-04-23</td>\n",
       "      <td>2135.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-04</td>\n",
       "      <td>12603910_2012-04</td>\n",
       "      <td>2265.42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166524</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>2012-03-19</td>\n",
       "      <td>2221.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-03</td>\n",
       "      <td>12603910_2012-03</td>\n",
       "      <td>2218.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166525</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>2012-02-17</td>\n",
       "      <td>2218.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-02</td>\n",
       "      <td>12603910_2012-02</td>\n",
       "      <td>1826.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166526</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012-01-23</td>\n",
       "      <td>1897.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-01</td>\n",
       "      <td>12603910_2012-01</td>\n",
       "      <td>1861.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166527</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-12-22</td>\n",
       "      <td>1789.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>12603910_2011-12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166528</th>\n",
       "      <td>12603910.0</td>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>2011-11-24</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-11</td>\n",
       "      <td>12603910_2011-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166529</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2015-12-16</td>\n",
       "      <td>2140.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-12</td>\n",
       "      <td>11346742_2015-12</td>\n",
       "      <td>2864.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166530</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>2015-11-13</td>\n",
       "      <td>2727.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-11</td>\n",
       "      <td>11346742_2015-11</td>\n",
       "      <td>2474.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166531</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>2015-10-19</td>\n",
       "      <td>2500.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-10</td>\n",
       "      <td>11346742_2015-10</td>\n",
       "      <td>2036.26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166532</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>2015-09-16</td>\n",
       "      <td>2036.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-09</td>\n",
       "      <td>11346742_2015-09</td>\n",
       "      <td>1625.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166533</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>2015-08-13</td>\n",
       "      <td>1609.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-08</td>\n",
       "      <td>11346742_2015-08</td>\n",
       "      <td>1966.64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166534</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>2015-07-16</td>\n",
       "      <td>2023.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-07</td>\n",
       "      <td>11346742_2015-07</td>\n",
       "      <td>1956.24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166535</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>2015-06-12</td>\n",
       "      <td>1955.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-06</td>\n",
       "      <td>11346742_2015-06</td>\n",
       "      <td>2276.88</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166536</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>2015-05-11</td>\n",
       "      <td>2276.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-05</td>\n",
       "      <td>11346742_2015-05</td>\n",
       "      <td>2607.96</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166537</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>2633.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-04</td>\n",
       "      <td>11346742_2015-04</td>\n",
       "      <td>1676.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166538</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>2015-03-15</td>\n",
       "      <td>1793.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>11346742_2015-03</td>\n",
       "      <td>1816.99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166539</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>2015-02-11</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-02</td>\n",
       "      <td>11346742_2015-02</td>\n",
       "      <td>3063.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166540</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-14</td>\n",
       "      <td>3215.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>11346742_2015-01</td>\n",
       "      <td>2314.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166541</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2014-12-13</td>\n",
       "      <td>2247.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-12</td>\n",
       "      <td>11346742_2014-12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166542</th>\n",
       "      <td>11346742.0</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>2014-11-15</td>\n",
       "      <td>3190.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>11346742_2014-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166543 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID_CPTE PERIODID_MY StatementDate  CurrentTotalBalance  \\\n",
       "0       99690111.0  2015-05-01    2015-05-03              8497.84   \n",
       "1       99690111.0  2014-11-01    2014-11-03               866.00   \n",
       "2       99690111.0  2015-06-01    2015-05-31             10790.95   \n",
       "3       99690111.0  2015-10-01    2015-10-04             12388.46   \n",
       "4       99690111.0  2015-11-01    2015-11-02             12746.50   \n",
       "5       99690111.0  2015-08-01    2015-08-02             10610.05   \n",
       "6       99690111.0  2015-09-01    2015-08-30             11417.12   \n",
       "7       99690111.0  2014-12-01    2014-12-03              1151.85   \n",
       "8       99690111.0  2015-02-01    2015-01-31              4045.67   \n",
       "9       99690111.0  2015-12-01    2015-11-30             13119.60   \n",
       "10      99690111.0  2015-01-01    2015-01-02              2298.96   \n",
       "11      99690111.0  2015-03-01    2015-03-03              5926.20   \n",
       "12      99690111.0  2015-04-01    2015-03-31              6916.62   \n",
       "13      99690111.0  2015-07-01    2015-07-05             10560.00   \n",
       "14      57427180.0  2012-08-01    2012-08-02               243.41   \n",
       "15      57427180.0  2012-10-01    2012-10-04               489.72   \n",
       "16      57427180.0  2012-05-01    2012-05-03               260.00   \n",
       "17      57427180.0  2012-12-01    2012-11-29               266.06   \n",
       "18      57427180.0  2012-07-01    2012-07-06               416.00   \n",
       "19      57427180.0  2012-06-01    2012-06-02               210.08   \n",
       "20      57427180.0  2012-01-01    2012-01-02               233.58   \n",
       "21      57427180.0  2012-03-01    2012-03-03               809.12   \n",
       "22      57427180.0  2012-11-01    2012-10-31               689.52   \n",
       "23      57427180.0  2012-09-01    2012-08-31               410.88   \n",
       "24      57427180.0  2012-04-01    2012-03-31              1058.84   \n",
       "25      57427180.0  2012-02-01    2012-02-03               234.00   \n",
       "26      57427180.0  2011-11-01    2011-11-04               392.20   \n",
       "27      57427180.0  2011-12-01    2011-12-02               219.00   \n",
       "28      29617912.0  2015-02-01    2015-01-30               135.00   \n",
       "29      29617912.0  2015-09-01    2015-08-30               229.84   \n",
       "...            ...         ...           ...                  ...   \n",
       "166513  97548597.0  2012-12-01    2012-12-21              2945.71   \n",
       "166514  97548597.0  2012-11-01    2012-11-27              1114.86   \n",
       "166515  12603910.0  2012-12-01    2012-12-22              2267.34   \n",
       "166516  12603910.0  2012-11-01    2012-11-21              1733.49   \n",
       "166517  12603910.0  2012-10-01    2012-10-22              1944.04   \n",
       "166518  12603910.0  2012-09-01    2012-09-24              4091.16   \n",
       "166519  12603910.0  2012-08-01    2012-08-20              2995.56   \n",
       "166520  12603910.0  2012-07-01    2012-07-20              2387.64   \n",
       "166521  12603910.0  2012-06-01    2012-06-23              2424.24   \n",
       "166522  12603910.0  2012-05-01    2012-05-21              7943.25   \n",
       "166523  12603910.0  2012-04-01    2012-04-23              2135.12   \n",
       "166524  12603910.0  2012-03-01    2012-03-19              2221.00   \n",
       "166525  12603910.0  2012-02-01    2012-02-17              2218.11   \n",
       "166526  12603910.0  2012-01-01    2012-01-23              1897.11   \n",
       "166527  12603910.0  2011-12-01    2011-12-22              1789.00   \n",
       "166528  12603910.0  2011-11-01    2011-11-24              2016.00   \n",
       "166529  11346742.0  2015-12-01    2015-12-16              2140.34   \n",
       "166530  11346742.0  2015-11-01    2015-11-13              2727.00   \n",
       "166531  11346742.0  2015-10-01    2015-10-19              2500.02   \n",
       "166532  11346742.0  2015-09-01    2015-09-16              2036.26   \n",
       "166533  11346742.0  2015-08-01    2015-08-13              1609.00   \n",
       "166534  11346742.0  2015-07-01    2015-07-16              2023.37   \n",
       "166535  11346742.0  2015-06-01    2015-06-12              1955.20   \n",
       "166536  11346742.0  2015-05-01    2015-05-11              2276.88   \n",
       "166537  11346742.0  2015-04-01    2015-04-13              2633.28   \n",
       "166538  11346742.0  2015-03-01    2015-03-15              1793.32   \n",
       "166539  11346742.0  2015-02-01    2015-02-11              1800.00   \n",
       "166540  11346742.0  2015-01-01    2015-01-14              3215.10   \n",
       "166541  11346742.0  2014-12-01    2014-12-13              2247.25   \n",
       "166542  11346742.0  2014-11-01    2014-11-15              3190.94   \n",
       "\n",
       "        CashBalance  CreditLimit  DelqCycle YEAR_MONTH    CPTE_PERIOD_ID  \\\n",
       "0           4293.12      16200.0        0.0    2015-05  99690111_2015-05   \n",
       "1              0.00      12000.0        0.0    2014-11  99690111_2014-11   \n",
       "2           5224.44      16200.0        0.0    2015-06  99690111_2015-06   \n",
       "3           4786.08      16200.0        0.0    2015-10  99690111_2015-10   \n",
       "4           4818.48      16200.0        0.0    2015-11  99690111_2015-11   \n",
       "5           4753.35      16200.0        0.0    2015-08  99690111_2015-08   \n",
       "6           4616.46      16200.0        0.0    2015-09  99690111_2015-09   \n",
       "7              0.00      12000.0        0.0    2014-12  99690111_2014-12   \n",
       "8           1148.45      16200.0        0.0    2015-02  99690111_2015-02   \n",
       "9           4791.44      16200.0        0.0    2015-12  99690111_2015-12   \n",
       "10           350.00      12000.0        0.0    2015-01  99690111_2015-01   \n",
       "11          2567.25      16200.0        0.0    2015-03  99690111_2015-03   \n",
       "12          3307.33      16200.0        0.0    2015-04  99690111_2015-04   \n",
       "13          5127.54      16200.0        0.0    2015-07  99690111_2015-07   \n",
       "14             0.00       2800.0        0.0    2012-08  57427180_2012-08   \n",
       "15             0.00       2800.0        0.0    2012-10  57427180_2012-10   \n",
       "16             0.00       2800.0        0.0    2012-05  57427180_2012-05   \n",
       "17             0.00       2800.0        0.0    2012-12  57427180_2012-12   \n",
       "18             0.00       2800.0        0.0    2012-07  57427180_2012-07   \n",
       "19             0.00       2800.0        0.0    2012-06  57427180_2012-06   \n",
       "20           236.08       2800.0        0.0    2012-01  57427180_2012-01   \n",
       "21             0.00       2800.0        1.0    2012-03  57427180_2012-03   \n",
       "22            20.00       2800.0        0.0    2012-11  57427180_2012-11   \n",
       "23             0.00       2800.0        0.0    2012-09  57427180_2012-09   \n",
       "24             1.01       2800.0        2.0    2012-04  57427180_2012-04   \n",
       "25             0.00       2800.0        0.0    2012-02  57427180_2012-02   \n",
       "26             0.00       2800.0        0.0    2011-11  57427180_2011-11   \n",
       "27             0.00       2800.0        0.0    2011-12  57427180_2011-12   \n",
       "28             0.00       5800.0        0.0    2015-02  29617912_2015-02   \n",
       "29             0.00       5800.0        0.0    2015-09  29617912_2015-09   \n",
       "...             ...          ...        ...        ...               ...   \n",
       "166513         0.00       7600.0        0.0    2012-12  97548597_2012-12   \n",
       "166514         0.00       7600.0        0.0    2012-11  97548597_2012-11   \n",
       "166515         0.00      20200.0        0.0    2012-12  12603910_2012-12   \n",
       "166516         0.00      20200.0        0.0    2012-11  12603910_2012-11   \n",
       "166517         0.00      20200.0        0.0    2012-10  12603910_2012-10   \n",
       "166518         0.00      20200.0        0.0    2012-09  12603910_2012-09   \n",
       "166519         0.00      20200.0        0.0    2012-08  12603910_2012-08   \n",
       "166520         0.00      20200.0        0.0    2012-07  12603910_2012-07   \n",
       "166521         0.00      20200.0        0.0    2012-06  12603910_2012-06   \n",
       "166522         0.00      20200.0        0.0    2012-05  12603910_2012-05   \n",
       "166523         0.00      20200.0        0.0    2012-04  12603910_2012-04   \n",
       "166524         0.00      20200.0        0.0    2012-03  12603910_2012-03   \n",
       "166525         0.00      20200.0        0.0    2012-02  12603910_2012-02   \n",
       "166526         0.00      20200.0        0.0    2012-01  12603910_2012-01   \n",
       "166527         0.00      20200.0        0.0    2011-12  12603910_2011-12   \n",
       "166528         0.00      20200.0        0.0    2011-11  12603910_2011-11   \n",
       "166529         0.00      20200.0        0.0    2015-12  11346742_2015-12   \n",
       "166530         0.00      20200.0        0.0    2015-11  11346742_2015-11   \n",
       "166531         0.00      20200.0        0.0    2015-10  11346742_2015-10   \n",
       "166532         0.00      20200.0        0.0    2015-09  11346742_2015-09   \n",
       "166533         0.00      20200.0        0.0    2015-08  11346742_2015-08   \n",
       "166534         0.00      20200.0        0.0    2015-07  11346742_2015-07   \n",
       "166535         0.00      20200.0        0.0    2015-06  11346742_2015-06   \n",
       "166536         0.00      15200.0        0.0    2015-05  11346742_2015-05   \n",
       "166537         0.00      15200.0        0.0    2015-04  11346742_2015-04   \n",
       "166538         0.00      15200.0        0.0    2015-03  11346742_2015-03   \n",
       "166539         0.00      15200.0        0.0    2015-02  11346742_2015-02   \n",
       "166540         0.00      15200.0        0.0    2015-01  11346742_2015-01   \n",
       "166541         0.00      15200.0        0.0    2014-12  11346742_2014-12   \n",
       "166542         0.00      15200.0        0.0    2014-11  11346742_2014-11   \n",
       "\n",
       "        TRANSACTION_AMT_PAID  COUNT_XFLG-Q  TRANSACTION_AMT_REFUNDED  \\\n",
       "0                     176.80           1.0                       0.0   \n",
       "1                       0.00           0.0                       0.0   \n",
       "2                     303.00           1.0                       0.0   \n",
       "3                     244.80           1.0                       0.0   \n",
       "4                     250.00           1.0                       0.0   \n",
       "5                     267.50           1.0                       0.0   \n",
       "6                     226.60           1.0                       0.0   \n",
       "7                       0.00           0.0                       0.0   \n",
       "8                     303.00           1.0                       0.0   \n",
       "9                     273.00           1.0                       0.0   \n",
       "10                    262.50           1.0                       0.0   \n",
       "11                    200.00           1.0                       0.0   \n",
       "12                    288.80           2.0                       0.0   \n",
       "13                    618.00           1.0                       0.0   \n",
       "14                    252.50           1.0                       0.0   \n",
       "15                    476.89           1.0                       0.0   \n",
       "16                    454.50           1.0                       0.0   \n",
       "17                      0.00           0.0                       0.0   \n",
       "18                    884.00           1.0                       0.0   \n",
       "19                    412.00           1.0                       0.0   \n",
       "20                    494.60           2.0                       0.0   \n",
       "21                      0.00           0.0                       0.0   \n",
       "22                      0.00           0.0                       0.0   \n",
       "23                    393.90           1.0                       0.0   \n",
       "24                   1076.25           1.0                       0.0   \n",
       "25                      0.00           0.0                       0.0   \n",
       "26                      0.00           0.0                       0.0   \n",
       "27                      0.00           0.0                       0.0   \n",
       "28                    144.45           1.0                       0.0   \n",
       "29                    433.63           2.0                       0.0   \n",
       "...                      ...           ...                       ...   \n",
       "166513                  0.00           0.0                       0.0   \n",
       "166514                  0.00           0.0                       0.0   \n",
       "166515               1715.64           1.0                       0.0   \n",
       "166516               1924.65           1.0                       0.0   \n",
       "166517               4090.13           1.0                       0.0   \n",
       "166518               2910.78           1.0                       0.0   \n",
       "166519               2504.78           1.0                       0.0   \n",
       "166520               2447.55           1.0                       0.0   \n",
       "166521               7868.64           1.0                       0.0   \n",
       "166522               2052.00           1.0                       0.0   \n",
       "166523               2265.42           1.0                       0.0   \n",
       "166524               2218.11           1.0                       0.0   \n",
       "166525               1826.19           1.0                       0.0   \n",
       "166526               1861.60           1.0                       0.0   \n",
       "166527                  0.00           0.0                       0.0   \n",
       "166528                  0.00           0.0                       0.0   \n",
       "166529               2864.40           1.0                       0.0   \n",
       "166530               2474.50           1.0                       0.0   \n",
       "166531               2036.26           1.0                       0.0   \n",
       "166532               1625.09           1.0                       0.0   \n",
       "166533               1966.64           1.0                       0.0   \n",
       "166534               1956.24           1.0                       0.0   \n",
       "166535               2276.88           1.0                       0.0   \n",
       "166536               2607.96           1.0                       0.0   \n",
       "166537               1676.00           1.0                       0.0   \n",
       "166538               1816.99           1.0                       0.0   \n",
       "166539               3063.00           1.0                       0.0   \n",
       "166540               2314.00           1.0                       0.0   \n",
       "166541                  0.00           0.0                       0.0   \n",
       "166542                  0.00           0.0                       0.0   \n",
       "\n",
       "        COUNT_XFLG-N  Default  \n",
       "0                0.0        0  \n",
       "1                0.0        0  \n",
       "2                0.0        0  \n",
       "3                0.0        0  \n",
       "4                0.0        0  \n",
       "5                0.0        0  \n",
       "6                0.0        0  \n",
       "7                0.0        0  \n",
       "8                0.0        0  \n",
       "9                0.0        0  \n",
       "10               0.0        0  \n",
       "11               0.0        0  \n",
       "12               0.0        0  \n",
       "13               0.0        0  \n",
       "14               0.0        0  \n",
       "15               0.0        0  \n",
       "16               0.0        0  \n",
       "17               0.0        0  \n",
       "18               0.0        0  \n",
       "19               0.0        0  \n",
       "20               0.0        0  \n",
       "21               0.0        0  \n",
       "22               0.0        0  \n",
       "23               0.0        0  \n",
       "24               0.0        0  \n",
       "25               0.0        0  \n",
       "26               0.0        0  \n",
       "27               0.0        0  \n",
       "28               0.0        0  \n",
       "29               0.0        0  \n",
       "...              ...      ...  \n",
       "166513           0.0        1  \n",
       "166514           0.0        1  \n",
       "166515           0.0        0  \n",
       "166516           0.0        0  \n",
       "166517           0.0        0  \n",
       "166518           0.0        0  \n",
       "166519           0.0        0  \n",
       "166520           0.0        0  \n",
       "166521           0.0        0  \n",
       "166522           0.0        0  \n",
       "166523           0.0        0  \n",
       "166524           0.0        0  \n",
       "166525           0.0        0  \n",
       "166526           0.0        0  \n",
       "166527           0.0        0  \n",
       "166528           0.0        0  \n",
       "166529           0.0        0  \n",
       "166530           0.0        0  \n",
       "166531           0.0        0  \n",
       "166532           0.0        0  \n",
       "166533           0.0        0  \n",
       "166534           0.0        0  \n",
       "166535           0.0        0  \n",
       "166536           0.0        0  \n",
       "166537           0.0        0  \n",
       "166538           0.0        0  \n",
       "166539           0.0        0  \n",
       "166540           0.0        0  \n",
       "166541           0.0        0  \n",
       "166542           0.0        0  \n",
       "\n",
       "[166543 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df_facturation\n",
    "#df1 = df1[df1.ID_CPTE == 94147317].sort_values(['PERIODID_MY_x']).tail(13)\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_CPTE</th>\n",
       "      <th>PERIODID_MY</th>\n",
       "      <th>StatementDate</th>\n",
       "      <th>CurrentTotalBalance</th>\n",
       "      <th>CashBalance</th>\n",
       "      <th>CreditLimit</th>\n",
       "      <th>DelqCycle</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th>CPTE_PERIOD_ID</th>\n",
       "      <th>TRANSACTION_AMT_PAID</th>\n",
       "      <th>COUNT_XFLG-Q</th>\n",
       "      <th>TRANSACTION_AMT_REFUNDED</th>\n",
       "      <th>COUNT_XFLG-N</th>\n",
       "      <th>Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>2298.96</td>\n",
       "      <td>350.00</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>99690111_2015-01</td>\n",
       "      <td>262.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>4045.67</td>\n",
       "      <td>1148.45</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-02</td>\n",
       "      <td>99690111_2015-02</td>\n",
       "      <td>303.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>2015-03-03</td>\n",
       "      <td>5926.20</td>\n",
       "      <td>2567.25</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>99690111_2015-03</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>6916.62</td>\n",
       "      <td>3307.33</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-04</td>\n",
       "      <td>99690111_2015-04</td>\n",
       "      <td>288.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>2015-05-03</td>\n",
       "      <td>8497.84</td>\n",
       "      <td>4293.12</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-05</td>\n",
       "      <td>99690111_2015-05</td>\n",
       "      <td>176.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>10790.95</td>\n",
       "      <td>5224.44</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-06</td>\n",
       "      <td>99690111_2015-06</td>\n",
       "      <td>303.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>10560.00</td>\n",
       "      <td>5127.54</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-07</td>\n",
       "      <td>99690111_2015-07</td>\n",
       "      <td>618.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>10610.05</td>\n",
       "      <td>4753.35</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-08</td>\n",
       "      <td>99690111_2015-08</td>\n",
       "      <td>267.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>11417.12</td>\n",
       "      <td>4616.46</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-09</td>\n",
       "      <td>99690111_2015-09</td>\n",
       "      <td>226.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>2015-10-04</td>\n",
       "      <td>12388.46</td>\n",
       "      <td>4786.08</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-10</td>\n",
       "      <td>99690111_2015-10</td>\n",
       "      <td>244.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>12746.50</td>\n",
       "      <td>4818.48</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-11</td>\n",
       "      <td>99690111_2015-11</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99690111.0</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>13119.60</td>\n",
       "      <td>4791.44</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-12</td>\n",
       "      <td>99690111_2015-12</td>\n",
       "      <td>273.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID_CPTE PERIODID_MY StatementDate  CurrentTotalBalance  CashBalance  \\\n",
       "10  99690111.0  2015-01-01    2015-01-02              2298.96       350.00   \n",
       "8   99690111.0  2015-02-01    2015-01-31              4045.67      1148.45   \n",
       "11  99690111.0  2015-03-01    2015-03-03              5926.20      2567.25   \n",
       "12  99690111.0  2015-04-01    2015-03-31              6916.62      3307.33   \n",
       "0   99690111.0  2015-05-01    2015-05-03              8497.84      4293.12   \n",
       "2   99690111.0  2015-06-01    2015-05-31             10790.95      5224.44   \n",
       "13  99690111.0  2015-07-01    2015-07-05             10560.00      5127.54   \n",
       "5   99690111.0  2015-08-01    2015-08-02             10610.05      4753.35   \n",
       "6   99690111.0  2015-09-01    2015-08-30             11417.12      4616.46   \n",
       "3   99690111.0  2015-10-01    2015-10-04             12388.46      4786.08   \n",
       "4   99690111.0  2015-11-01    2015-11-02             12746.50      4818.48   \n",
       "9   99690111.0  2015-12-01    2015-11-30             13119.60      4791.44   \n",
       "\n",
       "    CreditLimit  DelqCycle YEAR_MONTH    CPTE_PERIOD_ID  TRANSACTION_AMT_PAID  \\\n",
       "10      12000.0        0.0    2015-01  99690111_2015-01                 262.5   \n",
       "8       16200.0        0.0    2015-02  99690111_2015-02                 303.0   \n",
       "11      16200.0        0.0    2015-03  99690111_2015-03                 200.0   \n",
       "12      16200.0        0.0    2015-04  99690111_2015-04                 288.8   \n",
       "0       16200.0        0.0    2015-05  99690111_2015-05                 176.8   \n",
       "2       16200.0        0.0    2015-06  99690111_2015-06                 303.0   \n",
       "13      16200.0        0.0    2015-07  99690111_2015-07                 618.0   \n",
       "5       16200.0        0.0    2015-08  99690111_2015-08                 267.5   \n",
       "6       16200.0        0.0    2015-09  99690111_2015-09                 226.6   \n",
       "3       16200.0        0.0    2015-10  99690111_2015-10                 244.8   \n",
       "4       16200.0        0.0    2015-11  99690111_2015-11                 250.0   \n",
       "9       16200.0        0.0    2015-12  99690111_2015-12                 273.0   \n",
       "\n",
       "    COUNT_XFLG-Q  TRANSACTION_AMT_REFUNDED  COUNT_XFLG-N  Default  \n",
       "10           1.0                       0.0           0.0        0  \n",
       "8            1.0                       0.0           0.0        0  \n",
       "11           1.0                       0.0           0.0        0  \n",
       "12           2.0                       0.0           0.0        0  \n",
       "0            1.0                       0.0           0.0        0  \n",
       "2            1.0                       0.0           0.0        0  \n",
       "13           1.0                       0.0           0.0        0  \n",
       "5            1.0                       0.0           0.0        0  \n",
       "6            1.0                       0.0           0.0        0  \n",
       "3            1.0                       0.0           0.0        0  \n",
       "4            1.0                       0.0           0.0        0  \n",
       "9            1.0                       0.0           0.0        0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df1.ID_CPTE.unique()\n",
    "#df1.isnull().values.any()\n",
    "df1[df1.ID_CPTE == 99690111].sort_values(['PERIODID_MY']).tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ID_CPTE</th>\n",
       "      <th>Default</th>\n",
       "      <th>Year</th>\n",
       "      <th>CreditLimit_sum</th>\n",
       "      <th>CreditLimit_mean</th>\n",
       "      <th>CreditLimit_max</th>\n",
       "      <th>CreditLimit_min</th>\n",
       "      <th>CreditLimit_std</th>\n",
       "      <th>CurrentTotalBalance_sum</th>\n",
       "      <th>CurrentTotalBalance_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>COUNT_XFLG-N_month_3</th>\n",
       "      <th>COUNT_XFLG-N_month_4</th>\n",
       "      <th>COUNT_XFLG-N_month_5</th>\n",
       "      <th>COUNT_XFLG-N_month_6</th>\n",
       "      <th>COUNT_XFLG-N_month_7</th>\n",
       "      <th>COUNT_XFLG-N_month_8</th>\n",
       "      <th>COUNT_XFLG-N_month_9</th>\n",
       "      <th>COUNT_XFLG-N_month_10</th>\n",
       "      <th>COUNT_XFLG-N_month_11</th>\n",
       "      <th>COUNT_XFLG-N_month_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  ID_CPTE Default Year CreditLimit_sum CreditLimit_mean CreditLimit_max  \\\n",
       "0     NaN     NaN  NaN             NaN              NaN             NaN   \n",
       "1     NaN     NaN  NaN             NaN              NaN             NaN   \n",
       "2     NaN     NaN  NaN             NaN              NaN             NaN   \n",
       "3     NaN     NaN  NaN             NaN              NaN             NaN   \n",
       "4     NaN     NaN  NaN             NaN              NaN             NaN   \n",
       "\n",
       "  CreditLimit_min CreditLimit_std CurrentTotalBalance_sum  \\\n",
       "0             NaN             NaN                     NaN   \n",
       "1             NaN             NaN                     NaN   \n",
       "2             NaN             NaN                     NaN   \n",
       "3             NaN             NaN                     NaN   \n",
       "4             NaN             NaN                     NaN   \n",
       "\n",
       "  CurrentTotalBalance_mean          ...          COUNT_XFLG-N_month_3  \\\n",
       "0                      NaN          ...                           NaN   \n",
       "1                      NaN          ...                           NaN   \n",
       "2                      NaN          ...                           NaN   \n",
       "3                      NaN          ...                           NaN   \n",
       "4                      NaN          ...                           NaN   \n",
       "\n",
       "  COUNT_XFLG-N_month_4 COUNT_XFLG-N_month_5 COUNT_XFLG-N_month_6  \\\n",
       "0                  NaN                  NaN                  NaN   \n",
       "1                  NaN                  NaN                  NaN   \n",
       "2                  NaN                  NaN                  NaN   \n",
       "3                  NaN                  NaN                  NaN   \n",
       "4                  NaN                  NaN                  NaN   \n",
       "\n",
       "  COUNT_XFLG-N_month_7 COUNT_XFLG-N_month_8 COUNT_XFLG-N_month_9  \\\n",
       "0                  NaN                  NaN                  NaN   \n",
       "1                  NaN                  NaN                  NaN   \n",
       "2                  NaN                  NaN                  NaN   \n",
       "3                  NaN                  NaN                  NaN   \n",
       "4                  NaN                  NaN                  NaN   \n",
       "\n",
       "  COUNT_XFLG-N_month_10 COUNT_XFLG-N_month_11 COUNT_XFLG-N_month_12  \n",
       "0                   NaN                   NaN                   NaN  \n",
       "1                   NaN                   NaN                   NaN  \n",
       "2                   NaN                   NaN                   NaN  \n",
       "3                   NaN                   NaN                   NaN  \n",
       "4                   NaN                   NaN                   NaN  \n",
       "\n",
       "[5 rows x 129 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pd.DataFrame(index=range(0,11900),columns=[['ID_CPTE',\n",
    "                                                      'Default',\n",
    "                                                      'Year',\n",
    "                                                      'CreditLimit_sum','CreditLimit_mean','CreditLimit_max','CreditLimit_min','CreditLimit_std',\n",
    "                                                      'CurrentTotalBalance_sum','CurrentTotalBalance_mean','CurrentTotalBalance_max','CurrentTotalBalance_min','CurrentTotalBalance_std',\n",
    "                                                      'CashBalance_sum','CashBalance_mean','CashBalance_max','CashBalance_min','CashBalance_std',\n",
    "                                                      'DelqCycle_sum','DelqCycle_mean','DelqCycle_max','DelqCycle_min','DelqCycle_std',\n",
    "                                                      'TRANSACTION_AMT_PAID_sum','TRANSACTION_AMT_PAID_mean','TRANSACTION_AMT_PAID_max','TRANSACTION_AMT_PAID_min','TRANSACTION_AMT_PAID_std',\n",
    "                                                      'COUNT_XFLG-Q_sum','COUNT_XFLG-Q_mean','COUNT_XFLG-Q_max','COUNT_XFLG-Q_min','COUNT_XFLG-Q_std',\n",
    "                                                      'CreditLimit_month_1',\n",
    "                                                      'CreditLimit_month_2',\n",
    "                                                      'CreditLimit_month_3',\n",
    "                                                      'CreditLimit_month_4',\n",
    "                                                      'CreditLimit_month_5',\n",
    "                                                      'CreditLimit_month_6',\n",
    "                                                      'CreditLimit_month_7',\n",
    "                                                      'CreditLimit_month_8',\n",
    "                                                      'CreditLimit_month_9',\n",
    "                                                      'CreditLimit_month_10',\n",
    "                                                      'CreditLimit_month_11',\n",
    "                                                      'CreditLimit_month_12',\n",
    "                                                      #'CurrentTotalBalance_month_13',\n",
    "                                                      'CurrentTotalBalance_month_1',\n",
    "                                                      'CurrentTotalBalance_month_2',\n",
    "                                                      'CurrentTotalBalance_month_3',\n",
    "                                                      'CurrentTotalBalance_month_4',\n",
    "                                                      'CurrentTotalBalance_month_5',\n",
    "                                                      'CurrentTotalBalance_month_6',\n",
    "                                                      'CurrentTotalBalance_month_7',\n",
    "                                                      'CurrentTotalBalance_month_8',\n",
    "                                                      'CurrentTotalBalance_month_9',\n",
    "                                                      'CurrentTotalBalance_month_10',\n",
    "                                                      'CurrentTotalBalance_month_11',\n",
    "                                                      'CurrentTotalBalance_month_12',\n",
    "                                                      #'CurrentTotalBalance_month_13',\n",
    "                                                      'CashBalance_month_1',\n",
    "                                                      'CashBalance_month_2',\n",
    "                                                      'CashBalance_month_3',\n",
    "                                                      'CashBalance_month_4',\n",
    "                                                      'CashBalance_month_5',\n",
    "                                                      'CashBalance_month_6',\n",
    "                                                      'CashBalance_month_7',\n",
    "                                                      'CashBalance_month_8',\n",
    "                                                      'CashBalance_month_9',\n",
    "                                                      'CashBalance_month_10',\n",
    "                                                      'CashBalance_month_11',\n",
    "                                                      'CashBalance_month_12',\n",
    "                                                      #'CashBalance_month_13',\n",
    "                                                      'DelqCycle_month_1',\n",
    "                                                      'DelqCycle_month_2',\n",
    "                                                      'DelqCycle_month_3',\n",
    "                                                      'DelqCycle_month_4',\n",
    "                                                      'DelqCycle_month_5',\n",
    "                                                      'DelqCycle_month_6',\n",
    "                                                      'DelqCycle_month_7',\n",
    "                                                      'DelqCycle_month_8',\n",
    "                                                      'DelqCycle_month_9',\n",
    "                                                      'DelqCycle_month_10',\n",
    "                                                      'DelqCycle_month_11',\n",
    "                                                      'DelqCycle_month_12',\n",
    "                                                      #'DelqCycle_month_13',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_1',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_2',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_3',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_4',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_5',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_6',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_7',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_8',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_9',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_10',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_11',\n",
    "                                                      'TRANSACTION_AMT_PAID_month_12',\n",
    "                                                      #'TRANSACTION_AMT_PAID_month_13',\n",
    "                                                      'TRANSACTION_AMT_REFUNDED_month_1',\n",
    "                                                      'TRANSACTION_AMT_REFUNDED_month_2',\n",
    "                                                      'TRANSACTION_AMT_REFUNDED_month_3',\n",
    "                                                      'TRANSACTION_AMT_REFUNDED_month_4',\n",
    "                                                      'TRANSACTION_AMT_REFUNDED_month_5',\n",
    "                                                      'TRANSACTION_AMT_REFUNDED_month_6',\n",
    "                                                       'TRANSACTION_AMT_REFUNDED_month_7',\n",
    "                                                       'TRANSACTION_AMT_REFUNDED_month_8',\n",
    "                                                       'TRANSACTION_AMT_REFUNDED_month_9',\n",
    "                                                       'TRANSACTION_AMT_REFUNDED_month_10',\n",
    "                                                       'TRANSACTION_AMT_REFUNDED_month_11',\n",
    "                                                       'TRANSACTION_AMT_REFUNDED_month_12',\n",
    "                                                       #'TRANSACTION_AMT_REFUNDED_month_13',\n",
    "                                                       'COUNT_XFLG-Q_month_1',\n",
    "                                                       'COUNT_XFLG-Q_month_2',\n",
    "                                                       'COUNT_XFLG-Q_month_3',\n",
    "                                                       'COUNT_XFLG-Q_month_4',\n",
    "                                                       'COUNT_XFLG-Q_month_5',\n",
    "                                                       'COUNT_XFLG-Q_month_6',\n",
    "                                                       'COUNT_XFLG-Q_month_7',\n",
    "                                                       'COUNT_XFLG-Q_month_8',\n",
    "                                                       'COUNT_XFLG-Q_month_9',\n",
    "                                                       'COUNT_XFLG-Q_month_10',\n",
    "                                                       'COUNT_XFLG-Q_month_11',\n",
    "                                                       'COUNT_XFLG-Q_month_12',\n",
    "                                                       #'COUNT_XFLG-Q_month_13',\n",
    "                                                       'COUNT_XFLG-N_month_1',\n",
    "                                                       'COUNT_XFLG-N_month_2',\n",
    "                                                       'COUNT_XFLG-N_month_3',\n",
    "                                                       'COUNT_XFLG-N_month_4',\n",
    "                                                       'COUNT_XFLG-N_month_5',\n",
    "                                                       'COUNT_XFLG-N_month_6',\n",
    "                                                       'COUNT_XFLG-N_month_7',\n",
    "                                                       'COUNT_XFLG-N_month_8',\n",
    "                                                       'COUNT_XFLG-N_month_9',\n",
    "                                                       'COUNT_XFLG-N_month_10',\n",
    "                                                       'COUNT_XFLG-N_month_11',\n",
    "                                                       'COUNT_XFLG-N_month_12',\n",
    "                                                       #'COUNT_XFLG-N_month_13',\n",
    "                                                      ]])\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_CPTE</th>\n",
       "      <th>PERIODID_MY</th>\n",
       "      <th>StatementDate</th>\n",
       "      <th>CurrentTotalBalance</th>\n",
       "      <th>CashBalance</th>\n",
       "      <th>CreditLimit</th>\n",
       "      <th>DelqCycle</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th>CPTE_PERIOD_ID</th>\n",
       "      <th>TRANSACTION_AMT_PAID</th>\n",
       "      <th>COUNT_XFLG-Q</th>\n",
       "      <th>TRANSACTION_AMT_REFUNDED</th>\n",
       "      <th>COUNT_XFLG-N</th>\n",
       "      <th>Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29787</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>1262.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-01</td>\n",
       "      <td>43462064_2012-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29790</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>2012-02-03</td>\n",
       "      <td>1258.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-02</td>\n",
       "      <td>43462064_2012-02</td>\n",
       "      <td>144.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29786</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>2012-02-29</td>\n",
       "      <td>1112.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-03</td>\n",
       "      <td>43462064_2012-03</td>\n",
       "      <td>85.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29785</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>2012-04-06</td>\n",
       "      <td>1089.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-04</td>\n",
       "      <td>43462064_2012-04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29784</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>1084.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-05</td>\n",
       "      <td>43462064_2012-05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29777</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-06-01</td>\n",
       "      <td>2012-06-01</td>\n",
       "      <td>1101.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2012-06</td>\n",
       "      <td>43462064_2012-06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29783</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-07-01</td>\n",
       "      <td>2012-07-03</td>\n",
       "      <td>1166.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2012-07</td>\n",
       "      <td>43462064_2012-07</td>\n",
       "      <td>116.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29782</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>1055.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2012-08</td>\n",
       "      <td>43462064_2012-08</td>\n",
       "      <td>102.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29781</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>2012-09-04</td>\n",
       "      <td>1071.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-09</td>\n",
       "      <td>43462064_2012-09</td>\n",
       "      <td>63.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29778</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>2012-10-06</td>\n",
       "      <td>1198.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-10</td>\n",
       "      <td>43462064_2012-10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29780</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>1230.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2012-11</td>\n",
       "      <td>43462064_2012-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29779</th>\n",
       "      <td>43462064.0</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>1180.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>43462064_2012-12</td>\n",
       "      <td>108.07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID_CPTE PERIODID_MY StatementDate  CurrentTotalBalance  CashBalance  \\\n",
       "29787  43462064.0  2012-01-01    2012-01-04              1262.10          0.0   \n",
       "29790  43462064.0  2012-02-01    2012-02-03              1258.68          0.0   \n",
       "29786  43462064.0  2012-03-01    2012-02-29              1112.00          0.0   \n",
       "29785  43462064.0  2012-04-01    2012-04-06              1089.92          0.0   \n",
       "29784  43462064.0  2012-05-01    2012-05-01              1084.26          0.0   \n",
       "29777  43462064.0  2012-06-01    2012-06-01              1101.60          0.0   \n",
       "29783  43462064.0  2012-07-01    2012-07-03              1166.00          0.0   \n",
       "29782  43462064.0  2012-08-01    2012-08-01              1055.25          0.0   \n",
       "29781  43462064.0  2012-09-01    2012-09-04              1071.66          0.0   \n",
       "29778  43462064.0  2012-10-01    2012-10-06              1198.50          0.0   \n",
       "29780  43462064.0  2012-11-01    2012-11-03              1230.85          0.0   \n",
       "29779  43462064.0  2012-12-01    2012-12-01              1180.84          0.0   \n",
       "\n",
       "       CreditLimit  DelqCycle YEAR_MONTH    CPTE_PERIOD_ID  \\\n",
       "29787       1000.0        0.0    2012-01  43462064_2012-01   \n",
       "29790       1000.0        1.0    2012-02  43462064_2012-02   \n",
       "29786       1000.0        0.0    2012-03  43462064_2012-03   \n",
       "29785       1000.0        0.0    2012-04  43462064_2012-04   \n",
       "29784       1000.0        1.0    2012-05  43462064_2012-05   \n",
       "29777       1000.0        2.0    2012-06  43462064_2012-06   \n",
       "29783       1000.0        3.0    2012-07  43462064_2012-07   \n",
       "29782       1000.0        2.0    2012-08  43462064_2012-08   \n",
       "29781       1000.0        1.0    2012-09  43462064_2012-09   \n",
       "29778       1000.0        1.0    2012-10  43462064_2012-10   \n",
       "29780       1000.0        2.0    2012-11  43462064_2012-11   \n",
       "29779       1000.0        1.0    2012-12  43462064_2012-12   \n",
       "\n",
       "       TRANSACTION_AMT_PAID  COUNT_XFLG-Q  TRANSACTION_AMT_REFUNDED  \\\n",
       "29787                  0.00           0.0                       0.0   \n",
       "29790                144.20           1.0                       0.0   \n",
       "29786                 85.00           1.0                       0.0   \n",
       "29785                  0.00           0.0                       0.0   \n",
       "29784                  0.00           0.0                       0.0   \n",
       "29777                  0.00           0.0                       0.0   \n",
       "29783                116.60           1.0                       0.0   \n",
       "29782                102.00           1.0                       0.0   \n",
       "29781                 63.00           1.0                       0.0   \n",
       "29778                  0.00           0.0                       0.0   \n",
       "29780                  0.00           0.0                       0.0   \n",
       "29779                108.07           1.0                       0.0   \n",
       "\n",
       "       COUNT_XFLG-N  Default  \n",
       "29787           0.0        1  \n",
       "29790           0.0        1  \n",
       "29786           0.0        1  \n",
       "29785           0.0        1  \n",
       "29784           0.0        1  \n",
       "29777           0.0        1  \n",
       "29783           0.0        1  \n",
       "29782           0.0        1  \n",
       "29781           0.0        1  \n",
       "29778           0.0        1  \n",
       "29780           0.0        1  \n",
       "29779           0.0        1  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1.ID_CPTE == 43462064].sort_values(['PERIODID_MY']).tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "i = 0\n",
    "for x in df1.ID_CPTE.unique():\n",
    "    month = 12\n",
    "    \n",
    "    df2 = df1[df1.ID_CPTE == x].sort_values(['PERIODID_MY']).tail(month)\n",
    "    df_final.iloc[i]['ID_CPTE'] = df2.iloc[5]['ID_CPTE']\n",
    "    df_final.iloc[i]['Default'] = df2.iloc[5]['Default']\n",
    "    df_final.iloc[i]['Year'] = df2.iloc[5]['PERIODID_MY'][:4]\n",
    "    \n",
    "    \n",
    "    df_final.iloc[i]['ID_CPTE'] = df2.iloc[5]['ID_CPTE']\n",
    "    df_final.iloc[i]['Default'] = df2.iloc[5]['Default']\n",
    "    df_final.iloc[i]['Year'] = df2.iloc[5]['PERIODID_MY'][:4]\n",
    "    df_final.iloc[i]['CreditLimit_sum'] = df2['CreditLimit'].sum()\n",
    "    df_final.iloc[i]['CreditLimit_mean'] = df2['CreditLimit'].mean()\n",
    "    df_final.iloc[i]['CreditLimit_max'] = df2['CreditLimit'].max()\n",
    "    df_final.iloc[i]['CreditLimit_min'] = df2['CreditLimit'].min()\n",
    "    df_final.iloc[i]['CreditLimit_std'] = df2['CreditLimit'].std()\n",
    "    df_final.iloc[i]['CurrentTotalBalance_sum'] = df2['CurrentTotalBalance'].sum()\n",
    "    df_final.iloc[i]['CurrentTotalBalance_mean'] = df2['CurrentTotalBalance'].mean()\n",
    "    df_final.iloc[i]['CurrentTotalBalance_max'] = df2['CurrentTotalBalance'].max()\n",
    "    df_final.iloc[i]['CurrentTotalBalance_min'] = df2['CurrentTotalBalance'].min()\n",
    "    df_final.iloc[i]['CurrentTotalBalance_std'] = df2['CurrentTotalBalance'].std()\n",
    "    df_final.iloc[i]['CashBalance_sum'] = df2['CashBalance'].sum()\n",
    "    df_final.iloc[i]['CashBalance_mean'] = df2['CashBalance'].mean()\n",
    "    df_final.iloc[i]['CashBalance_max'] = df2['CashBalance'].max()\n",
    "    df_final.iloc[i]['CashBalance_min'] = df2['CashBalance'].min()\n",
    "    df_final.iloc[i]['CashBalance_std'] = df2['CashBalance'].std()\n",
    "    df_final.iloc[i]['DelqCycle_sum'] = df2['DelqCycle'].sum()\n",
    "    df_final.iloc[i]['DelqCycle_mean'] = df2['DelqCycle'].mean()\n",
    "    df_final.iloc[i]['DelqCycle_max'] = df2['DelqCycle'].max()\n",
    "    df_final.iloc[i]['DelqCycle_min'] = df2['DelqCycle'].min()\n",
    "    df_final.iloc[i]['DelqCycle_std'] = df2['DelqCycle'].std()\n",
    "    df_final.iloc[i]['TRANSACTION_AMT_PAID_sum'] = df2['TRANSACTION_AMT_PAID'].sum()\n",
    "    df_final.iloc[i]['TRANSACTION_AMT_PAID_mean'] = df2['TRANSACTION_AMT_PAID'].mean()\n",
    "    df_final.iloc[i]['TRANSACTION_AMT_PAID_max'] = df2['TRANSACTION_AMT_PAID'].max()\n",
    "    df_final.iloc[i]['TRANSACTION_AMT_PAID_min'] = df2['TRANSACTION_AMT_PAID'].min()\n",
    "    df_final.iloc[i]['TRANSACTION_AMT_PAID_std'] = df2['TRANSACTION_AMT_PAID'].std()\n",
    "    df_final.iloc[i]['COUNT_XFLG-Q_sum'] = df2['COUNT_XFLG-Q'].sum()\n",
    "    df_final.iloc[i]['COUNT_XFLG-Q_mean'] = df2['COUNT_XFLG-Q'].mean()\n",
    "    df_final.iloc[i]['COUNT_XFLG-Q_max'] = df2['COUNT_XFLG-Q'].max()\n",
    "    df_final.iloc[i]['COUNT_XFLG-Q_min'] = df2['COUNT_XFLG-Q'].min()\n",
    "    df_final.iloc[i]['COUNT_XFLG-Q_std'] = df2['COUNT_XFLG-Q'].std()\n",
    "    \n",
    "    tempCol = ''\n",
    "    var0 = []\n",
    "    var1 = []\n",
    "    var2 = []\n",
    "    var3 = []\n",
    "    var4 = []\n",
    "    var5 = []\n",
    "    var6 = []\n",
    "    var7 = []\n",
    "    \n",
    "    \n",
    "    \n",
    "    for xx in range(0,month):        \n",
    "        var0.append(df2.iloc[xx]['CreditLimit']) \n",
    "        tempCol = 'CreditLimit_month_'+str(xx+1)\n",
    "        df_final.iloc[i][tempCol] = var0[xx] \n",
    "    for xx in range(0,month):        \n",
    "        var1.append(df2.iloc[xx]['CurrentTotalBalance']) \n",
    "        tempCol = 'CurrentTotalBalance_month_'+str(xx+1)\n",
    "        df_final.iloc[i][tempCol] = var1[xx]    \n",
    "    for xx in range(0,month):\n",
    "        var2.append(df2.iloc[xx]['CashBalance'])\n",
    "        tempCol = 'CashBalance_month_'+str(xx+1)\n",
    "        df_final.iloc[i][tempCol] = var2[xx]     \n",
    "    for xx in range(0,month):\n",
    "        var3.append(df2.iloc[xx]['DelqCycle'])\n",
    "        tempCol = 'DelqCycle_month_'+str(xx+1)\n",
    "        df_final.iloc[i][tempCol] = var3[xx]        \n",
    "    for xx in range(0,month):\n",
    "        var4.append(df2.iloc[xx]['TRANSACTION_AMT_PAID'])\n",
    "        tempCol = 'TRANSACTION_AMT_PAID_month_'+str(xx+1)\n",
    "        df_final.iloc[i][tempCol] = var4[xx]\n",
    "    for xx in range(0,month):\n",
    "        var5.append(df2.iloc[xx]['TRANSACTION_AMT_REFUNDED'])\n",
    "        tempCol = 'TRANSACTION_AMT_REFUNDED_month_'+str(xx+1)\n",
    "        df_final.iloc[i][tempCol] = var5[xx]\n",
    "    for xx in range(0,month):\n",
    "        var6.append(df2.iloc[xx]['COUNT_XFLG-Q'])\n",
    "        tempCol = 'COUNT_XFLG-Q_month_'+str(xx+1)\n",
    "        df_final.iloc[i][tempCol] = var6[xx]\n",
    "    for xx in range(0,month):\n",
    "        var7.append(df2.iloc[xx]['COUNT_XFLG-N'])\n",
    "        tempCol = 'COUNT_XFLG-N_month_'+str(xx+1)\n",
    "        df_final.iloc[i][tempCol] = var7[xx]\n",
    "    \n",
    "    print(i)    \n",
    "    i += 1\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('test_df_checkpoint.csv', sep=',', index = False)\n",
    "df = pd.read_csv('test_df_checkpoint.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from math import log\n",
    "\n",
    "def selcols(prefix, a=1, b=13):\n",
    "    return [prefix+str(i) for i in np.arange(a,b+1)]\n",
    "\n",
    "def encode_text_dummy(df, name):\n",
    "    dummies = pd.get_dummies(df[name])\n",
    "    for x in dummies.columns:\n",
    "        dummy_name = \"{}-{}\".format(name, x)\n",
    "        df[dummy_name] = dummies[x]\n",
    "        print(dummy_name)\n",
    "    df.drop(name, axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_CPTE</th>\n",
       "      <th>Default</th>\n",
       "      <th>Year</th>\n",
       "      <th>CreditLimit_sum</th>\n",
       "      <th>CreditLimit_mean</th>\n",
       "      <th>CreditLimit_max</th>\n",
       "      <th>CreditLimit_min</th>\n",
       "      <th>CreditLimit_std</th>\n",
       "      <th>CurrentTotalBalance_sum</th>\n",
       "      <th>CurrentTotalBalance_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>TRANSACTION_AMT_PAID_min_log</th>\n",
       "      <th>TRANSACTION_AMT_PAID_std_log</th>\n",
       "      <th>DelqCycle_mean_log</th>\n",
       "      <th>DelqCycle_max_log</th>\n",
       "      <th>DelqCycle_min_log</th>\n",
       "      <th>DelqCycle_Mean*Max*Min</th>\n",
       "      <th>DelqCycle6Month_pond</th>\n",
       "      <th>CashBalance_rel_DelqCycle_6month_mean</th>\n",
       "      <th>CreditLimit_sum_rell</th>\n",
       "      <th>stdMultipli</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID_CPTE, Default, Year, CreditLimit_sum, CreditLimit_mean, CreditLimit_max, CreditLimit_min, CreditLimit_std, CurrentTotalBalance_sum, CurrentTotalBalance_mean, CurrentTotalBalance_max, CurrentTotalBalance_min, CurrentTotalBalance_std, CashBalance_sum, CashBalance_mean, CashBalance_max, CashBalance_min, CashBalance_std, DelqCycle_sum, DelqCycle_mean, DelqCycle_max, DelqCycle_min, DelqCycle_std, TRANSACTION_AMT_PAID_sum, TRANSACTION_AMT_PAID_mean, TRANSACTION_AMT_PAID_max, TRANSACTION_AMT_PAID_min, TRANSACTION_AMT_PAID_std, COUNT_XFLG-Q_sum, COUNT_XFLG-Q_mean, COUNT_XFLG-Q_max, COUNT_XFLG-Q_min, COUNT_XFLG-Q_std, CreditLimit_month_1, CreditLimit_month_2, CreditLimit_month_3, CreditLimit_month_4, CreditLimit_month_5, CreditLimit_month_6, CreditLimit_month_7, CreditLimit_month_8, CreditLimit_month_9, CreditLimit_month_10, CreditLimit_month_11, CreditLimit_month_12, CurrentTotalBalance_month_1, CurrentTotalBalance_month_2, CurrentTotalBalance_month_3, CurrentTotalBalance_month_4, CurrentTotalBalance_month_5, CurrentTotalBalance_month_6, CurrentTotalBalance_month_7, CurrentTotalBalance_month_8, CurrentTotalBalance_month_9, CurrentTotalBalance_month_10, CurrentTotalBalance_month_11, CurrentTotalBalance_month_12, CashBalance_month_1, CashBalance_month_2, CashBalance_month_3, CashBalance_month_4, CashBalance_month_5, CashBalance_month_6, CashBalance_month_7, CashBalance_month_8, CashBalance_month_9, CashBalance_month_10, CashBalance_month_11, CashBalance_month_12, DelqCycle_month_1, DelqCycle_month_2, DelqCycle_month_3, DelqCycle_month_4, DelqCycle_month_5, DelqCycle_month_6, DelqCycle_month_7, DelqCycle_month_8, DelqCycle_month_9, DelqCycle_month_10, DelqCycle_month_11, DelqCycle_month_12, TRANSACTION_AMT_PAID_month_1, TRANSACTION_AMT_PAID_month_2, TRANSACTION_AMT_PAID_month_3, TRANSACTION_AMT_PAID_month_4, TRANSACTION_AMT_PAID_month_5, TRANSACTION_AMT_PAID_month_6, TRANSACTION_AMT_PAID_month_7, TRANSACTION_AMT_PAID_month_8, TRANSACTION_AMT_PAID_month_9, TRANSACTION_AMT_PAID_month_10, TRANSACTION_AMT_PAID_month_11, TRANSACTION_AMT_PAID_month_12, TRANSACTION_AMT_REFUNDED_month_1, TRANSACTION_AMT_REFUNDED_month_2, TRANSACTION_AMT_REFUNDED_month_3, TRANSACTION_AMT_REFUNDED_month_4, TRANSACTION_AMT_REFUNDED_month_5, TRANSACTION_AMT_REFUNDED_month_6, TRANSACTION_AMT_REFUNDED_month_7, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 304 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CreditLimit_log'] = df['CreditLimit_mean'].astype(float).apply(lambda x: log(x+1))\n",
    "df['CreditLimit_log2'] = df['CreditLimit_mean'].apply(lambda x: log(x+1)**2)\n",
    "df['CreditLimit_log3'] = df['CreditLimit_mean'].apply(lambda x: log(x+1)**3)\n",
    "df['CreditLimit_cat'] = pd.cut(df['CreditLimit_mean'], range(0, 120000, 5000), right=False, include_lowest=True)\n",
    "\n",
    "df['CreditLimit_max_log'] = df['CreditLimit_max'].astype(float).apply(lambda x: log(x+1))\n",
    "df['CreditLimit_min_log'] = df['CreditLimit_min'].astype(float).apply(lambda x: log(x+1))\n",
    "df['CreditLimit_std_log'] = df['CreditLimit_std'].astype(float).apply(lambda x: log(x+1))\n",
    "\n",
    "df['CashBalance_log'] = df['CashBalance_mean'].astype(float).apply(lambda x: log(x+1))\n",
    "df['CashBalance_max_log'] = df['CashBalance_max'].astype(float).apply(lambda x: log(x+1))\n",
    "df['CashBalance_min_log'] = df['CashBalance_min'].astype(float).apply(lambda x: log(x+1))\n",
    "df['CashBalance_std_log'] = df['CashBalance_std'].astype(float).apply(lambda x: log(x+1))\n",
    "\n",
    "\n",
    "for i in np.arange(1,13):   \n",
    "    df['CreditLimit_reached_month_'+str(i)] = np.where(df['CurrentTotalBalance_month_'+str(i)] > df['CreditLimit_month_'+str(i)], 1, 0)\n",
    "    \n",
    "        \n",
    "df.isnull().any().any()\n",
    "\n",
    "# average bill\n",
    "df['CurrentTotalBalance_mean_log'] = df['CurrentTotalBalance_mean'].apply(lambda x: log(x+1) if x>0 else 0)\n",
    "df['CurrentTotalBalance_sum_log'] = df['CurrentTotalBalance_sum'].apply(lambda x: log(x+1) if x>0 else 0)\n",
    "df['CurrentTotalBalance_min_log'] = df['CurrentTotalBalance_min'].apply(lambda x: log(x+1) if x>0 else 0)\n",
    "df['CurrentTotalBalance_max_log'] = df['CurrentTotalBalance_max'].apply(lambda x: log(x+1) if x>0 else 0)\n",
    "\n",
    "# bill sign as a separate feature\n",
    "for i in np.arange(1,13):\n",
    "    df['CurrentTotalBalance_sign_month_'+str(i)] = df['CurrentTotalBalance_month_'+str(i)].apply(lambda x: float(x>0))\n",
    "    \n",
    "# bill log as a separate feature (0 if bill is negative)\n",
    "for i in np.arange(1,13):\n",
    "    df['CurrentTotalBalance_log_month_'+str(i)] = df['CurrentTotalBalance_month_'+str(i)].apply(lambda x: log(x+1) if x>0 else 0)\n",
    "# bill log to the power of 2\n",
    "for i in np.arange(1,13):\n",
    "    df['CurrentTotalBalance_log2_month_'+str(i)] = df['CurrentTotalBalance_month_'+str(i)].apply(lambda x: log(x+1)**2 if x>0 else 0)\n",
    "for i in np.arange(1,13):\n",
    "    df['CurrentTotalBalance_log3_month_'+str(i)] = df['CurrentTotalBalance_month_'+str(i)].apply(lambda x: log(x+1)**3 if x>0 else 0)\n",
    "#bill amounts relative to the limit\n",
    "for i in np.arange(1,13):\n",
    "    df['CurrentTotalBalance_rel_month_'+str(i)] = df['CurrentTotalBalance_month_'+str(i)]/(df['CreditLimit_mean']+0.00001)\n",
    "    \n",
    "for i in np.arange(1,13):\n",
    "    df['CashBalance_rel_CreditLimit_month_'+str(i)] = df['CashBalance_month_'+str(i)]/(df['CreditLimit_mean']+0.00001)\n",
    "    \n",
    "for i in np.arange(1,13):\n",
    "    df['CashBalance_rel_DelqCycle_month_'+str(i)] = df['CashBalance_month_'+str(i)]/(df['CreditLimit_mean']+0.00001)\n",
    "  \n",
    "df['CashBalance_rel_DelqCycle_mean'] = df['CashBalance_mean']/(df['DelqCycle_mean']+0.00001)\n",
    "df['CashBalance_mul_DelqCycle_mean'] = df['CashBalance_mean']*df['DelqCycle_mean']\n",
    "\n",
    "df['CashBalance_mul_log_DelqCycle_mean'] = df['CashBalance_mul_DelqCycle_mean'].apply(lambda x: log(x+1) if x>0 else 0)\n",
    "df['CashBalance_rel_log_DelqCycle_mean'] = df['CashBalance_rel_DelqCycle_mean'].apply(lambda x: log(x+1) if x>0 else 0)\n",
    "\n",
    "df[df.isnull().any(axis=1)]\n",
    "\n",
    "#amounts relative to the average payment\n",
    "for i in np.arange(1,13):\n",
    "    df['TRANSACTION_relAMT_PAID_month_'+str(i)] = df['TRANSACTION_AMT_PAID_month_'+str(i)]/df['TRANSACTION_AMT_PAID_mean']\n",
    "for i in np.arange(1,13):\n",
    "    df['TRANSACTION_logAMT_PAID_month_'+str(i)] = df['TRANSACTION_AMT_PAID_month_'+str(i)].apply(lambda x: log(x+1))\n",
    "for i in np.arange(1,13):\n",
    "    df['TRANSACTION_AMT_PAID_perQ_month_'+str(i)] = df['TRANSACTION_AMT_PAID_month_'+str(i)]/df['COUNT_XFLG-Q_month_'+str(i)]    \n",
    "for i in np.arange(1,13):\n",
    "    df['TRANSACTION_AMT_PAID_rel_CreditLimit_month_'+str(i)] = df['TRANSACTION_AMT_PAID_month_'+str(i)]/df['CreditLimit_mean']\n",
    "    \n",
    "for i in np.arange(1,13):\n",
    "    df['TRANSACTION_AMT_PAID_rel_CreditLimit_month_'+str(i)] = df['TRANSACTION_AMT_PAID_rel_CreditLimit_month_'+str(i)].fillna(0)\n",
    "    df['TRANSACTION_AMT_PAID_perQ_month_'+str(i)] = df['TRANSACTION_AMT_PAID_perQ_month_'+str(i)].fillna(0)\n",
    "    df['TRANSACTION_logAMT_PAID_month_'+str(i)] = df['TRANSACTION_logAMT_PAID_month_'+str(i)].fillna(0)\n",
    "    df['TRANSACTION_relAMT_PAID_month_'+str(i)] = df['TRANSACTION_relAMT_PAID_month_'+str(i)].fillna(0)\n",
    "    \n",
    "# log of average\n",
    "df['TRANSACTION_AMT_PAID_mean_log'] = df['TRANSACTION_AMT_PAID_mean'].apply(lambda x: log(x+1))\n",
    "df['TRANSACTION_AMT_PAID_max_log'] = df['TRANSACTION_AMT_PAID_max'].apply(lambda x: log(x+1))\n",
    "df['TRANSACTION_AMT_PAID_min_log'] = df['TRANSACTION_AMT_PAID_min'].apply(lambda x: log(x+1))\n",
    "df['TRANSACTION_AMT_PAID_std_log'] = df['TRANSACTION_AMT_PAID_std'].apply(lambda x: log(x+1))\n",
    "\n",
    "\n",
    "df['DelqCycle_mean_log'] = df['DelqCycle_mean'].apply(lambda x: log(x+1))\n",
    "df['DelqCycle_max_log'] = df['DelqCycle_max'].apply(lambda x: log(x+1))\n",
    "df['DelqCycle_min_log'] = df['DelqCycle_max'].apply(lambda x: log(x+1))\n",
    "\n",
    "df['DelqCycle_Mean*Max*Min'] = df['DelqCycle_mean']*df['DelqCycle_max']*(df['DelqCycle_min']+1)\n",
    "\n",
    "df['DelqCycle6Month_pond'] = df['DelqCycle_month_12']*1 + \\\n",
    "                            df['DelqCycle_month_11']*0.85 + \\\n",
    "                            df['DelqCycle_month_10']*0.7 + \\\n",
    "                            df['DelqCycle_month_9']*0.55 + \\\n",
    "                            df['DelqCycle_month_8']*0.40 + \\\n",
    "                            df['DelqCycle_month_7']*0.25\n",
    "\n",
    "df['CashBalance_rel_DelqCycle_6month_mean'] = df['CurrentTotalBalance_mean']/(df['TRANSACTION_AMT_PAID_mean']+1)*df['DelqCycle6Month_pond']  \n",
    "df['CreditLimit_sum_rell'] = df['CreditLimit_sum']/((df['CurrentTotalBalance_mean']/df['CreditLimit_mean']+0.0001)+0.0001)\n",
    "\n",
    "df['stdMultipli'] = df['COUNT_XFLG-Q_mean']*df['COUNT_XFLG-Q_std']*df['TRANSACTION_AMT_PAID_std']*(df['DelqCycle_std']+0.01)*(df['CurrentTotalBalance_mean']/df['CreditLimit_mean'])\n",
    "\n",
    "\n",
    "df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year-2012\n",
      "Year-2013\n",
      "Year-2014\n",
      "Year-2015\n",
      "Year-2016\n",
      "CreditLimit_cat-[0, 5000)\n",
      "CreditLimit_cat-[5000, 10000)\n",
      "CreditLimit_cat-[10000, 15000)\n",
      "CreditLimit_cat-[15000, 20000)\n",
      "CreditLimit_cat-[20000, 25000)\n",
      "CreditLimit_cat-[25000, 30000)\n",
      "CreditLimit_cat-[30000, 35000)\n",
      "CreditLimit_cat-[35000, 40000)\n",
      "CreditLimit_cat-[40000, 45000)\n",
      "CreditLimit_cat-[45000, 50000)\n",
      "CreditLimit_cat-[50000, 55000)\n",
      "CreditLimit_cat-[55000, 60000)\n",
      "CreditLimit_cat-[60000, 65000)\n",
      "CreditLimit_cat-[65000, 70000)\n",
      "CreditLimit_cat-[70000, 75000)\n",
      "CreditLimit_cat-[75000, 80000)\n",
      "CreditLimit_cat-[80000, 85000)\n",
      "CreditLimit_cat-[85000, 90000)\n",
      "CreditLimit_cat-[90000, 95000)\n",
      "CreditLimit_cat-[95000, 100000)\n",
      "CreditLimit_cat-[100000, 105000)\n",
      "CreditLimit_cat-[105000, 110000)\n",
      "CreditLimit_cat-[110000, 115000)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_CPTE</th>\n",
       "      <th>Default</th>\n",
       "      <th>CreditLimit_sum</th>\n",
       "      <th>CreditLimit_mean</th>\n",
       "      <th>CreditLimit_max</th>\n",
       "      <th>CreditLimit_min</th>\n",
       "      <th>CreditLimit_std</th>\n",
       "      <th>CurrentTotalBalance_sum</th>\n",
       "      <th>CurrentTotalBalance_mean</th>\n",
       "      <th>CurrentTotalBalance_max</th>\n",
       "      <th>...</th>\n",
       "      <th>CreditLimit_cat-[65000, 70000)</th>\n",
       "      <th>CreditLimit_cat-[70000, 75000)</th>\n",
       "      <th>CreditLimit_cat-[75000, 80000)</th>\n",
       "      <th>CreditLimit_cat-[80000, 85000)</th>\n",
       "      <th>CreditLimit_cat-[85000, 90000)</th>\n",
       "      <th>CreditLimit_cat-[90000, 95000)</th>\n",
       "      <th>CreditLimit_cat-[95000, 100000)</th>\n",
       "      <th>CreditLimit_cat-[100000, 105000)</th>\n",
       "      <th>CreditLimit_cat-[105000, 110000)</th>\n",
       "      <th>CreditLimit_cat-[110000, 115000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 330 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID_CPTE, Default, CreditLimit_sum, CreditLimit_mean, CreditLimit_max, CreditLimit_min, CreditLimit_std, CurrentTotalBalance_sum, CurrentTotalBalance_mean, CurrentTotalBalance_max, CurrentTotalBalance_min, CurrentTotalBalance_std, CashBalance_sum, CashBalance_mean, CashBalance_max, CashBalance_min, CashBalance_std, DelqCycle_sum, DelqCycle_mean, DelqCycle_max, DelqCycle_min, DelqCycle_std, TRANSACTION_AMT_PAID_sum, TRANSACTION_AMT_PAID_mean, TRANSACTION_AMT_PAID_max, TRANSACTION_AMT_PAID_min, TRANSACTION_AMT_PAID_std, COUNT_XFLG-Q_sum, COUNT_XFLG-Q_mean, COUNT_XFLG-Q_max, COUNT_XFLG-Q_min, COUNT_XFLG-Q_std, CreditLimit_month_1, CreditLimit_month_2, CreditLimit_month_3, CreditLimit_month_4, CreditLimit_month_5, CreditLimit_month_6, CreditLimit_month_7, CreditLimit_month_8, CreditLimit_month_9, CreditLimit_month_10, CreditLimit_month_11, CreditLimit_month_12, CurrentTotalBalance_month_1, CurrentTotalBalance_month_2, CurrentTotalBalance_month_3, CurrentTotalBalance_month_4, CurrentTotalBalance_month_5, CurrentTotalBalance_month_6, CurrentTotalBalance_month_7, CurrentTotalBalance_month_8, CurrentTotalBalance_month_9, CurrentTotalBalance_month_10, CurrentTotalBalance_month_11, CurrentTotalBalance_month_12, CashBalance_month_1, CashBalance_month_2, CashBalance_month_3, CashBalance_month_4, CashBalance_month_5, CashBalance_month_6, CashBalance_month_7, CashBalance_month_8, CashBalance_month_9, CashBalance_month_10, CashBalance_month_11, CashBalance_month_12, DelqCycle_month_1, DelqCycle_month_2, DelqCycle_month_3, DelqCycle_month_4, DelqCycle_month_5, DelqCycle_month_6, DelqCycle_month_7, DelqCycle_month_8, DelqCycle_month_9, DelqCycle_month_10, DelqCycle_month_11, DelqCycle_month_12, TRANSACTION_AMT_PAID_month_1, TRANSACTION_AMT_PAID_month_2, TRANSACTION_AMT_PAID_month_3, TRANSACTION_AMT_PAID_month_4, TRANSACTION_AMT_PAID_month_5, TRANSACTION_AMT_PAID_month_6, TRANSACTION_AMT_PAID_month_7, TRANSACTION_AMT_PAID_month_8, TRANSACTION_AMT_PAID_month_9, TRANSACTION_AMT_PAID_month_10, TRANSACTION_AMT_PAID_month_11, TRANSACTION_AMT_PAID_month_12, TRANSACTION_AMT_REFUNDED_month_1, TRANSACTION_AMT_REFUNDED_month_2, TRANSACTION_AMT_REFUNDED_month_3, TRANSACTION_AMT_REFUNDED_month_4, TRANSACTION_AMT_REFUNDED_month_5, TRANSACTION_AMT_REFUNDED_month_6, TRANSACTION_AMT_REFUNDED_month_7, TRANSACTION_AMT_REFUNDED_month_8, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 330 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode_text_dummy(df,'Year')\n",
    "encode_text_dummy(df,'CreditLimit_cat')\n",
    "df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['ID_CPTE','Default'], axis=1)\n",
    "Y = df['Default']\n",
    "scaledX = pd.DataFrame(scaler.fit_transform(X[X.columns]))\n",
    "\n",
    "preddf = pd.read_csv('pred_df')\n",
    "preddf = preddf.drop('ID_CPTE',axis=1)\n",
    "\n",
    "predscaledX = scaler.fit_transform(preddf[preddf.columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.metrics import log_loss, accuracy_score\n",
    "from sklearn.cross_validation import cross_val_score, train_test_split\n",
    "from sklearn import cross_validation, metrics\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "\n",
    "rcParams['figure.figsize'] = 12, 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelfit(alg, xinput, yinput, predictors, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):\n",
    "    global feat_imp\n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "        xgtrain = xgb.DMatrix(xinput.values, label=yinput.values)\n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
    "            metrics='auc', early_stopping_rounds=early_stopping_rounds, verbose_eval=True,  stratified=False)\n",
    "        alg.set_params(n_estimators=cvresult.shape[0])\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(xinput.values, yinput.values,eval_metric='auc')\n",
    "        \n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(xinput.values)\n",
    "    dtrain_predprob = alg.predict_proba(xinput.values)[:,1]\n",
    "    \n",
    "    best_auc_mean = cvresult['test-auc-mean'].iloc[alg.get_params()['n_estimators']-1]\n",
    "    \n",
    "    #Print model report:\n",
    "    print(\"\\nModel Report\")\n",
    "    print(\"Model best iteration : {} with an auc score of : {}\".format(alg.get_params()['n_estimators'], best_auc_mean))\n",
    "    print(\"Accuracy : {}\".format(metrics.accuracy_score(yinput.values, dtrain_predictions)))\n",
    "    print(\"AUC Score (Train): {}\".format(metrics.roc_auc_score(yinput, dtrain_predprob)))  \n",
    "                    \n",
    "    #feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False))\n",
    "    feat_imp = pd.Series(alg.get_booster().get_score(importance_type='weight')).sort_values(ascending=False)\n",
    "    feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "    plt.ylabel('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [x for x in X.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.849325+0.00538582\ttest-auc:0.843831+0.00858191\n",
      "[1]\ttrain-auc:0.869062+0.00562755\ttest-auc:0.862998+0.0105016\n",
      "[2]\ttrain-auc:0.877448+0.00497875\ttest-auc:0.871085+0.00681015\n",
      "[3]\ttrain-auc:0.879531+0.00492072\ttest-auc:0.872269+0.00688561\n",
      "[4]\ttrain-auc:0.883733+0.00260198\ttest-auc:0.876323+0.0065516\n",
      "[5]\ttrain-auc:0.886187+0.00335787\ttest-auc:0.878036+0.00718743\n",
      "[6]\ttrain-auc:0.887542+0.0040366\ttest-auc:0.879261+0.00678087\n",
      "[7]\ttrain-auc:0.888343+0.00439686\ttest-auc:0.879577+0.00686019\n",
      "[8]\ttrain-auc:0.889009+0.0043915\ttest-auc:0.880232+0.00639852\n",
      "[9]\ttrain-auc:0.889416+0.0045073\ttest-auc:0.880859+0.00703598\n",
      "[10]\ttrain-auc:0.889853+0.00458419\ttest-auc:0.881141+0.00698572\n",
      "[11]\ttrain-auc:0.890102+0.00454464\ttest-auc:0.881892+0.00689702\n",
      "[12]\ttrain-auc:0.891462+0.00282397\ttest-auc:0.882944+0.00569437\n",
      "[13]\ttrain-auc:0.891457+0.00273772\ttest-auc:0.882967+0.00576837\n",
      "[14]\ttrain-auc:0.892478+0.00265264\ttest-auc:0.883837+0.00602896\n",
      "[15]\ttrain-auc:0.892956+0.00273364\ttest-auc:0.884301+0.00655408\n",
      "[16]\ttrain-auc:0.89335+0.00240243\ttest-auc:0.884994+0.00559227\n",
      "[17]\ttrain-auc:0.893388+0.00234135\ttest-auc:0.885101+0.00558411\n",
      "[18]\ttrain-auc:0.893665+0.00231833\ttest-auc:0.885764+0.00574966\n",
      "[19]\ttrain-auc:0.893868+0.002122\ttest-auc:0.885993+0.00563967\n",
      "[20]\ttrain-auc:0.894095+0.00202306\ttest-auc:0.886287+0.00573417\n",
      "[21]\ttrain-auc:0.89434+0.00210851\ttest-auc:0.886331+0.00571543\n",
      "[22]\ttrain-auc:0.894485+0.0019389\ttest-auc:0.886703+0.00537644\n",
      "[23]\ttrain-auc:0.894577+0.00187385\ttest-auc:0.886944+0.00532529\n",
      "[24]\ttrain-auc:0.894685+0.00177743\ttest-auc:0.887491+0.00506858\n",
      "[25]\ttrain-auc:0.894739+0.0016992\ttest-auc:0.887815+0.00512681\n",
      "[26]\ttrain-auc:0.895007+0.00173094\ttest-auc:0.887799+0.00520071\n",
      "[27]\ttrain-auc:0.895004+0.00171651\ttest-auc:0.887795+0.0052284\n",
      "[28]\ttrain-auc:0.895132+0.0016708\ttest-auc:0.887864+0.00525817\n",
      "[29]\ttrain-auc:0.895302+0.00162536\ttest-auc:0.88813+0.00556974\n",
      "[30]\ttrain-auc:0.895368+0.00164606\ttest-auc:0.888195+0.00548937\n",
      "[31]\ttrain-auc:0.895517+0.0016483\ttest-auc:0.888658+0.00569343\n",
      "[32]\ttrain-auc:0.895529+0.00164115\ttest-auc:0.88889+0.00580671\n",
      "[33]\ttrain-auc:0.895619+0.00167925\ttest-auc:0.888941+0.00587292\n",
      "[34]\ttrain-auc:0.895928+0.00135852\ttest-auc:0.889228+0.00556925\n",
      "[35]\ttrain-auc:0.896106+0.00121999\ttest-auc:0.889283+0.00562736\n",
      "[36]\ttrain-auc:0.896145+0.00121933\ttest-auc:0.88916+0.00579556\n",
      "[37]\ttrain-auc:0.896146+0.00126802\ttest-auc:0.889164+0.00578915\n",
      "[38]\ttrain-auc:0.896257+0.00128407\ttest-auc:0.889156+0.00585224\n",
      "[39]\ttrain-auc:0.896378+0.0012628\ttest-auc:0.889194+0.0058516\n",
      "[40]\ttrain-auc:0.896397+0.00124685\ttest-auc:0.889295+0.00586197\n",
      "[41]\ttrain-auc:0.896686+0.00125553\ttest-auc:0.889416+0.0060818\n",
      "[42]\ttrain-auc:0.896754+0.0012349\ttest-auc:0.889512+0.00609066\n",
      "[43]\ttrain-auc:0.896859+0.00118876\ttest-auc:0.889564+0.00602559\n",
      "[44]\ttrain-auc:0.897051+0.00107829\ttest-auc:0.889719+0.00593715\n",
      "[45]\ttrain-auc:0.89721+0.00120086\ttest-auc:0.889679+0.00597268\n",
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      "[47]\ttrain-auc:0.897379+0.00115343\ttest-auc:0.889664+0.00604542\n",
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    {
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    {
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      "[814]\ttrain-auc:0.933538+0.000681757\ttest-auc:0.905584+0.00309449\n",
      "[815]\ttrain-auc:0.933568+0.000686917\ttest-auc:0.905591+0.00306303\n",
      "[816]\ttrain-auc:0.933593+0.000693463\ttest-auc:0.905583+0.00308906\n",
      "[817]\ttrain-auc:0.933623+0.000684267\ttest-auc:0.905588+0.00308501\n",
      "[818]\ttrain-auc:0.933657+0.000691258\ttest-auc:0.905572+0.00308907\n",
      "[819]\ttrain-auc:0.933687+0.000689802\ttest-auc:0.905569+0.00309838\n",
      "[820]\ttrain-auc:0.933714+0.000698069\ttest-auc:0.905558+0.00311544\n",
      "[821]\ttrain-auc:0.933738+0.000701616\ttest-auc:0.90556+0.00311875\n",
      "[822]\ttrain-auc:0.933763+0.000693506\ttest-auc:0.905565+0.00311639\n",
      "[823]\ttrain-auc:0.93379+0.000698913\ttest-auc:0.905557+0.00312015\n",
      "[824]\ttrain-auc:0.93382+0.00070342\ttest-auc:0.905568+0.00311491\n",
      "[825]\ttrain-auc:0.933841+0.000708575\ttest-auc:0.905564+0.00310771\n",
      "[826]\ttrain-auc:0.933869+0.000712554\ttest-auc:0.905561+0.00309616\n",
      "[827]\ttrain-auc:0.933892+0.000717674\ttest-auc:0.905563+0.00310139\n",
      "[828]\ttrain-auc:0.933915+0.000720894\ttest-auc:0.90555+0.0030906\n",
      "[829]\ttrain-auc:0.933947+0.000723581\ttest-auc:0.905545+0.00309305\n",
      "[830]\ttrain-auc:0.933979+0.000725451\ttest-auc:0.905533+0.00309347\n",
      "[831]\ttrain-auc:0.934009+0.000720486\ttest-auc:0.90553+0.00309888\n",
      "[832]\ttrain-auc:0.934038+0.000725817\ttest-auc:0.905543+0.00310623\n",
      "[833]\ttrain-auc:0.934065+0.00072708\ttest-auc:0.90554+0.00311051\n",
      "[834]\ttrain-auc:0.934086+0.000728797\ttest-auc:0.905534+0.00311712\n",
      "[835]\ttrain-auc:0.934109+0.000724164\ttest-auc:0.905536+0.00310953\n",
      "[836]\ttrain-auc:0.934133+0.000728834\ttest-auc:0.90553+0.00311719\n",
      "[837]\ttrain-auc:0.934158+0.000735611\ttest-auc:0.905521+0.00311534\n",
      "[838]\ttrain-auc:0.934182+0.000737747\ttest-auc:0.905514+0.00311782\n",
      "[839]\ttrain-auc:0.934205+0.000741416\ttest-auc:0.905504+0.00312843\n",
      "[840]\ttrain-auc:0.934228+0.000740286\ttest-auc:0.905504+0.00312531\n",
      "[841]\ttrain-auc:0.934251+0.000746899\ttest-auc:0.905504+0.00312211\n",
      "[842]\ttrain-auc:0.934272+0.000750055\ttest-auc:0.90551+0.00312728\n",
      "[843]\ttrain-auc:0.934303+0.000747271\ttest-auc:0.905486+0.00313643\n",
      "[844]\ttrain-auc:0.934321+0.000748256\ttest-auc:0.9055+0.0031321\n",
      "[845]\ttrain-auc:0.934346+0.000753521\ttest-auc:0.905506+0.0031187\n",
      "[846]\ttrain-auc:0.934376+0.000758649\ttest-auc:0.905493+0.00311494\n",
      "[847]\ttrain-auc:0.934399+0.000755711\ttest-auc:0.905495+0.00310212\n",
      "[848]\ttrain-auc:0.934422+0.000752544\ttest-auc:0.90549+0.00310419\n",
      "[849]\ttrain-auc:0.934449+0.000755029\ttest-auc:0.90549+0.00311031\n",
      "[850]\ttrain-auc:0.934475+0.000745094\ttest-auc:0.905497+0.00311154\n",
      "[851]\ttrain-auc:0.934506+0.000731433\ttest-auc:0.905499+0.00310641\n",
      "[852]\ttrain-auc:0.934532+0.000724774\ttest-auc:0.905499+0.00310779\n",
      "[853]\ttrain-auc:0.934555+0.00072987\ttest-auc:0.905497+0.00311432\n",
      "[854]\ttrain-auc:0.93458+0.00072729\ttest-auc:0.905498+0.00311321\n",
      "[855]\ttrain-auc:0.934603+0.000725853\ttest-auc:0.905499+0.00310825\n",
      "[856]\ttrain-auc:0.934625+0.000723188\ttest-auc:0.905502+0.00311407\n",
      "[857]\ttrain-auc:0.934653+0.000726499\ttest-auc:0.905495+0.00310791\n",
      "[858]\ttrain-auc:0.934673+0.000733604\ttest-auc:0.905487+0.00311452\n",
      "[859]\ttrain-auc:0.934706+0.000734677\ttest-auc:0.905502+0.0031156\n",
      "[860]\ttrain-auc:0.93473+0.000743732\ttest-auc:0.905501+0.00310153\n",
      "[861]\ttrain-auc:0.934749+0.000737314\ttest-auc:0.905509+0.00310811\n",
      "[862]\ttrain-auc:0.934773+0.000737421\ttest-auc:0.905517+0.00310729\n",
      "[863]\ttrain-auc:0.934799+0.00074495\ttest-auc:0.905512+0.00310742\n",
      "[864]\ttrain-auc:0.934821+0.000756143\ttest-auc:0.905509+0.00310471\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\marcp\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Model best iteration : 816 with an auc score of : 0.9055914000000002\n",
      "Accuracy : 0.8894957983193277\n",
      "AUC Score (Train): 0.9294242360734102\n"
     ]
    },
    {
     "data": {
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1ioiIiIh0mUaeFvJ2MxttZgvNbJGZLTazRRuaz90fAOY1mI6zgRvcfaW7jwfGAK9qcF4RERERkc1CIzc0/gA4y917uftO7t7T3Xd6Huv8jJkNyWEju+R3fYDJtWmm5Hf/wsw+YWaPmdljs2fPfh7JEBERERHpXI0E1zPdvbP+veHPgYOBY4h/oX5Jfm/rmNbXtQB3v8Ldj3X3Y3v37t1JyRIRERERef4aGXP9mJndCNwCrCxfuvvNG7syd59Z3pvZlcCf8uMUYL/apPsC0zZ2+SIiIiIiXamR4HonYBlwSu07BzY6uDazvd19en58G1CeJDIQuM7MfkTc0HgI8OjGLl9EREREpCttMLh29/Oey4LN7HrgBGB3M5sCXAicYGbHEMH5BOCTuY7hZvZb4ClgDXCBnhQiIiIiIq1mg8G1mW0LfJR4TN625Xt3/0hH87n7+9bx9S87mP67xPO0RURERERaUiM3NF4L7AW8GbifGA+9uJmJEhERERFpRY0E1y9y928AS919APAW4MjmJktEREREpPU0Elyvzr8LzOylQC+gb9NStIlc8p4zujoJIiIiIvIC00hwfUX+s5evE0/1eAq4uKmp2sSm9Huwq5MgIiIiIi8AjTyK7153nw88ABwEYGYHNjVVIiIiIiItqJGe65vW8d3vOzshIiIiIiKtbr0912Z2GPH4vV5m9vbaTztReySfiIiIiIiEjoaFvBg4A9gZOLP2/WLg481MlIiIiIhIK1pvcO3ut5rZn4Avu/tFmzBNIiIiIiItqcMx1/kvyN+0idIiIiIiItLSGnlayN/M7FLgRmBp+dLdn2haqkREREREWlAjwfXr8u+3at85cFLnJ0dEREREpHVtMLh29xM3RUJERERERFrdBp9zbWa9zOxHZvZYvi4xs16bInEiIiIiIq2kkX8icxXx+L1352sRcHUzEyUiIiIi0ooaGXN9sLu/o/b5m2Y2uFkJEhERERFpVY30XC83s+PLBzM7DljevCSJiIiIiLSmRnquPwUMyHHWBswDzm1qqkREREREWlAjTwsZDBxtZjvl50VNT5WIiIiISAtq5Gkhu5nZT4G/AIPM7CdmtlvTUyYiIiIi0mIaGXN9AzAbeAfwznx/YzMTJSIiIiLSihoZc72ru3+79vk7ZvbWZiVIRERERKRVNdJzPcjM3mtm3fL1buC2ZidMRERERKTVNBJcfxK4DliVrxuA/zCzxWammxtFRERERFIjTwvpuSkSIiIiIiLS6hoZc42ZHQX0rU/v7jc3KU0iIiIiIi2pkUfxXQVcRTwt5Mx8ndHkdHWZ/v37d3USRERERKRFNdJz/Rp3P6LpKRERERERaXGN3ND4sJkpuBYRERER2YBGeq4HEAH2DGAlYIC7+1FNTVkXu/e+gwE4+aSxXZwSEREREWkVjQTXVwHnAEOBtuYmR0RERESkdTUSXE9y94FNT4mIiIiISItrJLgeaWbXAX8khoUAehSfiIiIiEh7jdzQuB0RVJ/CRjyKz8yuMrNZZjas9t2uZnaPmY3Ov7vk92ZmPzWzMWY2xMxe/tw2R0RERESk6zTyHxrPe47L/hVwKXBN7bt+wL3u/n0z65efvwycBhySr1cDP8+/IiIiIiItY73BtZn9DPD1/e7un+towe7+gJn1bff12cAJ+X4A8BciuD4buMbdHfi7me1sZnu7+/QNpF9EREREZLPRUc/1Y01Y354lYHb36Wa2R37fB5hcm25KfqfgWkRERERaxnqDa3cfsAnTYetKwjonNPsE8AmA/ffff50zioiIiIh0hUZuaOxMM81sb4D8Oyu/nwLsV5tuX2Dauhbg7le4+7Hufmzv3r2bmlgRERERkY2xqYPrgcC5+f5c4Nba9x/Kp4a8Blio8dYiIiIi0moaec71c2Jm1xM3L+5uZlOAC4HvA781s48Ck4B35eS3A6cDY4BlwHN9QklT7DVoMAAzTjymi1MiIiIiIpuzDQbXZnYo8Wi8Pd39pWZ2FHCWu3+no/nc/X3r+enkdUzrwAUNpFdEREREZLPVyLCQK4GvAKsB3H0I8N5mJmpz1rffbV2dBBERERHZTDUSXG/v7o+2+25NMxIjIiIiItLKGgmu55jZweSj8czsnej50yIiIiIi/6KRGxovAK4ADjOzqcB44ANNTZWIiIiISAvqMLg2s27Ase7+RjPbAejm7os3TdJERERERFpLh8NC3L0N+Ey+X6rA+tmedXNj/14AHDngSI4ccGQXpUhEREREulIjY67vMbMvmdl+ZrZreTU9ZSIiIiIiLaaRMdcfyb/151A7cFDnJ+eFZcRhhwNw+MgRXZwSEREREdkUNhhcu/uBmyIhIiIiIiKtrpH/0PihdX3v7td0fnJERERERFpXI8NCXll7vy3x78ufABRci4iIiIjUNDIs5LP1z2bWC7i2aSkSEREREWlRjTwtpL1lwCGdnZAXusvOv4/Lzr+vq5MhIiIiIk3UyJjrP5L/+pwIxo8AftfMRImIiIiItKJGxlz/sPZ+DTDR3ac0KT1bhEvecwZfvPFPXZ0MEREREelkjQwLOd3d78/XX919ipld3PSUbSGm9Huwq5MgIiIiIp2kkeD6Tev47rTOToiIiIiISKtb77AQM/sU8GngIDMbUvupJ/DXZidMRERERKTVdDTm+jrgDuB7QL/a94vdfV5TUyUiIiIi0oLWOyzE3Re6+wR3f5+7TwSWE08N2dHM9t9kKdyC9O/fv6uTICIiIiLPwwbHXJvZmWY2GhgP3A9MIHq0RURERESkppEbGr8DvAZ42t0PJP79ucZci4iIiIi000hwvdrd5wLdzKybuw8CjmlyurZo9953MPfed3BXJ0NERERENlIj/0RmgZntCDwI/MbMZhH/TEY2gb0GDQZgxok6nxERERHZ3DXSc302sAz4AnAnMBY4s5mJEhERERFpRRvsuXb3pWZ2AHCIuw8ws+2B7s1PmoiIiIhIa2nkaSEfB34P/F9+1Qe4pZmJEhERERFpRY0MC7kAOA5YBODuo4E9mpkoEREREZFW1EhwvdLdV5UPZrYV8c9kRERERESkppHg+n4z+yqwnZm9Cfgd8MfmJkvWpW+/27o6CSIiIiLSgUaC637AbGAo8EngduDrzUyUbJgCbREREZHNz3qfFmJm+7v7JHdvA67Ml4iIiIiIrEdHPdfPPBHEzG7aBGkREREREWlpHT3n2mrvD+rMlZrZBGAxsBZY4+7HmtmuwI1AX2AC8G53n9+Z6xURERERaaaOeq59Pe87y4nufoy7H5uf+wH3uvshwL35WRrRv1dXp0BERERE6Ljn+mgzW0T0YG+X78nP7u47dXJazgZOyPcDgL8AX+7kdYiIiIiINM16g2t3b+a/OHfgbjNz4P/c/QpgT3efnuuebmb6RzUiIiIi0lI66rlupuPcfVoG0PeY2chGZzSzTwCfANh///2fNTB8S3fkgCMBGHru0C5OiYiIiMiWqZHnXHc6d5+Wf2cBfwBeBcw0s70B8u+s9cx7hbsf6+7H9u7de1MlueWMOOxwRhx2OACXnX9fF6dGREREZMuwyYNrM9vBzHqW98ApwDBgIHBuTnYucOumTtuW4JL3nPHM+yn9HuzClIiIiIi88HTFsJA9gT+YWVn/de5+p5n9A/itmX0UmAS8qwvStkXq37//M3/vve9gAE4+aWwXpkhERESkNW3y4NrdxwFHr+P7ucDJmzo9IiIiIiKdpUvGXIuIiIiIvBApuJb12mvQYPYaNBiAvv1ue+b7+nsRERERqSi4lucn/zvkkQOOfOZRgPUnlYiIiIhsSRRci4iIiIh0EgXX0lR6xraIiIhsSRRcyyZTf8a2iIiIyAuRgmvpEvoHNiIiIvJCpOBaulT//v2f+Sc299538DP/xKY8pQT0pBIRERFpHQqupXWt40klIiIiIl1JwbW8oOgxgCIiItKVFFzLC5aeVCIiIiKbmoJr2SLUn1SimylFRESkWRRci4iIiIh0EgXXssVa35NKRERERJ4rBdci7dQfAygiIiKyMRRci3Rgnc/Y7t/rmccAioiIiNQpuBZ5HurP2K4/ArD+pBL923cREZEth4JrkU2oPKlE471FRERemBRci2xGNvhv32tDUtRrLiIisvlRcC2yBZjS70H1mouIiGwCCq5FBGig11xEREQ2SMG1iDRGQ1JEREQ2SMG1iDTV+oakiIiIvBApuBaRLlEf672uISl9+92mZ4uLiEjLUXAtIi1lY4akdHQjZ7HBwF5ERGQjKLgWEelIu7Hmhcaai4jIuii4FhFpAvWai4hsmRRci4hsrjai1/yS95yhXnMRkc2AgmsRkReY9r3mxfp6zUVEpPMouBYR2cJt6AktG9NrrsBeRLZ0Cq5FRKRL1IP5fwnsU7MCe41tF5FmUXAtIiJbtI0J7C87/z6NcxeRDim4FhEReZ6ea6/5xvbYi8jmT8G1iIhIC1hfr3kJ6mHdgf1egwY/78C+s9ct8kK22QXXZnaqmY0yszFm1q+r0yMiIiKd61lBdicE9iKbk80quDaz7sBlwGnAEcD7zOyIrk2ViIiIbM42tse+mvFfA/sRhx2+3sC+eD5XC5qx7nvvO3iTr1vWb7MKroFXAWPcfZy7rwJuAM7u4jSJiIiISE0jgX2xrsD+yAFHNvWk4rmuu3g+JxXm7hs1QzOZ2TuBU939Y/n5HODV7v6Z2jSfAD6RH18MjAJ2B+bU/rKR75/rfJ2xjFZct9K8ec+nNLfOupXmzXu+LXXdSvPmPZ/S3HXrPsDde7Mh7r7ZvIB3Ab+ofT4H+FkD8z1W/7ux75/rfFvqupXmzXs+pbl11q00b97zbanrVpo37/mU5q5ddyOvzW1YyBRgv9rnfYFpXZQWEREREZGNsrkF1/8ADjGzA81sa+C9wMAuTpOIiIiISEO26uoE1Ln7GjP7DHAX0B24yt2HNzDrFe3+buz75zrflrpupXnznq8r192Kae7KdSvNm/d8W+q6lebNe76uXHcrprmz171Bm9UNjSIiIiIirWxzGxYiIiIiItKyFFyLiIiIiHQSBdciIiIiIp3kBR9cm9luXZ2GzUln54eZ7dGZy3uOadA+3gRaqexsDuVSXpg2VXuzrjL8XNfdFW3kxtTBzbm+6viyeWpWmems5bbsDY1mtiPw/4h/PHMgsA3QBiwHJgOPACcCWwO9gUeBK4EBOf3bgPOBp4mnk/wqp90X2At4GbAn0BPYGXDi37PPJv4t+5uA43P+ccAbc/0AI4GPuftQMzsI+DrxvO4/Ar8HFuY0E4BDgR7AYGAN8V+A2oADMp0AK4Fz3H24mR1DPJ5wX2AtsJT4r0E9ge2Bp4DLgD2AVwOfyuUdDNye02wDrABWAwsyPeOAXrlcgO2AGZneN2ce3gTsAJwBzAIuB35APDLxZODJXM5PgQ8A52W+/Qz4LvB+4HHgo+4+08wOBn6T++ZwYB/iKTG753b9H3A68BLiP3G+Dfg88EPgW8BVwH2Zlh7Ah9z9fjM7Frg2t/MNwNXAK4ElmZf7E+VkRC7jgNzuO9z9OgAzeyLz53/d/WozOw+4GfhRLmsY8O/Ag7m9NwD/Afw290+fzMdVwERgLGA537cy734P3A0McfdDc3rM7ChgJ6K8rQUOA16e8z4NDHX323PazwDjif9WuiL3xVHAsszr68s25fSXu/unzexU4Fzgd0TZORk4Jpd1ANAXuBGYR9SxvYiy3w34EFEmbnD3IWZ2EXAh8F+5f54EPl2e9JPbM4VKH+AnwEFEHboyt3X73EdjifoxG7jF3f+ay+me++B44N/d/Zr8fo9M6yqi3D4AfJooJ/9GlJ01RH0e5u4X53zbEmVgh9w3y3KfdyPK5MeJ/f3aTPMvgZ8Dh+R+uZeoX18m6scKYDTxSNExRJ2fmXkzFbieqEPdcr2rct9OAC4FfuPuS8zsqMzXz2V+/A2Ym9syCTgLeAdRZo2o8yNzHx2Q2zAVuJWoT28h6tNDmRfLgRnuPsrM3kzUgduJdnMGUYYPz23rRtSjCTndbsBxRPtyR+bTtUT5vD+3+SyiLm9NlKdBwDeB9wB/INrKl2e6r8ynRB2fZWAc8BqiLi3JMrEMuCbz951E3XiAqH+/Bl4+AFfZAAAgAElEQVQHDAEeA7YlytYFRDtlRPs4E7gl83Lf3M4PA3/PdR2S05J5dQtwLNFuDiLayXfmNLNz//cjysV+RPkaTdS3hWa2J1EPr8l8+k/gs8R/F348//Yhjj13AN8n2slXEHXl07n/XkXUt+NzefOItu6xTN8umS9jgT+5+3fM7PvAj3MZbyDq9ZqcbwqwK1Hmy3x/dfcvAphZL+I4tTNRpn7o7o/m9zdlfp1FlO+vEPWub277xzJ9nyXqTq/My7W5rulE+ftdbtfR+fl/gL/kftuHaD+PyDz7CPBFogweTLRVTwIvzTS0EXXj45nvvYg6eypRDz+Zx5oHgNuI+nEUccy7CPhq5tG2RH1cTpTh7sSxYrf8fkfgn8DeRN2dTLS5T+VyriPK6iuI49VVuY1tub3jiGP8AcRxbJ+cvidRFsdmXm1DtAnDiTr3EiJG+AFRLx/I7/8z92cf4rhwAdEmHJTpHUGU4Y8QccRqoh5tQzwlbm3uq+FE+diNqOuLiTL7YqKODgU+6O7Ta2XjFUR58tynDxH1fHDm0wdzuw7O38/Lffsq4vjVP39/BXHsvoqoE7tm+hZlfowm2vk5ZvaK3H/b5/bdBpxAlJtX5faOym36K1F3VhBt9BuJ9nk40V4dn3m9H1GO3pHLmJjTfZQo2wcRscgdwPHu/mmojqFsyMb8x5mufuXOLq+/EAetx4gCtyQzd0DuyBn53S+yICzPDF1FFM6LicozNnfCGqKSriEq3rycfy4RwK0iAqjriQrz69xZtxMN1kqigK0iKvX43GFPEJXvp7meRblTnwTmZ/qnUDUUQ4mD1epM84L8+whxsJpNFP5RROH6K3HAnZXpvoOoODOJyjSBqNhriAJ3G1EJ5hCVciVxwFmQy7ws3z9FBI5LM503EpVpZf6+Otfj+XdVTrsy17WGqFiXEwfd2bnOMTnNkFz/wvxtGBEELyWCmMtyupnA5zI904lKtCvR0D0IzMyycW+ma2Cmd0W+VmValhCNXDmpWJ3rWkocRN6Z895EVPDxue5VRIWcn/OsJBqgNbndbflaletflmkcmHk2nShD83IZZXqvvdpyWYtyWybmPirlbmltffNyHV/PaafmtBdSNXaP57aOIMrDQCLwui3zbdfcvjWZ52uJRvOATOPsTPOM3C9lmeNymcsyH5bl8hbkbyVfl+drGBHgjs5tnJqv1bnuMv0K4E9EGX8cuJPqQOc5zaL8rp5/KzMPShlcnulaVcvXUoZGAG/NtJZ9PDLXfWVu8/icfgZVGfpvor7OJurZJKo6cQNRvwcQZXxZ5tU4Imi6Opd3KRF4rMrteB9R7tcQdX91fl4GnJ3LvDy3YTHVwfeq/PxL4oAxIbdxYubxr3KbBhMBatmeQUT5n5zfLSfq4V9q+TUrt3cx1UnoP3JbZ1IF3hNzn8/IZUwkDpiLcrnzqU5e1xDlcD5RPxfm8mfn67rcphVUAZhTlfXSbn+nlv+jiCDjKap2ek2moQQVq3MfXJDzrMi/bVTt0H1EELUy53kZ0faOzPWtzDyZldMNy+U8nH+nZtouzvz4be6XKbnu8VTt5Lyc9rrMz8X527TMm/flds/M+Uqd8FqeLSbK7TXEcWd1Lm86UXcOIersRbkdl+R+nJrreCK3YVZOM5U4sX9mvqxL1+a+/FL+XZjpnJbLm5Bp/3vuo+FEMFLq4NLM45VUnV0zc3seJDqlnCqILeWmjSj/CzK942v7dxlRnkfltr0m9/U8qjZ6CHAKcfL2i0zHklzeoMyvfpnu23M/rsntGZ558QRVW/RAvv9H5kvJhwNyvfU2ubRH5XjowLKcrxwrSps5igjuLiOOD9OoOhOWEPXqU5nmSUTgv4SoZyUmWUZVpobm359QnTROy/26LJf9GqIujwPOJMr/7Fr+Dyba3j9nPi7L7/tn3pbj9WKqtntpTlfagTFE2VpJ1PW1RL36Q+bPhcRx56LM14m5nx/I9KwggtwHc5uOzTz/Uc5X8ndFfr86v5uby/pH5tflVG3pN6lOekt5bOPZsUt5zaI6vpf4ZRVVXDAf2Cb36RMNxatdHTBvZHC9lmgUy8FiUGbioMy0kcTZ3Mjc+U/nfP8EFuf7yUSDNCMLyygiePpu7pS1WVBOISru7JxvGPBkvl+VO2xUFswngaX525BMS9k5q3OaksbltYCgNB5X59+1ucwJOe+TxBnedKKwz87lngKMqq1vOdGj8jTVgea1+X5cTrecCMSH5OdlOd2jRCV4GhiRvw3N7dsq01/ycX7m2dPEmf0/cztuJAL98UQFnp7bMJLoySxBef0EpDQ4JR/+uZ58fjrzpZwglGCiXllKHqwhGpMSMEzP9J+V27iIaEifzNewXNaSzNt5VA3n8tprbKZhLVFuvkgcROflfNOJXuDZud5zgcG1/bOaOBmbRBwgFxNlazTRqzin9t3/Eg1I79wP5SRkLRG0TKFq5P43t7V3bR9PyHTNzXxty8/lRGNl5mUbsCrnW1HL/2VEeR2Z6R2c8/yaqoFfmnk5h3j2Z9nufycOUK/KZbZlesuBcwlR3kr+js/vu5ENFlXDfnqm4Xc57W2ZH0/mfE/mum+mOkiMJOrBEKJcdSPqSjlpvjO34ZtEXViZ6R1C9IgMydddRLlbkds7kOoKz8DcjnJisJjohRxFNPCDqU42S1kvwZ7X8nxVbs9Q4gBaAsBysr2UCCB3ozqoDMm/paNgfObtLKJcj815nqzl5eha21kOio/mtjjR61QO0sNzfW3A2JxvRG5T6TFamvP+MfO8Ld8vzTyYkXk7hGgfPk4ViMzJad6c+2Q2VQB3E3Fl4elMy/iyHbX2sgSmg/JvG3FSszy3b2ju/9VU9W9ozjeU6BEsgdxORBlaQdUmPlmbb1WmtV4PltamG0t1AlrSNojqhL2ksaRtJVFuSvrLVRInyuVS4B7gSKr2aVWut0dOX8r2MuCpTMuXc7r/IALmmfmaShWA9ajtj6G1fKm3naV8lWBjZW73ykxnKcdP0q59zvdfoiqbuxH1ahVVcDqC6tg3NfPiyMybsi8HUR3bp+VvTxPl5ubcF/WyXdY9KqcpJ2Yj2+Vz6WUt9b9n5mUJvJ6s5e1q4rg3KvfJk7U2cmht3fMyTXdRnRC9gTgWrKqV21L2Sg91OY7PI9qmJ3J9y3O5uxDtfTn2DKm9H5Z5WuryXUR7tCaXe0NJd+14MLIWA5VyPji3p9TXke3yYBVVfPHHnPZuog0bnr8fSPRuryXq0qhM//DacW9JrZw8SdWOleNQOY7X28VyslLazlU8+9g/orZ8z+1eRHV8XpTfr6mtewhxVWhJ7ouSD6Wj7E6iLA7Oacfn+68RbdluRHks7xsKrlttzPUI4jLPiUQmfIM4OEwgMuoy4iDck9gR25rZ64lLmKtzGQuIgt2HKOS7EWckXyMC71FED3E/otHvmfMtBnYxs3cRB+09cvpDiUBzgZl9gWjIphFnZHvl++XEEBYHVpjZO4gDaDdi538kl7XG3V9M9OYA7Ojui/NzCarnEUMpHjKzh4hGZQnRKz2LqCBj3P3h/G0PMzuJCIwPA7Yxs+tze3pQNabzgB3NrFtufzkAdScKLEQwN4MI/P47f1tF9AZ8LecpPRELiLPP03KaMUTFOzD34wiiISm9N4fkutcCPfL9ivxtHBG8P5Xbvlt+vyzX9W1iCMlTubxxuX3dqIKs0mu8PLdrFlXQN5VoKLbKdH4j83Fubsd1VL0H3yGC659kWrcjeq1OzGVPJM7At8uhJJOIALEXcSn220QDvBNxKfKOzLNxwOuJsrw11XCFHuQZtbtPyjSNIw4KEzKdE3L40bzchrnE5c4xVAfl0rjNcPeDyEDLzL6Y73fOfbwi13tZzrct0Qv2EqIH4Te5Td2IA8cdmY9t7v4/AO7+KNVJztVEQ/V2om5OJOrdEqoerLcBmNn7My1bewx7MXd/V85zeO6jHXO+XrnuVZn2uUR9uyTT3ebube5+N1XweTlRdr5AHNR6EL3SRtVLvoa4JL0q98OKXOZ8oj5dQhWMzMz3r6W6rNwt98+ELOuDc/0lCFqebYgRZad75vso4hLkDUSbszVxwJmb+TuB6HUaTpSfUhbLwed3uS2LgB2y/pD5DFFe1gDj3f1VVCc978jtWp35Pbe2HVD18LRlfi3IvP5pvofo+S8945bbcEjul4FE/RqeebU7USamEMNL5me6TqK6Krhrrr8bQA6xWJp5OQz4HlHWVxNDCubkMlZ6HDUXZlohynI52JaheCUw6Ua0Z/vmOnoCZUjHitzOPxLlandgkZldTdTdHYmDcp/c7pV5XJqWeXxi7pc5RF3+Ra5rdP42hjiRmk0EPhDD+P4r826nTMOLiOFAC4mTye0z3WWY1bfzb0+iPG2Xv/UmytBSot3ePvN1ak6/P1E/z8z9uIQqUJtFBK77E+VyPHH53In6s7WZvTLTsW0u7xaqq2FjM79KvYLqeAcRqM3NbV1ElOU17n5i5s0Ud98n07Q3sZ//mcvcp3acWJXL60XVizqP6JWu5/MYqquoOxEdTp77plem6xCqOjw2191GHJcuoxoyAdGZtDbz/M7ME3L+UmY/kGO1uxNlYzFR9uYSx+FlxHDRPYj6YkSZPSHT2IMoV2dmesYTHTFPE+3OGKJXeEfiZPl7RI84xPHzIKJs7W1mBxD7f3Hm3d5UvceTiWNGt8yT7pnfkwHc/czM10MzrXvm9+PdfRTVid2MXPea/LsC2CrbOifK3rhc7hAiSD6AOGbPItoBctuWZtv530T5eSNVWdnbzB6katfe6e4luB+e72cD83LdAD3d/bPElboDgN3NbBsqX8rl709c0fT8+z2izj2QeVLeNzYGv9m9zZ35IrroX5zvj6LqgZlCVbFKb0G5LDQqvyuX0b+SO/4KooCXsbTfo7oUPy9/n0wUljuIRnZl7rgF5Jk4VY/ZaqrLUDcDr8j1nZxpGEEEF7OoAtBzid6u4fl3PhFQQ1Si1fn+Rfn7I0TlLL3ApQe49OCW7b6DCHafpBqeMJoqmFlLBEgziMZ2HNFzNL+2zHLJazrRWF1JnPVdmOn4TKZlRqZxX6oz8knEwWRHouI+kst6gBiH9RDRoz2EOBscm9OU+Zfn+xHE+K0yfvL3ua7/l/t0LtFA3k70HvbIdNxN1Tt6X+ZdOeNflfvpp8QB9mfApbncXxJjqyAq4ujc3gszrRcSB61umb8zM52DiAZyZW3/lGEK83M7LiYOULcTB+BJRLl9MD9PoLqcvDj3W7l0Piz35V7EJbdyxl+uFPTP/Cv7bE7O1yvz7jiirJWz+3Kp8s+5TRdnOsfm/m6jGiN4Z6679A6NyvyfltM+msubRfRY31BL5zlE2VnZrh6fR9S9VUTPY+lZWkPVm3gO8DmvrmZ0oxoGMY0I0MrrIaL+/YIYfrGQrDs5/xVUvU5XE5fWr6YaTraslp5ySX4M1bCIMfnd2bmMCzMtvyLq+1iqy6yjiPr3OBHUTCbqzR3EkI8SFJbe+NU5/3FUPVLlhHdtru9G4oQZ4oR1WU77MNEu9SWCm7INnn8X15a/nBjjf3HOt5woi0upeukfzTSX3s87iKCmDFtZTRwMb8t1LSbK0BNEWRie+2IkVVkbQQQ15YD4x1xWGcJThtH0zX38jdp+WJ75VcaPlnG6D1ANWfpZbsMC4OTMozdTXV0sPXSPEAfPHxABaLnqNqXkdb5K235Dzj+CuNJR8nxN7tsbiTbhCqIN/UqtrM0lxpdfldvzI6I9uZQo3y/KfXF0fr6DCLgupQp0S+fBHbnt5dL2Mqo6V4YzXp3rvo7o6Lg651tGlN3SA/g41eXvNcD3c76LibJwITEk4s/EUMFXE3XlXKIM/S231amuYo6iGhJwNnFcLnm6mqgTTxPt3bBczuNUVyMnUt0HcGqm59fEmOm3EielZTjPzNq+Kj3q44i6eHnuk58SbXRpL6/JZU3PPBlJDOO6M+f9baatnEROyM9L8rv5mf7bc/ljifJerqDOJk4KF1INJZtKdRW8DTgjt+sIqmF302r7YVXu4yeJ2GFsvu7L+R4G7s33N+R+vg84Zh2x0Mxc5yO5rlJmF1BdTVxOXBncJfOrdBKUYY3DiaAVqrK6Q66znGiXYY5lOOREotwvJ+rAhHxfhqm21eY9g2irdyHq0NFUw40mUvV+9ybKwiNUQ0cmUMVPyzJNY/Lze3K+o3Pf3kEMc5qWyx5OnIDcXMuLzxHHqisz/2YRbcsPgDfm8k6lugL4zPsNvVr2hsZGmNnpRIH+p7vfk991Iwrji4ibm0aa2Z+Ig8aniB7JW4kznAlUvbt9iLPLKcSlrrXPI127Az929w+a2eeJXqcfA8d69Co+l2W+NtN5G9ED+l2iEv7e3dvy5sF3EA3+XGA3jxsFuhEHrfr2jSXOUue7+zQz25k4e5zkcXPLNu6+0sy2Aw5292GZhiOJs+ud3P2r7dJ3nFc3ppm7u5m9hOiRHJa9SuXObHP3Oc8lH2rrewsRsBxGBEltRG/sm4ngbhLwbXef/jzWsTcxVvMO4uah17r7BzcibXcTB5aXed6g2G66bYiehD2BA9z9L/n97sDe7j603fSLibJ9lrv/ZCO2Y2dyPxA3cWxF9B7NXMe0VraVCIDL+/8mxhmObjd9D+Dd7v6bdt9vR5Spbrm+nwPvJm6ueRJ43OPmvj2Jhv6ynG9v1pNf7ZZ9krvfVk+3d9DYmdl7iJ7xh9u1FT2IHqr566rzmR+lLu1ClK96XbrL3eevY76jqa68rHH3GfX8IoLRvu7+QNk/7v6wmfUh9tVdRBl6dX1f582jXyI6Cw4l6nZP4ma3u3Oa1wHd3f1BM3sZcSVmO+IAPTGXvTDnPZIYs/k34mSiL3GlYXEuc2r79iHXcUIpr7W0nUp1Q1VpR9vaTbM9cRK6e0f7OKfdH5ju7qvbfV/yaDDRm7iG2H8r2013HHHg7ePuD3W0rpx+d2rlYD3t17VEu/sHon6XXuaB7n67mR3q7k83sK72beu1RIfGBUTQUfLwRKID4gPt67yZ7elxM9+62vgOj2GlfJb6XCvb+xF19I9k2W6fL/X0k0ONSntuZj1p18a039YO0rRbpn9l+2WsY9pn5XMjeVBvI9a1TR2k/wziptqf0UHblGV7T3cfn9uyC3F1Z4y7L1jXPB3kxb7U2o12vz1zrK1919BxtYF28mjg39z90nX8tjsRvJYrwAvdfXwHy3qmXduYNOQ0z5SZ9e2rjpZn8WCIl7r7r+vtF9ERssGy2IiWDq4zYHwbUeFfTJz9PUzcNLEfEVj9gTiz3J+48WJboqdjNHFGdjpxae31xAGtjLccQF7icPd5ub4jiQbmDOLM6HGyZ8jd781L7O8gKv8wslElArD3E2dw2xI78UTirPG/iB7TlUQQ1Svf/9TdH69t6+eAN7v7W8zsCOIMakku+yVUl8M+TlxKupUMrokDCMSlrxOIBsbz+4FEr8uHzOwwovfhwEzDNKJ39E53X5Tbty9xYvIUcRPMwkzfriWfaml+ee6LPsQlvXL5voxt/i1xJeFjxFMupua2tOW+G0H0bO4JPOLuS2rL/rO7v7Hd+s7L7T6EuOy0yMxG5D79GNGLcXHus+/ndiwjTmwOJcanladwvLq2jCOIhvNAqpt6phFn7VMzDydnmt9InG1f5O4fape+azKfX0ScXZex7lfl9zvmMsYRJzdnt99X7j6itryz3H1gbXkXUz215WCqpx+8HPAso98gyr8TvSB9cpveR/UEh4tK/uf2b0cMkzo78/IiIjib1y4dWxFB2DuIXo0LgQHZAL663fK+QpTd7Yjeqd8RPVXXEmXmHUQPwt9yfbsRY1IXET1qI4gepK/X6sRbiLJyp7v/2WKYyUcyP77n7vNy3Zdn/jxMXLE5vuz72j7aNaffal37p7btrwLe4u4XZvvwVaK92ZMIzHfK/C3l+Yr2wWAu5zxgrrsPbPf9WeW7du+fIoZcDSTq9CuIYWPfJ9rC12Z+31HbP4cRV/9+3K4u9Xf3/utYx6617Vzf+7Lskl+HZZn6O3FSdXVOd2rm8YT2y6jlo7v7Pyye9vN6opdxEREUDSPajw9S9XqOphoyNJSojwcQva+vJTpL1lLV14HuPsLMdiWOB+dQ3Vh7AFG/yz0Z1xOXz+t1sHv+1ouot6/k2XWmO9Fz/w2ivfgscVXrFTl9uapar0u/o2pTb3T3yZkfHyPK/EuJXsPjiTr6I6qAehJR599F1JsTiPq/sLa+nxGBzsuJOvl/RI/uOKK+vZyogz8gTsJGkEOvcl+UY83I3KdLiLZ6kbvfknXsdVT3Ph1ey5tXEAHkSKLXd/ec9pl6kMe1yVRPtPh7rvcttf1XrmReTrRpp+Yyxue+eYRI8JLMuy/m/hrm7ndn8PS2Wlksx7nTM2+ezmWNr637DOLkaERtHce7+525jFOJk9HSTp+V2/k0/9pOH5Z5tjtxbJpIDGd6HXFcmkV1Y9+7cr8/QpTpl9X25eRau1bycVjmc/csK90yH3emusGwHOediDneTnQ6ln38idznC4jhG/sRde4vVO3JZ4i27O+ZV/fn/nkV0ft8kcfTWnas7YeTiKse3TIto4Hr8hhQ2otriOFRNxN1qMQC03Jbv0Gc4N+f6z2fqH+TqZ5+dB7Vk6LGZbr7Zt6NIerWSfn7YKLerc7fnxVj5N+35f5flvl2fYlzNopvBsM9nsuL6M6/hxi39Dei8N1FVP5fUg2DKMFceexQuYyxiCh4D1A9tcKzEJS7s8dT3RD4OaKSf51qKEYZevAocaBfQDQii4hAvR/VUyZKYDApd+hSqrvky2Wucrl+VS5/cu7ckVSX/FcQDc3dVE8feYxnP5avpM9r85QnpTyVaX0i86Nchp6Xy+qX8ywjAvMVwFczD8qQinKH+p+ISloapwlETxpUY0z7EUHcGqonmJRLi+XAVp4i8Ueqp0SUsX6Lcn3lcujAnK5cch6Y6zub6kbStcAp+f1fa/lRbsYrl1NX1aYvT1z4bs43nDgD/zxxkrQi07GUaozYstwXJW9nU12SL2M/Z+U+KWVzQa5zItVNmOXGorKvys1EPyYah3KT6kTiIDo0990MojEp4+dWUD1loNxYNb+2zD/lb+US2zziklj57nv5urlsf+bFrVRPZbg7t2FGLvOpXFa56aRs/9raayTRwO/l1ZCb5bmNpayXy+7lJsEyXKNcOn2C6g71UtfK8JvRVGNXZxHl4lrixHpO5sE1ue7HqG56KkN4JuZ3K9v9LcMT2oiDytLMr8lEoHNhfl/yttyFXtJWLw+jiGD+hkzH2/P1hfyttAE/IQKmi6nuR7iK6gavu4mg8LO5nfUbVst+X0kEh5OI9m0+cal/Xqal3EB4MRFUrSFOCMvY67syL8fVvluV85cD/RziBKlc4p5Jdef+LURdWUOUo49TXXYvN9ROyHx8bS0fH8tlrSA6HZbnci7M9ZQnBSzKvCxDO0o9K0+VKTfW3Z3z/ZaoL09T3UTpVO3LbKJtKembRHUfxGQikP0g1eXoezIPHiKC3plUx4vypJUyvKiMYS9DuP5B1OuZuW/KcaRsx4NEuV1OtHXTM1/KkJ9yY2iZr1z2X1n7XH5bSnVz2EOZ/sXEJfnVtfSXMl5uuCvHqHI/zy219SzPbSrrup8oj+UmvzJE8yGqcfZlCN8yqqED12c9WJbrLsMWplHdDFr239xcbhlr/F/5d3ambSXVgwc+nsu8jOqpLG257I8SZWkpcVxaRtSN+VRBaBlSOIJqONQtOU25yfcnVO3nqto61uYyFhJXRSHu0SjTriBijRFU5WQJ0Z49mfOtIsppKYu/IOrKeOK4N4coH08RweA8qpuop1Ado+/LfH46py3DsNbUpnmMaO8XE8Ne5hNluwyTmZ6/rSTqxyqiHflfopzcRTXG/nGi7E7J7b428/ExqiEcpYyWY2ApUyXvyjCc6bmt/8ztG0fEByupYpYluW1ttfyckXm1Ntd7DlFmlhDHiFInyv0ni/L3RbmPS7kuMV4ZPjudHBK4UTFqVwfJGxlQD6m96nfSlgPLcqqx0GVMzYGZSWUczz/y952pxhodSjV+9j/yNY0IKsprURaQcld2Ge91A1Xj9FGq53CWO1JXUAXo5ekAW1E9LWIB1d3cQ6nGXA/PdP+Mqpdmen53Whasx3O6oURPTOnpmUBcZi9PmziF6pF3C4mTkdPyt35UY7LGEz16JXj7Ta0gv5fqyQ6jiROYNVR3ZX+FaGjnZn6V4Og/qMZSP0H0ppfHVZWbINcSjfH7qe7WLjdvrsk0P0515/hYqsZnbG5HCdR3pnp808FUDdf8zNfBuc+7Ezd5rK3l0aNUweDC3KbpVE8GGZ3bWJ6mMYSqfC0heo7GUTUA5a74ZUQjNoMoD4Oonq4ynggM1uY0pXd/BdFAnEoVeJeTvvk5TRlTOz6Xd0quu41q3F6pJ0NreVbSXO4kX57LKPu7BLml/LRRPeqsBHBXUT0msjzh4x6qRxxeRDUmfhnVid+dVD2MJR1PZdp/k+taTPR47JzL60715Jr57fK5jBEfz7MfTVUODuWkbhpRFlfnviz7voyJ/zjVWNFHqU6qyg03ZYxleWJH/T6HciI7kirgH1lLcylf86ger+eZZ+VkogSfJRBr49ljj8tTYsrTD0r5KwHPUKo77+/M/VNfxkqqDocSwDoRPJbgYwJVQPanLBuLiUCmDDtblPvxVqoxsItz28rBs7TLbUQP3epc9liq8cRlLPus3E9fpBpfO68WeA2vldGniLq8O1W9OoGqrRyV+7WN6LUkpx9CBDW3Uz1C72yiHRxG1VYPzWWeUts3i4iy2DPn2yfzcRnVOP6FVIHXU0T5Ke1vORaV41ZbbV8tojo2jMttupxqfPti4l6SnkRP3OO1fV2e7tOXaCceItq6eUQ5fUPmjd5WhlMAABr5SURBVOf7N5R5a/lZtrs80eIUqhtMF2a6z8y0fJTqRH8IUbbXEM/ILssrvf6Da3nZOz+Xcr1z7oMVRPtc1j2euLJYTtA/lds9jOppPiuIsvcfRJmZTnUzXWlvy1Cr8jSd5VTH51upTn7fm+nqltP9qrbuz+W6yxMwPp/pKO8XUvV4/5WqXL4686D+dKIVwLsyj0YTZa7k3Vii7SlP6VhO9PQ/TRVflDZ5IdXxsXump9yvtYxoT4dmupbUyvM8qvZyaubZJKoOj3JloN52lvat1KVSZkqbU2KgUnd/QXWStIoqVihPsXoiP48iyv3k3Ma/5/ZMIToZ5hJ1YgERuK/N/Cht9bBMY6n/azPPn6Q6VpW8fbpezqnK0BKqJ3oNyfSXsjiyto8HE/XvFKoY786cr2cj8WqrPS1kT+KB+mdSPZVhCXGGsjq/O4soPBOIMUnlQFVsR1wOWUAEfTOIMbM7EoXtImLn9SAuG+1MVLRuRCXoSRRuI3bEh4hKA3HJdVz+vo1V/9nJ869R3WW8Q343gwgyehCX2svl2rcQhe19mYbSS16ezgHVkyTWejxJolSSle7+qfwNj7GW04mxkx8ihqbcSox/LHfozyCCyTtzuQvd/QNUAdO7c77biUJ3MVEAdyAa0K8Tl6q2pnrUUTeqp610J27GGkNUmJVEb1kJFC7Lbd2K6N27m+oE5EO5vL7Epam/Ud2wdBJRHtYQvRcLiN7Fxbkt04h9/mcz60f1MP2XuXvpDVqZ6zuKKBNPZJr75zbMoRp7vxdVr1S3LF8lgPwaVYAxiXjG89+JBu7wzKeHMw+/luteRt6Z7u5fqZXLmcTlsHKW/xqifG9H9ezdOcRB9fJcXumRbiPqxklEA34mcZlrBTH0YHyue/vczmVEXfkA0YhPzWUdRJTnhUTwMyd/W0s07LsSZdSIsa9vynUv9BhzP5G4tD4rp9ua6vnNg2t1c6m7353rH5b5VS7ZdiPqY0+qZ5R/jao3tQTSb8s8fxtR97sRPX+lt2zHXMZqoEfu+3Kzc5/cntJLe33mV7nKUZ5scnbmZbnzfwwxDKj0qp6a62rLclDaje5ZvsqJ0BeIXsvtcp1HUp0ADCPq1Eepemqfdvd9qR7ZeD5Rd/+LqDtlH88lDlo9cntenekowd8huY4RxEn1w1RXt0a4e9/cVyvd/YwoGvG0II9xkROo2r23Uj154t+IS6yTcvvOJMrNTKLcG9WjDMuztrsT7UdPol0/l2hf1gLda21nW74vJx7dcl92A/AY1+2Z58tqdbo8haX8tpooz2VfPpz570SbQ6azW+6r8tShubk9JVB7P9VJVXkiyTbEU0Z2y7zpke3vHkQduJcIHM/MPPhqLneb/AvVjbEHUT2Cbj4xBGScx3CaD+a698l0k9+fkNv1l0zXtsAbMm/agO3d/f5M2465vmVEW1qUNnAC1c3Ly4iTtLXu/stcRzn5Lm18r9zuFUS73o0I+LbJ3/fINHQnHodbnjZTrr6WYQwLPIbRDc3Pp1I9O78t9+vWmdZyDC7vIerq7pnmcqPkCZmno4g25QgikFtEHM9eShyDFhA92mNzfa/NZawl6vhpRNtd3pcgrY1qGKe7+yNUj4AdS7R1PYieXnK68o9jSuw1jBgK0SPz8OW5bd0yTz5AdbWoHB9vznVvk9O1UZ0UtVGV56NznV/LZS6huvJwLDH8ZGEtv95OdVP9mnXUpaFELHJR5ufW5DO9c/2zMg3vJurWZTnfVkRZODzzehfiht8bqcrFjzMtk4jy/avcttG1dJTAeS1VuRmZeWeZlp75t/70q5LfR+V03Yme8W6ZnplErLAVEVOcRhwTuudx6b1EGbqcqlxuWFf3Rm9kz/UvqZ7mUJ42MTILxt/zu/KoqsVE438p1XCQW3PnlN6Qr1M9zL5cViwB3YVEEPeK2vpWEXfClst9/040RiuInoplROUtl3hLL1MJ4MtTM8oYsnKneOlVmkpcrr2TqMinEmekK4lKU04axuUyS8/KBOISSgnWbiJudppO3HRCLmtMru8KIoBYTRSuWbXfH6IanlDuqL4n87Bsz1qi8b0/l3EcUZn+RvVUg1PztzuIYLKNquf8vnbr/neqCl4e23Ul0Rj8vLb/X0RU/IlEBVpANUxkDXBzTteLqOyTqXroxmeeTaZ6TvE4opwcnfM9QvUczV7Epa1ykHGqgO6h2nIm1vL8rVTDHSblcvYleqrn5rZfmvn0w0zbZKoe6Z9lXl77/9u7/mC/iur+WQgoDI8YiCFQoFNsrYEmdYQCNbY0RtPya7Qjo3awnXZaOjITSAfaWi1jrWir0+IMI9WOFmeojohBnWeREGQgMJA20BpIgkkTSNK8R/qgeSQ+AyTkx/aPzznfPd/97t67974vhszcnfnOu+9+7+45+zk/dr+7Z89FmHTrCpLqxLcRwol2R+09L7ztRbCTNeDgCsHqaamj+Y2fQwjN2IqQs3smuGryPDhwHZB2N4AHmpZJv54VDHYihCc9CG6HbgbtaKXB81mEU+w/Ebn9q/Rbn1V92AEOJLq1rBOeb0o9fXHA7ZC3NiK8OGUruIL4DMKKmq6cevn+YXAAehrcan0BYXv5TIQ0mkr7TuHzR4LrGnCw/KZg+/fSZ13hm5A+ToL6/CKA240+nwj6i0cQbO0Y8Ifry1JXJ9Sa2k37ugD0GZMRH5rCbpm0uwkhNOZqQ2MZOBn7AcLq84Xy/Uvotyv1l+9HWMHbhbDKrVk6HkSw/yeFnxkIK/M/Mm2sBmNFN4B6c43U2y40tyGsuG9D2M35KUJ4xEuC63OgH3xB5PMNhEwbmllpN8I5gwekLQ3rmgJXHw8D+Bfh40Po35XRXQPVBV3l1JCO5xBCP3RH6A6R0UxwsqA7WofN34/4sMK+QfB6ACGk8Tsic7X/yyA7mNHYOAO0o0PgJGY7QpiBl/7vRDjvstfcf1Xk9UmEHTtd1T1B+jVT5He7PK/ZIQ4jvOBpA/pfgKPjyE7B56ciLw0x0p2iF0Ed1lXuS6WvKxFe+nOfyGOp9PdhBF+9HWEXVcPLngQnaU8i7JguQFgcWCH8P4Tww+Z3IFk4pN8Pgz9EZxgcNbTOI7xIR8OkvoKg65rJZw9C5op/Rxj/NSx1E4Ke6Y6W4rZG8FK5/Q9CKIra3mrQzjVkTm1kBagP/yt9PAja+w4Ef/E1wfUQwur8LuHnccF2qfz/ZTD+WnPW2xA69aOXgPL/tPC1H2FMuEva+wPBbTk4Dr2KMI7tQP9cYLu0rRhMIMRubwffsLpNvtMQFQ1B0tBflZsuOu0B9e4xwWav9E0X1g4KvUPgjzqAevmIsbUTSuarR/uBxtRpbb33pwgvbvkn8NfhR8BfhlcC+EXv/a3OuTOkuRngwcZfAgf5c8FB7FrISWwXcmbvBZPLP6/1PbNqzAJXlXRbfhycAHhw4PoFcPV1Paj8I+DKl+aHnS/PboA5yeyYNeI6AKd4k4XDOfdmcNviOoQXzywFD4T9Eego/9xLRozEiem7wK3YJd77Jeb7VeDkax2Av/DeH5JT0osQYqN/D1yteh+o5O8HD9M8Cq6C3Oqcmyv9/Tnw16puK3tw4PkwgOuEtsrhJNCA3gMJjfDRiWg5cPIhcGX2auHjFqmzABxArwKdyztFJh8VeY2ABzS8yGeml1PlzrlRcJL6Di8n7+X594IOdyXoIC8E4yZ1oviJCPMVAM5VWTme9P8+eNJ/LcJJ/8WgYzgbdEx/DeroI2AO4zEwC8fvgg7nCpHvElA/ZwqO2t4ouL210NB+g5csCc657yJkTvkWuFKxRHC7AsCvee8/E2E9InxcDsr/l733v++Y5UZTt42Ag9iUYPte8ODvUnBwutno4AgY3/ZjhLclvgVRBpfIrt4Gro4+BU5e3wM6xjfZvkr754ErodeCun0T+Ar7x43sZ4Fb4s9LnUmR7xfAlRLFbiW4ujMF7oj1ZZoBwxdy2P4h6D9eAAeb+ejPpvF1wfGwtH0z6JNGBNsloB87Wfh9CEy9Ndv09Wzw8NxSw8fXjXwWg6tb5wvvfwNgkaHxG8LvEoSXeFwC2p3a1c2Cy4vSn1VgJqKPC7YOwFs9M7u8HUyn9jnB9Cww3OaT4EGte8EDy18AfdXnhf8PepPJw4UsF72sOI6ZFt4leDwD2tktBvNvCI6Xi3w+A9rVveDE6t1gCskLQLs5VfB9ECFjzaPe++8YPpIZJsCJybNgyMuxYKzpQHYbZ7JDSHsjwvtNoI5uAl9IsdUx29M7fMiK8y6R+xyEdLNPiC8+E9x5+zdExfVnZboK/MH3JyL/E6SfLwgen5L7p4Nj2jiAewXvWH5vB/3XV0Eb+JK0dzVoyzuMjZ0rfD+OsOo5Bk6sFkPsUXi8BPR5S+VZzaah2P8Kwm7ljZDxWOR2qg8ZlE4z9rwQ9KHvls8NRue/B+rc7aILE+ACwmqD7UHv/YQcUD7Ve7/KOTcuWB4H+vvPIYSKXYKQDu5t4Nio9rMWnNyfJ7gsBn+krwB/wP8ZqFv/h5A2cws4+TsLtJV/FtmoP3wT6Os/Ba6yv1lk+AZwgr8Sg3q7E7T38wD8bWRvC733jwluFwH4oTeZqFzIFPNWkcVvgukpfwvAtd77kwTbC+T+B4Un3bV4H7jje43vz2z0x4LTR8F510Lv/SfMXGAS1DHN5PES6Ee3g7ud6yPeTgfnLN8Cx/crEHa8TgbHjmdBXfxtcBfqu5A5hvjTW8B50V5wRb0v60qjMp2V5NfzBzT4nwcH5FngFvYpoENbkLivn6dNvY3y7AZwYnyOffYI9u2kwr5W8jkNjGw9i9FmcACsxastzxVtbMI0ZRXJvpKnJvxHz9rnLM4p/u09e73F4HxOrq8FenJWC3nncK5sY9iyf41kuR4N7GA6fTLP7wMHDCvXjZGM7XUTu9oEhhM11am6632GT223x5PhY16kM7be5kQblTqAFn7P8KE4/9jwW+wjLG1wASBnS6U2GOtaUb/j9hr4yPUImSBi3T4l5j+qNy3fmsGrUb+NTc8raQP1dlApq4zcYn1Othc9W6QbgtEpGXoDbSTkmsQi16c6ncrpmfC5CenxIDcuxX50AI+mfOZ4TulSAufYBpPyKfEFlc81MdQj/REB/Qf4y+NucBVBf3m+au7tB3/56QnZcYStOI1bju9reMX1CKfwdyFsKxwwz+pJ3xztuxHeWJa7/gp4QEX7tB/cKnnCfm/6bduIaT8GxgTp1v4++atZLnJ42XoWi30ZjLTfesI6xkgPZZXglaOtz44JHntRJmM9QR/TVrlanLUNS2MMeZ3RT472GMr7ap+1OKf41xPUvgZnDXHZJn+1rwdBPZoleqQZb7R/vkbe+rH853DWNl5F2BZWLBS7FM4WjyayStlViX5NgW9PBDjZ0gN2GjZgQ4nq7KCpPqcw0FCjWK656wHdNv7xcQT/pToV1yvRqZLrWP/0YJL6m8cQwjqszhT3Ce393jb0Z3k6gJAlQXVeaethYpXVkgjPAVsCV9GsLW1FCGXp0ZY2dqB/3FKeVdeqZDk/Q1v1VseJWbZeNGaqPnjDp8aSq27b+ypDaxN142BuPFPbVR+S8q0pm7d9tTpgdSrlcy0fNptObAd69iHrLxM45/Q51d715tmkboDhSwfAHwwXCTZbETLo5NpIyWcrgv4P6F98Lf21eI0l8B8zfOph1asRfMhhUCdKxqWUrsV4DPBZwPNCkfHLKQxz7SHohtqg+oQBPUrZlW2v6nO0HWj8ErgVMh/c6pwDbg9/H+Hk6BwQ4O9JnbsR4pRng1tCWxL3Pw1uO/zAez8PPPg123t/PLgtswaMd5uNcBAyR/t8cPvoyorrzeA20dekT/8NbqvMke/nAljgnPssuI2ucU+LwK09S/sRSMYPz5fQbJS/28FwjRxevXoRFjdlMNJ+LwK3hg+DqfeOB7DLe3+s994V4pWjrc9eKXgcVyJjz+3ypxK0Va4WZ8XC0rgSaZ15Anzj2ZYcbXBrMOY/11f7bA/nDP+ngqENuytw/ph8boO8bRLUl3Vg/NlcABtFj2aAju4/EdIx5eRt+231K4eztrEH1Ln1BosL0K/7OeyayCplVyX6tQrAGwWPUdCu/gHc2vfgxE35r7ODpvqcwuDbKbnmrpHQbefcZ51zN4AxpjPArXtNs7UbDXWq8DrWv4+BOnUH6Kf0oGufbhf0ycqn2O+Ber9L/q4DsF7CvBaDg/waMJRlP7hNvwAMGZsFhovMBidl9wieo+BW80MIW8vvBPVVX+KitrRevv+ifG5zzt0gbcyWfjxlcJmJELc8oJeG9ipDey44IThZ2pkA/fpmAI86596iOuCcG9WP9H+z4KR83gb677+K+NcY2DvQbxM9PUHar/fGKzCG/yl5dhFCRpDceLwKPIBp61mcrQ70dAppn3uX4WMuaAcvo98OVFe/iJBFKSfj0RRto8+rTXvrTHuqc7FuWL28R2QyCeqxQ8jSdU9FGyn53Img/z0azrkNAObG16a/66S/ZyTwP0Ns8E4wLOkBMMzyFdCH3Co8l4xLqms7TV96eOX4LOB5ORiiutdgeJtiGLWxQa/BMBO1CT1LlNRn61vFpm9EOBRcWY62yfWI9/4+zxPHU2Bc47lg/NQYOAh/Htyq0UNnF4LxbVPe+z3e+38E47ty9+9zzl0sdQEAnie/m9A+CBro8opr5c9mhNB2l4MxQVMISdMPIGQHeSWifRUYV36i8O4L8erVK8Eo4u9R0Lm1xStJO6KheNXKWPsd0zZ9yeGckkmv3+BhwGvANxHW6ldbnGv4/0AFzn8HThBGzOdEMDbegfG094N6NA+0+fvlmUM5eUf9HtCvCpxntsG5haxS1yX6ZfGwdvVf4EDco11gB231uQ+DjFxz1ynMP47wAgnVgfshWTVa6FTRdUL/TgQHUMVzHwZ1u7JPFfKp9HvgBOpY4cNmedKsForHPjCbx3rPDCiHvPe3CO2LELKxXC7tHS90HRivvxWMbbW2dFmE/Yjh45gELivAyXlOL1O0td4IMuOWwSDu/wioeyk+Y/5jXl9Gv56k/Lodr05HyKx1AJygnoS8v9R+23oW59hG9yXaUD5uACeHP0Hwd1PotwOrr4pTTsZJ2maMPT/RVqxzFlv7jGb0+HWEnZTLpN5xFW2k5GPt39LQTD3xtWb5WIKQNjDG/5BgqOn4NI3oAdE7PTzeZFw6U3hzhXzW8TxHaBxEfyaZhQhZy7SNeWAWJm1PbaI3DiI//1JbH4HJVFRb4qXs1/MHPDQ1014jpKKaNNd7EE7S63bCi1FbVfe3gCvXbWnvB5VxQcX1lqjepGl3AWjse2I+5ZnxqJ621+t3Ic+2Xi1GEX9bojaa4pWkHdFQvEpknOy34SeHc0om9r7tR61+TQPnOv5zOPcy2sTYgw5PszKkaIzn5B31u1a/potzizZS1yX6ZfHooy149doosIO2+lxrPyU+MLq3I9UGwvZusU419Mk2o1IfDYTt5koaka4l5YN6v/cKuLPSx5PFwPD0pPl/W0T7GfT735QtTaDfDlYDmMhgM5HBXl/ClNLLAdq5epEstV7KJ4zV8VkiQ6T9uqWdst0J5P2l9tvWszinbDRnjz07wKC/G7P0Ito5Gedoa18G2krYncU2thWld2NEe0NFGyn52H7HPE1k2hg39Pr8HoLeKs6K4TaYMQOMrW4yLll6JXyW8GznUbbeM7YNWy+B3bjFFoPzr77+2TYqfWNbp3okPuCvqYvBtDF6radRV8s9vT4bwFdNvVEAyxLtpe736rak3Wu34vpshJQvtp7SuAfchkm1camlbXlO9DvLc1SvFqOoPUunMV452ol6o4UyTvY74ieF84BMon4fjp6t1K9p4FzJfwXOy8EtrbivF4NhDb3vEzQurZD3YcsDavRrCDg3bSN1XaJfFo8+2uBq2CHbRo0dtNXnWvsp8YGRDvxqzLP2qalONfTJMZ5WZ04roRFhlJQP6v2e9Zd9NqF8GBlPgitWsXzuBcORrP9VO7C2dFMkv+Xg6laMrd7vs6VI11J6OUA7Vy+ip/XWYtAnnFbAZ60MMajP8XgV+2rFLucvtd+2nsV5wEYr7HEtjB1ENE8zfZ2doJ2ScY62YpFqK37WYmufnwQzaa019JaDoW+jFW0MyAfG/hM8zUtcqy1Z2tbn2md3g2F6tt4yMNznLxuOSxbbEj7reJ4Ew6dinq0d6/1evYQtXYq8Pvf56tifVPrGtk71SHyQzlpgT6Db0/YlmQxy90tOH1fRLjl5X3d6f3NNG5Z21Yn9Kp5tvRKMLH9NsxaU0rY0Un3NyTjXb+1LDudcJgbL83wMZnPI6VdbnOv4z+Gcyoph+5fLeFMnb9vvEv2aLs5N22hiE5bGxkx7KXuss4O2+lxrP4U+MIXXtHWqoU+2+lfsHyr6lJNPWxnnsjmkeLY+d3OCXi7DSVtbyvGcot3EV5fQa+XjC7BrOh5rv3M4l/iNOnu0dpDKBtaUtn5KMovV+Z6m+pWST87fp/S5ZFytm3uUjP9NxqUcn215rrPjEl9dnPks9zmq8lw7564DY0DPAWPjTkZ4SxbAX6AaD3NQ/o7LcyMIyf6dfJe7DzBG8JyWtEuulb8p4eE48JeYtlvShpaD5npceS/g2dZTPqowsm1YfIHmeOVo22dTfc3JuMdDRFv7ov2owjmlM074Knk25r+ur0mdy/Df94z+45y7Hsz9WtLXJjZh+12iX9PF2Tdso4lNlOhXyh63JXBpKuM6ekn7yZUCzC3PvXab6FSTEulfsX+o6FMOr7YyHuADzFGf4rmOXtvxJWdLTfxeE199jLRRRa+Vjwdze9fJSktbf1k3Zjb1udZn5fS1CW3FIqdHJXwOA69hziHazl9y43+Tcakp7bY8a/mZ+OojvhrdcrXky/F16l5VvZL706Vdct22Xop2W56bYjRMvJpg0JSHtjinnm+qX21xHoZetulfSb9/Fjg3bWOY+tUEl2HTa/ppwnNbnWrLT1sar6WMh0lv2LY0jL62pfdaYdcUr7p6w7DHYdOeLp/DwKsJjen0u619D5PPYfDcVj5tPkfVynVXutKVrnSlK13pSle68nou8XJ5V7rSla50pStd6UpXutKVlqWbXHelK13pSle60pWudKUrQyrd5LorXelKV7rSla50pStdGVLpJtdd6UpXutKVrnSlK13pypBKN7nuSle60pWudKUrXelKV4ZU/h9Ix8JTQUt9ogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_ = XGBClassifier(\n",
    " learning_rate =0.01,\n",
    " n_estimators=3000,\n",
    " max_depth=3,\n",
    " min_child_weight=4,\n",
    " gamma=0.3,\n",
    " subsample=0.65,\n",
    " colsample_bytree=0.75,\n",
    " reg_alpha=0.00009,\n",
    " objective= 'binary:logistic',\n",
    " nthread=5,\n",
    " scale_pos_weight=1)\n",
    "\n",
    "modelfit(xgb_, scaledX, Y, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "soumission16 = xgb_.predict_proba(predscaledX)\n",
    "np.savetxt(\"Sub_16.csv\", soumission16, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CashBalance_rel_CreditLimit_month_1', 'Year-2012',\n",
       "       'CurrentTotalBalance_rel_month_12',\n",
       "       'CashBalance_rel_DelqCycle_6month_mean',\n",
       "       'TRANSACTION_AMT_REFUNDED_month_1', 'CreditLimit_sum_rell',\n",
       "       'CreditLimit_month_1', 'TRANSACTION_AMT_PAID_mean_log',\n",
       "       'CashBalance_sum', 'TRANSACTION_AMT_PAID_sum', 'CashBalance_mean',\n",
       "       'stdMultipli', 'TRANSACTION_AMT_PAID_rel_CreditLimit_month_1',\n",
       "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month_12',\n",
       "       'CurrentTotalBalance_rel_month_9',\n",
       "       'TRANSACTION_logAMT_PAID_month_1',\n",
       "       'CurrentTotalBalance_rel_month_10',\n",
       "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month_11',\n",
       "       'CurrentTotalBalance_sum', 'CurrentTotalBalance_rel_month_4',\n",
       "       'CashBalance_rel_DelqCycle_month_1',\n",
       "       'CurrentTotalBalance_mean_log', 'CashBalance_mul_DelqCycle_mean',\n",
       "       'TRANSACTION_relAMT_PAID_month_12',\n",
       "       'CurrentTotalBalance_rel_month_8', 'TRANSACTION_AMT_PAID_month_12',\n",
       "       'CreditLimit_std', 'TRANSACTION_AMT_PAID_rel_CreditLimit_month_2',\n",
       "       'CurrentTotalBalance_rel_month_11',\n",
       "       'TRANSACTION_relAMT_PAID_month_11', 'TRANSACTION_AMT_PAID_mean',\n",
       "       'CurrentTotalBalance_rel_month_2', 'TRANSACTION_AMT_PAID_month_2',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_1',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_12', 'CashBalance_month_1',\n",
       "       'DelqCycle_sum', 'CashBalance_min',\n",
       "       'CurrentTotalBalance_rel_month_5',\n",
       "       'CurrentTotalBalance_rel_month_3',\n",
       "       'TRANSACTION_relAMT_PAID_month_9', 'TRANSACTION_AMT_PAID_month_3',\n",
       "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month_6',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_11',\n",
       "       'CashBalance_rel_CreditLimit_month_12',\n",
       "       'TRANSACTION_relAMT_PAID_month_3',\n",
       "       'CashBalance_rel_CreditLimit_month_6',\n",
       "       'CurrentTotalBalance_month_11', 'COUNT_XFLG-Q_sum',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_6', 'CurrentTotalBalance_std',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_7',\n",
       "       'CashBalance_mul_log_DelqCycle_mean',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_10', 'CashBalance_max',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_5', 'DelqCycle6Month_pond',\n",
       "       'CurrentTotalBalance_rel_month_6', 'TRANSACTION_AMT_PAID_month_8',\n",
       "       'TRANSACTION_relAMT_PAID_month_8',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_8',\n",
       "       'TRANSACTION_relAMT_PAID_month_5', 'CashBalance_month_12',\n",
       "       'TRANSACTION_relAMT_PAID_month_6', 'TRANSACTION_AMT_PAID_month_11',\n",
       "       'COUNT_XFLG-Q_mean', 'CurrentTotalBalance_rel_month_7',\n",
       "       'TRANSACTION_AMT_PAID_std', 'CurrentTotalBalance_month_7',\n",
       "       'DelqCycle_month_1', 'CreditLimit_reached_month_12',\n",
       "       'CurrentTotalBalance_min', 'CashBalance_rel_CreditLimit_month_2',\n",
       "       'CashBalance_log', 'TRANSACTION_AMT_PAID_month_9',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_3', 'DelqCycle_month_12',\n",
       "       'CashBalance_rel_CreditLimit_month_3',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_4', 'CreditLimit_month_6',\n",
       "       'CurrentTotalBalance_month_4', 'TRANSACTION_relAMT_PAID_month_7',\n",
       "       'TRANSACTION_AMT_PAID_month_6',\n",
       "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month_3',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_9',\n",
       "       'TRANSACTION_AMT_PAID_perQ_month_2',\n",
       "       'TRANSACTION_AMT_PAID_month_10',\n",
       "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month_5',\n",
       "       'CurrentTotalBalance_mean',\n",
       "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month_8',\n",
       "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month_10',\n",
       "       'TRANSACTION_relAMT_PAID_month_4',\n",
       "       'CashBalance_rel_DelqCycle_mean',\n",
       "       'CashBalance_rel_CreditLimit_month_4',\n",
       "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month_9', 'DelqCycle_min',\n",
       "       'TRANSACTION_logAMT_PAID_month_12', 'CreditLimit_reached_month_1',\n",
       "       'TRANSACTION_relAMT_PAID_month_2', 'CreditLimit_month_11',\n",
       "       'TRANSACTION_relAMT_PAID_month_10', 'TRANSACTION_AMT_PAID_month_5',\n",
       "       'CashBalance_rel_CreditLimit_month_7',\n",
       "       'CashBalance_rel_CreditLimit_month_5',\n",
       "       'TRANSACTION_relAMT_PAID_month_1', 'TRANSACTION_AMT_PAID_month_4',\n",
       "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month_7',\n",
       "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month_4',\n",
       "       'CashBalance_rel_CreditLimit_month_8',\n",
       "       'CurrentTotalBalance_month_2', 'TRANSACTION_AMT_PAID_min',\n",
       "       'CurrentTotalBalance_month_5', 'CurrentTotalBalance_month_3',\n",
       "       'TRANSACTION_AMT_PAID_max', 'CashBalance_rel_CreditLimit_month_9',\n",
       "       'CreditLimit_min', 'CreditLimit_std_log',\n",
       "       'CurrentTotalBalance_month_6', 'CurrentTotalBalance_month_9',\n",
       "       'CashBalance_rel_CreditLimit_month_11',\n",
       "       'CurrentTotalBalance_month_8', 'CreditLimit_month_12',\n",
       "       'CreditLimit_month_5', 'CurrentTotalBalance_month_10',\n",
       "       'CreditLimit_month_10', 'CurrentTotalBalance_month_1',\n",
       "       'CashBalance_month_6', 'COUNT_XFLG-Q_month_12',\n",
       "       'CashBalance_rel_CreditLimit_month_10', 'CreditLimit_month_9',\n",
       "       'CreditLimit_month_8', 'TRANSACTION_AMT_PAID_month_1',\n",
       "       'DelqCycle_mean', 'CashBalance_rel_DelqCycle_month_12',\n",
       "       'CurrentTotalBalance_max_log', 'TRANSACTION_logAMT_PAID_month_3',\n",
       "       'DelqCycle_mean_log', 'TRANSACTION_logAMT_PAID_month_8',\n",
       "       'TRANSACTION_AMT_PAID_month_7', 'CashBalance_min_log',\n",
       "       'COUNT_XFLG-Q_month_9', 'CurrentTotalBalance_month_12',\n",
       "       'CashBalance_month_11', 'CreditLimit_cat-[10000, 15000)',\n",
       "       'TRANSACTION_AMT_PAID_std_log', 'CurrentTotalBalance_log_month_11',\n",
       "       'COUNT_XFLG-Q_month_8', 'TRANSACTION_logAMT_PAID_month_6',\n",
       "       'CurrentTotalBalance_log_month_5', 'CashBalance_month_7',\n",
       "       'CurrentTotalBalance_sign_month_1', 'CreditLimit_mean',\n",
       "       'COUNT_XFLG-N_month_1', 'TRANSACTION_logAMT_PAID_month_11',\n",
       "       'CashBalance_month_5', 'CashBalance_rel_DelqCycle_month_6',\n",
       "       'CurrentTotalBalance_log_month_12', 'COUNT_XFLG-Q_month_5',\n",
       "       'CashBalance_rel_DelqCycle_month_4', 'CurrentTotalBalance_max',\n",
       "       'CashBalance_rel_DelqCycle_month_7', 'CreditLimit_max',\n",
       "       'CreditLimit_reached_month_11', 'CurrentTotalBalance_log_month_4',\n",
       "       'CashBalance_month_8', 'CurrentTotalBalance_log_month_7',\n",
       "       'CashBalance_month_9', 'CurrentTotalBalance_log2_month_1',\n",
       "       'CashBalance_rel_DelqCycle_month_9', 'CreditLimit_month_4',\n",
       "       'CreditLimit_month_2', 'TRANSACTION_AMT_PAID_max_log',\n",
       "       'TRANSACTION_logAMT_PAID_month_2',\n",
       "       'CurrentTotalBalance_log_month_2',\n",
       "       'TRANSACTION_logAMT_PAID_month_9', 'TRANSACTION_AMT_PAID_min_log',\n",
       "       'COUNT_XFLG-Q_max', 'TRANSACTION_logAMT_PAID_month_4',\n",
       "       'CurrentTotalBalance_log_month_9',\n",
       "       'CashBalance_rel_DelqCycle_month_10', 'COUNT_XFLG-Q_month_2',\n",
       "       'CashBalance_rel_DelqCycle_month_8', 'DelqCycle_max',\n",
       "       'CashBalance_rel_DelqCycle_month_5',\n",
       "       'TRANSACTION_logAMT_PAID_month_5', 'COUNT_XFLG-Q_month_11',\n",
       "       'COUNT_XFLG-Q_min', 'DelqCycle_month_6', 'COUNT_XFLG-Q_month_3',\n",
       "       'CurrentTotalBalance_log_month_8',\n",
       "       'CashBalance_rel_log_DelqCycle_mean', 'CreditLimit_log2',\n",
       "       'CashBalance_rel_DelqCycle_month_3', 'CashBalance_month_4',\n",
       "       'CashBalance_month_2', 'COUNT_XFLG-Q_month_4',\n",
       "       'CurrentTotalBalance_log2_month_4', 'CashBalance_month_3',\n",
       "       'COUNT_XFLG-Q_month_7', 'CurrentTotalBalance_log2_month_6',\n",
       "       'CreditLimit_cat-[25000, 30000)',\n",
       "       'CurrentTotalBalance_log_month_3',\n",
       "       'CurrentTotalBalance_log2_month_11', 'DelqCycle_month_7',\n",
       "       'CurrentTotalBalance_log3_month_1',\n",
       "       'TRANSACTION_logAMT_PAID_month_7', 'CashBalance_month_10',\n",
       "       'CurrentTotalBalance_rel_month_1', 'COUNT_XFLG-Q_month_6',\n",
       "       'CreditLimit_reached_month_6', 'CreditLimit_min_log',\n",
       "       'CashBalance_rel_DelqCycle_month_11', 'COUNT_XFLG-Q_month_10',\n",
       "       'DelqCycle_min_log', 'CashBalance_max_log',\n",
       "       'CreditLimit_cat-[0, 5000)', 'TRANSACTION_logAMT_PAID_month_10',\n",
       "       'CashBalance_std', 'DelqCycle_month_11', 'DelqCycle_month_5',\n",
       "       'CurrentTotalBalance_log3_month_12', 'CreditLimit_month_7',\n",
       "       'CreditLimit_reached_month_7', 'CreditLimit_reached_month_9',\n",
       "       'CurrentTotalBalance_log2_month_5',\n",
       "       'CurrentTotalBalance_log2_month_7', 'CreditLimit_reached_month_2',\n",
       "       'DelqCycle_Mean*Max*Min', 'CreditLimit_reached_month_4',\n",
       "       'Year-2014', 'CurrentTotalBalance_log2_month_9',\n",
       "       'CurrentTotalBalance_log_month_6',\n",
       "       'CurrentTotalBalance_log3_month_7',\n",
       "       'CurrentTotalBalance_log2_month_10', 'COUNT_XFLG-Q_std',\n",
       "       'CashBalance_rel_DelqCycle_month_2', 'DelqCycle_max_log',\n",
       "       'CurrentTotalBalance_log_month_10'], dtype=object)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_list = list(feat_imp.index.str.replace('f',''))\n",
    "new_feat_list = [int(x)+1 for x in feat_list]\n",
    "X.columns.get_values()[[new_feat_list]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "importantFeat = X[['CashBalance_rel_month_1', 'CurrentTotalBalance_rel_month_12',\n",
    "       'CreditLimit_month_1', 'TRANSACTION_AMT_PAID_sum',\n",
    "       'TRANSACTION_AMT_REFUNDED_month_1', 'DelqCycle_mean',\n",
    "       'CashBalance_mean', 'CashBalance_sum',\n",
    "       'CurrentTotalBalance_rel_month_9', 'TRANSACTION_AMT_PAID_mean_log',\n",
    "       'DelqCycle_month_12', 'CurrentTotalBalance_rel_month_4',\n",
    "       'CurrentTotalBalance_sum',\n",
    "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month1',\n",
    "       'CurrentTotalBalance_rel_month_10',\n",
    "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month12',\n",
    "       'CurrentTotalBalance_rel_month_8',\n",
    "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month11',\n",
    "       'TRANSACTION_logAMT_PAID_month_1',\n",
    "       'TRANSACTION_relAMT_PAID_month_1',\n",
    "       'TRANSACTION_relAMT_PAID_month_12',\n",
    "       'CurrentTotalBalance_rel_month_2', 'CurrentTotalBalance_mean_log',\n",
    "       'TRANSACTION_relAMT_PAID_month_11',\n",
    "       'TRANSACTION_relAMT_PAID_month_9',\n",
    "       'CurrentTotalBalance_rel_month_11',\n",
    "       'CurrentTotalBalance_rel_month_3', 'CashBalance_month_1',\n",
    "       'TRANSACTION_AMT_PAID_month_12',\n",
    "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month2',\n",
    "       'TRANSACTION_AMT_PAID_month_2', 'TRANSACTION_AMT_PAID_mean',\n",
    "       'TRANSACTION_AMT_PAID_rel_CreditLimit_month6']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold # import KFold\n",
    "\n",
    "kf = KFold(n_splits=10) # Define the split - into 10 folds \n",
    "kf.get_n_splits(scaledX.values) # returns the number of splitting iterations in the cross-validator\n",
    "\n",
    "print(kf.get_n_splits(scaledX.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "train_preds_df = pd.DataFrame().reindex(index=range(0,11900))\n",
    "test_preds_df = pd.DataFrame()\n",
    "temp_test_preds_df = pd.DataFrame()\n",
    "\n",
    "kk = [1,3,5]\n",
    "\n",
    "kf.get_n_splits(scaledX.values)\n",
    "for k in kk:\n",
    "    count = 0\n",
    "    for train_index, test_index in kf.split(scaledX.values):\n",
    "        KFold_X_train, KFold_X_test = scaledX.values[train_index], scaledX.values[test_index]\n",
    "        KFold_y_train, KFold_y_test = Y.values[train_index], Y.values[test_index]\n",
    "    \n",
    "        knn = KNeighborsClassifier(n_neighbors=k)\n",
    "        knn.fit(KFold_X_train, KFold_y_train)\n",
    "    \n",
    "        train_pred = knn.predict_proba(KFold_X_test)\n",
    "        test_pred = knn.predict_proba(predscaledX)\n",
    "                \n",
    "        name = 'scaled_k{}'.format(k)\n",
    "        temp_name = 'temp_scaled_k{}_{}'.format(k,count)\n",
    "    \n",
    "        train_preds_df.loc[test_index,name] = train_pred[:, 1].tolist()\n",
    "        temp_test_preds_df[temp_name] = test_pred[:, 1].tolist()\n",
    "        count += 1\n",
    "    test_preds_df[name] = 0.1*temp_test_preds_df['temp_scaled_k{}_0'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k{}_1'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k{}_2'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k{}_3'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k{}_4'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k{}_5'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k{}_6'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k{}_7'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k{}_8'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k{}_9'.format(k)]\n",
    "    print('Did k={}'.format(k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_feat_k_df = pd.merge(test_preds_df, preddf, left_index=True, right_index=True)\n",
    "train_feat_k_df = pd.merge(train_preds_df, scaledX, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_k = XGBClassifier(\n",
    " learning_rate =0.01,\n",
    " n_estimators=3000,\n",
    " max_depth=3,\n",
    " min_child_weight=4,\n",
    " gamma=0.3,\n",
    " subsample=0.65,\n",
    " colsample_bytree=0.75,\n",
    " reg_alpha=0.00009,\n",
    " objective= 'binary:logistic',\n",
    " nthread=5,\n",
    " scale_pos_weight=1)\n",
    "\n",
    "modelfit(xgb_k, train_feat_k_df, Y, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_list = list(feat_imp.index.str.replace('f',''))\n",
    "new_feat_list = [int(x)+1 for x in feat_list]\n",
    "X.columns.get_values()[[new_feat_list]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score, log_loss, accuracy_score\n",
    "\n",
    "rf = RandomForestClassifier(bootstrap = False,\n",
    "    max_depth= 15,\n",
    "    max_features= 8,\n",
    "    min_samples_leaf= 2,\n",
    "    min_samples_split=2,\n",
    "    n_estimators= 800)\n",
    "\n",
    "count = 0\n",
    "for train_index, test_index in kf.split(scaledX.values):\n",
    "    KFold_X_train, KFold_X_test = X.values[train_index], X.values[test_index]\n",
    "    KFold_y_train, KFold_y_test = Y.values[train_index], Y.values[test_index]\n",
    "\n",
    "    rf.fit(KFold_X_train, KFold_y_train)\n",
    "    \n",
    "    train_pred = rf.predict_proba(KFold_X_test)\n",
    "    test_pred = rf.predict_proba(predscaledX)\n",
    "                \n",
    "    name = 'scaled_rf'\n",
    "    temp_name = 'temp_scaled_k_{}'.format(count)\n",
    "    \n",
    "    train_preds_df.loc[test_index,name] = train_pred[:, 1].tolist()\n",
    "    temp_test_preds_df[temp_name] = test_pred[:, 1].tolist()\n",
    "    count += 1\n",
    "    print(count)\n",
    "test_preds_df[name] = 0.1*temp_test_preds_df['temp_scaled_k_0'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_1'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_2'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_3'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_4'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_5'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_6'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_7'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_8'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_9'.format(k)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_feat_k_rf_df = pd.merge(test_preds_df, preddf, left_index=True, right_index=True)\n",
    "train_feat_k_rf_df = pd.merge(train_preds_df, scaledX, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_k_rf = XGBClassifier(\n",
    " learning_rate =0.01,\n",
    " n_estimators=3000,\n",
    " max_depth=3,\n",
    " min_child_weight=4,\n",
    " gamma=0.3,\n",
    " subsample=0.65,\n",
    " colsample_bytree=0.75,\n",
    " reg_alpha=0.00009,\n",
    " objective= 'binary:logistic',\n",
    " nthread=5,\n",
    " scale_pos_weight=1)\n",
    "\n",
    "modelfit(xgb_k_rf, train_feat_k_rf_df, Y, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_feat_rf_df = test_feat_k_rf_df.drop(['scaled_k1','scaled_k3','scaled_k5'],axis=1)\n",
    "train_feat_rf_df = train_feat_k_rf_df.drop(['scaled_k1','scaled_k3','scaled_k5'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_rf = XGBClassifier(\n",
    " learning_rate =0.01,\n",
    " n_estimators=3000,\n",
    " max_depth=3,\n",
    " min_child_weight=4,\n",
    " gamma=0.3,\n",
    " subsample=0.65,\n",
    " colsample_bytree=0.75,\n",
    " reg_alpha=0.00009,\n",
    " objective= 'binary:logistic',\n",
    " nthread=5,\n",
    " scale_pos_weight=1)\n",
    "\n",
    "modelfit(xgb_k_rf, train_feat_rf_df, Y, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_first = XGBClassifier(\n",
    " learning_rate =0.01,\n",
    " n_estimators=3000,\n",
    " max_depth=3,\n",
    " min_child_weight=4,\n",
    " gamma=0.3,\n",
    " subsample=0.65,\n",
    " colsample_bytree=0.75,\n",
    " reg_alpha=0.00009,\n",
    " objective= 'binary:logistic',\n",
    " nthread=4,\n",
    " scale_pos_weight=1)\n",
    "\n",
    "count = 0\n",
    "\n",
    "for train_index, test_index in kf.split(scaledX.values):\n",
    "    KFold_X_train, KFold_X_test = scaledX.values[train_index], scaledX.values[test_index]\n",
    "    KFold_y_train, KFold_y_test = Y.values[train_index], Y.values[test_index]\n",
    "    \n",
    "    KFold_X_train_df = pd.DataFrame(KFold_X_train.tolist())\n",
    "    KFold_y_train_df = pd.DataFrame(KFold_y_train.tolist())\n",
    "    KFold_X_test_df = pd.DataFrame(KFold_X_test.tolist())\n",
    "    KFold_y_test_df = pd.DataFrame(KFold_y_test.tolist())\n",
    "    \n",
    "    modelfit(xgb_first, KFold_X_train_df, KFold_y_train_df, predictors)\n",
    "    \n",
    "    train_pred = xgb_first.predict_proba(KFold_X_test)\n",
    "    test_pred = xgb_first.predict_proba(predscaledX)\n",
    "                \n",
    "    name = 'scaled_xgb'\n",
    "    temp_name = 'temp_scaled_k_{}'.format(count)\n",
    "    \n",
    "    train_preds_df.loc[test_index,name] = train_pred[:, 1].tolist()\n",
    "    temp_test_preds_df[temp_name] = test_pred[:, 1].tolist()\n",
    "    count += 1\n",
    "test_preds_df[name] = 0.1*temp_test_preds_df['temp_scaled_k_0'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_1'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_2'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_3'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_4'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_5'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_6'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_7'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_8'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_9'.format(k)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_feat_k_rf_xgb_df = pd.merge(test_preds_df, preddf, left_index=True, right_index=True)\n",
    "train_feat_k_rf_xgb_df = pd.merge(train_preds_df, scaledX, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_k_rf_xgb = XGBClassifier(\n",
    " learning_rate =0.01,\n",
    " n_estimators=3000,\n",
    " max_depth=3,\n",
    " min_child_weight=4,\n",
    " gamma=0.3,\n",
    " subsample=0.65,\n",
    " colsample_bytree=0.75,\n",
    " reg_alpha=0.00009,\n",
    " objective= 'binary:logistic',\n",
    " nthread=5,\n",
    " scale_pos_weight=1)\n",
    "\n",
    "modelfit(xgb_k_rf_xgb, train_feat_k_rf_xgb_df, Y, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "\n",
    "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=4,\n",
    "                              learning_rate=0.05, n_estimators=700,\n",
    "                              max_bin = 55, bagging_fraction = 0.8,\n",
    "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
    "                              feature_fraction_seed=9, bagging_seed=9,\n",
    "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)\n",
    "\n",
    "count = 0\n",
    "for train_index, test_index in kf.split(scaledX.values):\n",
    "    KFold_X_train, KFold_X_test = scaledX.values[train_index], scaledX.values[test_index]\n",
    "    KFold_y_train, KFold_y_test = Y.values[train_index], Y.values[test_index]\n",
    "\n",
    "    model_lgb.fit(KFold_X_train, KFold_y_train)\n",
    "    \n",
    "    train_pred = model_lgb.predict(KFold_X_test)\n",
    "    test_pred = model_lgb.predict(predscaledX)\n",
    "                \n",
    "    name = 'scaled_X_lgb'\n",
    "    temp_name = 'temp_scaled_k_{}'.format(count)\n",
    "    \n",
    "    train_preds_df.loc[test_index,name] = train_pred#[:, 0].tolist()\n",
    "    temp_test_preds_df[temp_name] = test_pred#[:, 0].tolist()\n",
    "    count += 1\n",
    "test_preds_df[name] = 0.1*temp_test_preds_df['temp_scaled_k_0'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_1'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_2'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_3'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_4'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_5'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_6'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_7'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_8'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_9'.format(k)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_feat_k_rf_xgb_lgb_df = pd.merge(test_preds_df, preddf, left_index=True, right_index=True)\n",
    "train_feat_k_rf_xgb_lgb_df = pd.merge(train_preds_df, scaledX, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_k_rf_xgb_lgb = XGBClassifier(\n",
    " learning_rate =0.01,\n",
    " n_estimators=3000,\n",
    " max_depth=3,\n",
    " min_child_weight=4,\n",
    " gamma=0.3,\n",
    " subsample=0.65,\n",
    " colsample_bytree=0.75,\n",
    " reg_alpha=0.00009,\n",
    " objective= 'binary:logistic',\n",
    " nthread=5,\n",
    " scale_pos_weight=1)\n",
    "\n",
    "modelfit(xgb_k_rf_xgb_lgb, train_feat_k_rf_xgb_lgb_df, Y, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Fold 1 - Run 1\n",
      "\n",
      "Train on 10710 samples, validate on 1190 samples\n",
      "Epoch 1/5000\n",
      " - 1s - loss: 0.5063 - acc: 0.8007 - val_loss: 0.3930 - val_acc: 0.8765\n",
      "roc_auc: 0.88447 - roc_auc_val: 0.83517 - norm_gini: 0.76894 - norm_gini_val: 0.67034          \n",
      "\n",
      "Epoch 00001: norm_gini_val improved from -inf to 0.67034, saving model to keras-5fold-run-01-v1-fold-01-run-01.check\n",
      "Epoch 2/5000\n",
      " - 1s - loss: 0.3900 - acc: 0.8496 - val_loss: 0.3221 - val_acc: 0.8866\n",
      "roc_auc: 0.90337 - roc_auc_val: 0.85734 - norm_gini: 0.80675 - norm_gini_val: 0.71468          \n",
      "\n",
      "Epoch 00002: norm_gini_val improved from 0.67034 to 0.71468, saving model to keras-5fold-run-01-v1-fold-01-run-01.check\n",
      "Epoch 3/5000\n",
      " - 1s - loss: 0.3360 - acc: 0.8572 - val_loss: 0.2816 - val_acc: 0.8891\n",
      "roc_auc: 0.90474 - roc_auc_val: 0.85376 - norm_gini: 0.80948 - norm_gini_val: 0.70752          \n",
      "\n",
      "Epoch 00003: norm_gini_val did not improve from 0.71468\n",
      "Epoch 4/5000\n",
      " - 1s - loss: 0.3102 - acc: 0.8688 - val_loss: 0.2674 - val_acc: 0.8924\n",
      "roc_auc: 0.91604 - roc_auc_val: 0.86741 - norm_gini: 0.83207 - norm_gini_val: 0.73482          \n",
      "\n",
      "Epoch 00004: norm_gini_val improved from 0.71468 to 0.73482, saving model to keras-5fold-run-01-v1-fold-01-run-01.check\n",
      "Epoch 5/5000\n",
      " - 1s - loss: 0.3006 - acc: 0.8654 - val_loss: 0.2792 - val_acc: 0.8992\n",
      "roc_auc: 0.9011 - roc_auc_val: 0.88164 - norm_gini: 0.8022 - norm_gini_val: 0.76329          \n",
      "\n",
      "Epoch 00005: norm_gini_val improved from 0.73482 to 0.76329, saving model to keras-5fold-run-01-v1-fold-01-run-01.check\n",
      "Epoch 6/5000\n",
      " - 1s - loss: 0.2913 - acc: 0.8696 - val_loss: 0.2796 - val_acc: 0.8941\n",
      "roc_auc: 0.91744 - roc_auc_val: 0.86024 - norm_gini: 0.83488 - norm_gini_val: 0.72049          \n",
      "\n",
      "Epoch 00006: norm_gini_val did not improve from 0.76329\n",
      "Epoch 7/5000\n",
      " - 1s - loss: 0.2802 - acc: 0.8744 - val_loss: 0.2697 - val_acc: 0.8983\n",
      "roc_auc: 0.92275 - roc_auc_val: 0.87772 - norm_gini: 0.84551 - norm_gini_val: 0.75544          \n",
      "\n",
      "Epoch 00007: norm_gini_val did not improve from 0.76329\n",
      "Epoch 8/5000\n",
      " - 1s - loss: 0.2799 - acc: 0.8731 - val_loss: 0.3196 - val_acc: 0.8723\n",
      "roc_auc: 0.91027 - roc_auc_val: 0.81504 - norm_gini: 0.82054 - norm_gini_val: 0.63008          \n",
      "\n",
      "Epoch 00008: norm_gini_val did not improve from 0.76329\n",
      "Epoch 9/5000\n",
      " - 1s - loss: 0.2743 - acc: 0.8796 - val_loss: 0.2817 - val_acc: 0.8908\n",
      "roc_auc: 0.92491 - roc_auc_val: 0.87046 - norm_gini: 0.84981 - norm_gini_val: 0.74091          \n",
      "\n",
      "Epoch 00009: norm_gini_val did not improve from 0.76329\n",
      "Epoch 10/5000\n",
      " - 1s - loss: 0.2697 - acc: 0.8813 - val_loss: 0.2884 - val_acc: 0.8891\n",
      "roc_auc: 0.9293 - roc_auc_val: 0.85554 - norm_gini: 0.85861 - norm_gini_val: 0.71108          \n",
      "\n",
      "Epoch 00010: norm_gini_val did not improve from 0.76329\n",
      "Epoch 11/5000\n",
      " - 1s - loss: 0.2650 - acc: 0.8819 - val_loss: 0.2803 - val_acc: 0.8933\n",
      "roc_auc: 0.92973 - roc_auc_val: 0.86913 - norm_gini: 0.85947 - norm_gini_val: 0.73825          \n",
      "\n",
      "Epoch 00011: norm_gini_val did not improve from 0.76329\n",
      "Epoch 12/5000\n",
      " - 1s - loss: 0.2662 - acc: 0.8829 - val_loss: 0.3224 - val_acc: 0.8580\n",
      "roc_auc: 0.92362 - roc_auc_val: 0.8527 - norm_gini: 0.84723 - norm_gini_val: 0.7054          \n",
      "\n",
      "Epoch 00012: norm_gini_val did not improve from 0.76329\n",
      "Epoch 13/5000\n",
      " - 1s - loss: 0.2539 - acc: 0.8867 - val_loss: 0.3013 - val_acc: 0.8874\n",
      "roc_auc: 0.94082 - roc_auc_val: 0.85867 - norm_gini: 0.88164 - norm_gini_val: 0.71733          \n",
      "\n",
      "Epoch 00013: norm_gini_val did not improve from 0.76329\n",
      "Epoch 14/5000\n",
      " - 1s - loss: 0.2450 - acc: 0.8914 - val_loss: 0.3200 - val_acc: 0.8790\n",
      "roc_auc: 0.9397 - roc_auc_val: 0.81731 - norm_gini: 0.87939 - norm_gini_val: 0.63461          \n",
      "\n",
      "Epoch 00014: norm_gini_val did not improve from 0.76329\n",
      "Epoch 15/5000\n",
      " - 1s - loss: 0.2370 - acc: 0.8963 - val_loss: 0.3334 - val_acc: 0.8622\n",
      "roc_auc: 0.92051 - roc_auc_val: 0.85666 - norm_gini: 0.84103 - norm_gini_val: 0.71331          \n",
      "\n",
      "Epoch 00015: norm_gini_val did not improve from 0.76329\n",
      "Epoch 16/5000\n",
      " - 1s - loss: 0.2446 - acc: 0.8924 - val_loss: 0.3271 - val_acc: 0.8723\n",
      "roc_auc: 0.94294 - roc_auc_val: 0.83303 - norm_gini: 0.88589 - norm_gini_val: 0.66607          \n",
      "\n",
      "Epoch 00016: norm_gini_val did not improve from 0.76329\n",
      "Epoch 17/5000\n",
      " - 1s - loss: 0.2341 - acc: 0.8972 - val_loss: 0.3144 - val_acc: 0.8773\n",
      "roc_auc: 0.9448 - roc_auc_val: 0.85018 - norm_gini: 0.8896 - norm_gini_val: 0.70035          \n",
      "\n",
      "Epoch 00017: norm_gini_val did not improve from 0.76329\n",
      "Epoch 18/5000\n",
      " - 1s - loss: 0.2247 - acc: 0.9014 - val_loss: 0.3589 - val_acc: 0.8613\n",
      "roc_auc: 0.93929 - roc_auc_val: 0.83492 - norm_gini: 0.87857 - norm_gini_val: 0.66983          \n",
      "\n",
      "Epoch 00018: norm_gini_val did not improve from 0.76329\n",
      "Epoch 19/5000\n",
      " - 1s - loss: 0.2128 - acc: 0.9058 - val_loss: 0.3621 - val_acc: 0.8924\n",
      "roc_auc: 0.93264 - roc_auc_val: 0.84445 - norm_gini: 0.86528 - norm_gini_val: 0.6889          \n",
      "\n",
      "Epoch 00019: norm_gini_val did not improve from 0.76329\n",
      "Epoch 20/5000\n",
      " - 1s - loss: 0.2189 - acc: 0.9059 - val_loss: 0.3526 - val_acc: 0.8714\n",
      "roc_auc: 0.93532 - roc_auc_val: 0.84633 - norm_gini: 0.87064 - norm_gini_val: 0.69267          \n",
      "\n",
      "Epoch 00020: norm_gini_val did not improve from 0.76329\n",
      "Epoch 21/5000\n",
      " - 1s - loss: 0.2158 - acc: 0.9068 - val_loss: 0.6591 - val_acc: 0.6874\n",
      "roc_auc: 0.89259 - roc_auc_val: 0.74743 - norm_gini: 0.78519 - norm_gini_val: 0.49485          \n",
      "\n",
      "Epoch 00021: norm_gini_val did not improve from 0.76329\n",
      "Epoch 22/5000\n",
      " - 1s - loss: 0.2034 - acc: 0.9114 - val_loss: 0.3746 - val_acc: 0.8546\n",
      "roc_auc: 0.94871 - roc_auc_val: 0.83622 - norm_gini: 0.89742 - norm_gini_val: 0.67244          \n",
      "\n",
      "Epoch 00022: norm_gini_val did not improve from 0.76329\n",
      "Epoch 23/5000\n",
      " - 1s - loss: 0.1994 - acc: 0.9133 - val_loss: 0.3656 - val_acc: 0.8840\n",
      "roc_auc: 0.93868 - roc_auc_val: 0.84172 - norm_gini: 0.87736 - norm_gini_val: 0.68343          \n",
      "\n",
      "Epoch 00023: norm_gini_val did not improve from 0.76329\n",
      "Epoch 24/5000\n",
      " - 1s - loss: 0.1982 - acc: 0.9131 - val_loss: 0.3873 - val_acc: 0.8756\n",
      "roc_auc: 0.94283 - roc_auc_val: 0.84931 - norm_gini: 0.88567 - norm_gini_val: 0.69863          \n",
      "\n",
      "Epoch 00024: norm_gini_val did not improve from 0.76329\n",
      "Epoch 25/5000\n",
      " - 1s - loss: 0.2039 - acc: 0.9128 - val_loss: 0.3905 - val_acc: 0.8882\n",
      "roc_auc: 0.95283 - roc_auc_val: 0.85809 - norm_gini: 0.90565 - norm_gini_val: 0.71618          \n",
      "\n",
      "Epoch 00025: norm_gini_val did not improve from 0.76329\n",
      "Epoch 26/5000\n",
      " - 1s - loss: 0.1884 - acc: 0.9199 - val_loss: 0.3430 - val_acc: 0.8765\n",
      "roc_auc: 0.96445 - roc_auc_val: 0.83987 - norm_gini: 0.92889 - norm_gini_val: 0.67973          \n",
      "\n",
      "Epoch 00026: norm_gini_val did not improve from 0.76329\n",
      "Epoch 27/5000\n",
      " - 1s - loss: 0.1813 - acc: 0.9220 - val_loss: 0.3789 - val_acc: 0.8714\n",
      "roc_auc: 0.9587 - roc_auc_val: 0.85029 - norm_gini: 0.91741 - norm_gini_val: 0.70058          \n",
      "\n",
      "Epoch 00027: norm_gini_val did not improve from 0.76329\n",
      "Epoch 28/5000\n",
      " - 1s - loss: 0.1704 - acc: 0.9273 - val_loss: 0.3735 - val_acc: 0.8748\n",
      "roc_auc: 0.96542 - roc_auc_val: 0.8201 - norm_gini: 0.93084 - norm_gini_val: 0.64021          \n",
      "\n",
      "Epoch 00028: norm_gini_val did not improve from 0.76329\n",
      "Epoch 29/5000\n",
      " - 1s - loss: 0.1604 - acc: 0.9318 - val_loss: 0.3943 - val_acc: 0.8924\n",
      "roc_auc: 0.95672 - roc_auc_val: 0.83625 - norm_gini: 0.91344 - norm_gini_val: 0.6725          \n",
      "\n",
      "Epoch 00029: norm_gini_val did not improve from 0.76329\n",
      "Epoch 30/5000\n",
      " - 1s - loss: 0.1648 - acc: 0.9313 - val_loss: 0.4307 - val_acc: 0.8807\n",
      "roc_auc: 0.96533 - roc_auc_val: 0.84868 - norm_gini: 0.93066 - norm_gini_val: 0.69737          \n",
      "\n",
      "Epoch 00030: norm_gini_val did not improve from 0.76329\n",
      "Epoch 31/5000\n",
      " - 1s - loss: 0.1598 - acc: 0.9329 - val_loss: 0.4047 - val_acc: 0.8622\n",
      "roc_auc: 0.95933 - roc_auc_val: 0.82326 - norm_gini: 0.91866 - norm_gini_val: 0.64653          \n",
      "\n",
      "Epoch 00031: norm_gini_val did not improve from 0.76329\n",
      "Epoch 32/5000\n",
      " - 1s - loss: 0.1678 - acc: 0.9279 - val_loss: 0.4013 - val_acc: 0.8824\n",
      "roc_auc: 0.96417 - roc_auc_val: 0.83654 - norm_gini: 0.92835 - norm_gini_val: 0.67308          \n",
      "\n",
      "Epoch 00032: norm_gini_val did not improve from 0.76329\n",
      "Epoch 33/5000\n",
      " - 1s - loss: 0.1549 - acc: 0.9324 - val_loss: 0.4664 - val_acc: 0.8370\n",
      "roc_auc: 0.95329 - roc_auc_val: 0.81885 - norm_gini: 0.90657 - norm_gini_val: 0.63771          \n",
      "\n",
      "Epoch 00033: norm_gini_val did not improve from 0.76329\n",
      "Epoch 34/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 1s - loss: 0.1428 - acc: 0.9402 - val_loss: 0.4534 - val_acc: 0.8849\n",
      "roc_auc: 0.96654 - roc_auc_val: 0.84521 - norm_gini: 0.93308 - norm_gini_val: 0.69042          \n",
      "\n",
      "Epoch 00034: norm_gini_val did not improve from 0.76329\n",
      "Epoch 35/5000\n",
      " - 1s - loss: 0.1532 - acc: 0.9380 - val_loss: 0.4461 - val_acc: 0.8681\n",
      "roc_auc: 0.97112 - roc_auc_val: 0.82052 - norm_gini: 0.94225 - norm_gini_val: 0.64103          \n",
      "\n",
      "Epoch 00035: norm_gini_val did not improve from 0.76329\n",
      "Epoch 00035: early stopping\n",
      "\n",
      " Fold 1 Run 1 Log-loss: 0.27917\n",
      " Fold 1 Run 1 AUC: 0.88164\n",
      " Fold 1 Run 1 normalized gini: 0.76329\n",
      "\n",
      " Fold 1 Log-loss: 0.27917\n",
      " Fold 1 AUC: 0.88164\n",
      " Fold 1 normalized gini: 0.76329\n",
      "\n",
      " Time taken: 0 hours 0 minutes and 38.27 seconds.\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'KFold_X_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-33-64da9d15c0a0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[1;31m# We add up predictions on the test data for each fold. Create out-of-fold predictions for validation data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m     \u001b[0mtrain_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mKFold_X_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m     \u001b[0mtest_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredscaledX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'KFold_X_test' is not defined"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "from sklearn.metrics import log_loss, roc_auc_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from keras.models import load_model\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Input, Dense, Dropout, Activation\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, CSVLogger, Callback\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n",
    "class roc_auc_callback(Callback):\n",
    "    def __init__(self,training_data,validation_data):\n",
    "        self.x = training_data[0]\n",
    "        self.y = training_data[1]\n",
    "        self.x_val = validation_data[0]\n",
    "        self.y_val = validation_data[1]\n",
    "\n",
    "    def on_train_begin(self, logs={}):\n",
    "        return\n",
    "\n",
    "    def on_train_end(self, logs={}):\n",
    "        return\n",
    "\n",
    "    def on_epoch_begin(self, epoch, logs={}):\n",
    "        return\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        y_pred = self.model.predict_proba(self.x, verbose=0)\n",
    "        roc = roc_auc_score(self.y, y_pred)\n",
    "        logs['roc_auc'] = roc_auc_score(self.y, y_pred)\n",
    "        logs['norm_gini'] = ( roc_auc_score(self.y, y_pred) * 2 ) - 1\n",
    "\n",
    "        y_pred_val = self.model.predict_proba(self.x_val, verbose=0)\n",
    "        roc_val = roc_auc_score(self.y_val, y_pred_val)\n",
    "        logs['roc_auc_val'] = roc_auc_score(self.y_val, y_pred_val)\n",
    "        logs['norm_gini_val'] = ( roc_auc_score(self.y_val, y_pred_val) * 2 ) - 1\n",
    "\n",
    "        print('\\rroc_auc: %s - roc_auc_val: %s - norm_gini: %s - norm_gini_val: %s' % (str(round(roc,5)),str(round(roc_val,5)),str(round((roc*2-1),5)),str(round((roc_val*2-1),5))), end=10*' '+'\\n')\n",
    "        return\n",
    "\n",
    "    def on_batch_begin(self, batch, logs={}):\n",
    "        return\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        return\n",
    "\n",
    "def timer(start_time=None):\n",
    "    if not start_time:\n",
    "        start_time = datetime.now()\n",
    "        return start_time\n",
    "    elif start_time:\n",
    "        thour, temp_sec = divmod(\n",
    "            (datetime.now() - start_time).total_seconds(), 3600)\n",
    "        tmin, tsec = divmod(temp_sec, 60)\n",
    "        print('\\n Time taken: %i hours %i minutes and %s seconds.' %\n",
    "              (thour, tmin, round(tsec, 2)))\n",
    "\n",
    "def scale_data(X, scaler=None):\n",
    "    if not scaler:\n",
    "        scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "        scaler.fit(X)\n",
    "    X = scaler.transform(X)\n",
    "    return X, scaler\n",
    "\n",
    "folds = 4\n",
    "runs = 1\n",
    "\n",
    "\n",
    "cv_LL = 0\n",
    "cv_AUC = 0\n",
    "cv_gini = 0\n",
    "fpred = []\n",
    "avpred = []\n",
    "avreal = []\n",
    "i = 0\n",
    "#avids = []\n",
    "\n",
    "patience = 30\n",
    "batchsize = 100\n",
    "\n",
    "# Let's split the data into folds. I always use the same random number for reproducibility, \n",
    "# and suggest that you do the same (you certainly don't have to use 1001).\n",
    "\n",
    "skf = StratifiedKFold(n_splits=folds)\n",
    "starttime = timer(None)\n",
    "#for i, (train_index, test_index) in enumerate(skf.split(np_scaled_raw_X, np_y)):\n",
    "    \n",
    "    #X_train, X_val = np_scaled_raw_X[train_index], np_scaled_raw_X[test_index]\n",
    "    #y_train, y_val = np_y[train_index], np_y[test_index]\n",
    "    #train_ids, val_ids = tr_ids[train_index], tr_ids[test_index]\n",
    "\n",
    "for train_index, test_index in kf.split(scaledX.values):\n",
    "    X_train, X_val = scaledX.values[train_index], scaledX.values[test_index]\n",
    "    y_train, y_val = Y.values[train_index], Y.values[test_index]    \n",
    "      \n",
    "    start_time = timer(None)\n",
    "    count = 0\n",
    "\n",
    "    def baseline_model():\n",
    "        model = Sequential()\n",
    "        model.add(Dense(300,input_dim=X_train.shape[1],kernel_initializer='glorot_normal',))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(BatchNormalization())\n",
    "        model.add(Dense(100, kernel_initializer='glorot_normal'))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(BatchNormalization())\n",
    "        model.add(Dense(50, kernel_initializer='glorot_normal'))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(BatchNormalization())\n",
    "        model.add(Dense(25, kernel_initializer='glorot_normal'))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(BatchNormalization())\n",
    "        model.add(Dropout(0.1))\n",
    "        model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "        # Compile model\n",
    "        model.compile(optimizer='adam', metrics = ['accuracy'], loss='binary_crossentropy')\n",
    "\n",
    "        return model\n",
    "\n",
    "    for run in range(runs):\n",
    "        print('\\n Fold %d - Run %d\\n' % ((i + 1), (run + 1)))\n",
    "        np.random.seed()\n",
    "\n",
    "        callbacks = [\n",
    "            roc_auc_callback(training_data=(X_train, y_train),validation_data=(X_val, y_val)),\n",
    "            EarlyStopping(monitor='norm_gini_val', patience=patience, mode='max', verbose=1),\n",
    "            ModelCheckpoint(\n",
    "                    'keras-5fold-run-01-v1-fold-' + str('%02d' % (i + 1)) + '-run-' + str('%02d' % (run + 1)) + '.check',\n",
    "                    monitor='norm_gini_val', mode='max',\n",
    "                    save_best_only=True,\n",
    "                    verbose=1)\n",
    "        ]\n",
    "\n",
    "        nnet = KerasClassifier(\n",
    "            build_fn=baseline_model,\n",
    "            epochs=5000,\n",
    "            batch_size=batchsize,\n",
    "            validation_data=(X_val, y_val),\n",
    "            verbose=2,\n",
    "            shuffle=True,\n",
    "            callbacks=callbacks)\n",
    "\n",
    "        fit = nnet.fit(X_train, y_train)\n",
    "\n",
    "        del nnet\n",
    "        nnet = load_model('keras-5fold-run-01-v1-fold-' + str('%02d' % (i + 1)) + '-run-' + str('%02d' % (run + 1)) + '.check')\n",
    "        scores_val_run = nnet.predict_proba(X_val, verbose=0)\n",
    "        LL_run = log_loss(y_val, scores_val_run)\n",
    "        print('\\n Fold %d Run %d Log-loss: %.5f' % ((i + 1), (run + 1), LL_run))\n",
    "        AUC_run = roc_auc_score(y_val, scores_val_run)\n",
    "        print(' Fold %d Run %d AUC: %.5f' % ((i + 1), (run + 1), AUC_run))\n",
    "        print(' Fold %d Run %d normalized gini: %.5f' % ((i + 1), (run + 1), AUC_run*2-1))\n",
    "        y_pred_run = nnet.predict_proba(predscaledX, verbose=0)\n",
    "        if run > 0:\n",
    "            scores_val = scores_val + scores_val_run\n",
    "            y_pred = y_pred + y_pred_run\n",
    "        else:\n",
    "            scores_val = scores_val_run\n",
    "            y_pred = y_pred_run\n",
    "\n",
    "    scores_val = scores_val / runs\n",
    "    y_pred = y_pred / runs\n",
    "    LL = log_loss(y_val, scores_val)\n",
    "    print('\\n Fold %d Log-loss: %.5f' % ((i + 1), LL))\n",
    "    AUC = roc_auc_score(y_val, scores_val)\n",
    "    print(' Fold %d AUC: %.5f' % ((i + 1), AUC))\n",
    "    print(' Fold %d normalized gini: %.5f' % ((i + 1), AUC*2-1))\n",
    "    timer(start_time)\n",
    "\n",
    "    train_pred = nnet.predict_proba(KFold_X_test)\n",
    "    test_pred = nnet.predict_proba(predscaledX)\n",
    "                \n",
    "    name = 'scaled_X_nn'\n",
    "    temp_name = 'temp_scaled_k_{}'.format(count)\n",
    "    \n",
    "    train_preds_df.loc[test_index,name] = train_pred.T\n",
    "    temp_test_preds_df[temp_name] = test_pred.tolist()\n",
    "    count += 1\n",
    "    \n",
    "    \n",
    "    if i > 0:\n",
    "        fpred = pred + y_pred\n",
    "    else:\n",
    "        fpred = y_pred\n",
    "    pred = fpred\n",
    "    cv_LL = cv_LL + LL\n",
    "    cv_AUC = cv_AUC + AUC\n",
    "    cv_gini = cv_gini + (AUC*2-1)\n",
    "    i += 1\n",
    "\n",
    "test_preds_df[name] = np.asarray(0.1)*temp_test_preds_df['temp_scaled_k_0'] + \\\n",
    "                            np.asarray(0.1)*temp_test_preds_df['temp_scaled_k_1'] + \\\n",
    "                            np.asarray(0.1)*temp_test_preds_df['temp_scaled_k_2'] + \\\n",
    "                            np.asarray(0.1)*temp_test_preds_df['temp_scaled_k_3'] + \\\n",
    "                            np.asarray(0.1)*temp_test_preds_df['temp_scaled_k_4'] + \\\n",
    "                            np.asarray(0.1)*temp_test_preds_df['temp_scaled_k_5'] + \\\n",
    "                            np.asarray(0.1)*temp_test_preds_df['temp_scaled_k_6'] + \\\n",
    "                            np.asarray(0.1)*temp_test_preds_df['temp_scaled_k_7'] + \\\n",
    "                            np.asarray(0.1)*temp_test_preds_df['temp_scaled_k_8'] + \\\n",
    "                            np.asarray(0.1)*temp_test_preds_df['temp_scaled_k_9']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_feat_k_rf_xgb_lgb_nn_df = pd.merge(test_preds_df, preddf, left_index=True, right_index=True)\n",
    "train_feat_k_rf_xgb_lgb_nn_df = pd.merge(train_preds_df, scaledX, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_k_rf_xgb_lgb_nn = XGBClassifier(\n",
    " learning_rate =0.01,\n",
    " n_estimators=3000,\n",
    " max_depth=3,\n",
    " min_child_weight=4,\n",
    " gamma=0.3,\n",
    " subsample=0.65,\n",
    " colsample_bytree=0.75,\n",
    " reg_alpha=0.00009,\n",
    " objective= 'binary:logistic',\n",
    " nthread=5,\n",
    " scale_pos_weight=1)\n",
    "\n",
    "modelfit(xgb_k_rf_xgb_lgb_nn, train_feat_k_rf_xgb_lgb_nn_df, Y, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_feat_k_rf_xgb_lgb_nn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "soumission14 = xgb_k_rf_xgb_lgb_nn.predict_proba(test_feat_k_rf_xgb_lgb_nn_df.values)\n",
    "np.savetxt(\"Sub_14.csv\", soumission14, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "regr = linear_model.LogisticRegression()\n",
    "\n",
    "count = 0\n",
    "for train_index, test_index in kf.split(scaledX.values):\n",
    "    KFold_X_train, KFold_X_test = scaledX.values[train_index], scaledX.values[test_index]\n",
    "    KFold_y_train, KFold_y_test = Y.values[train_index], Y.values[test_index]\n",
    "\n",
    "    regr.fit(KFold_X_train, KFold_y_train)\n",
    "    \n",
    "    train_pred = regr.predict_proba(KFold_X_test)\n",
    "    test_pred = regr.predict_proba(predscaledX)\n",
    "                \n",
    "    name = 'scaled_regr'\n",
    "    temp_name = 'temp_scaled_k_{}'.format(count)\n",
    "    \n",
    "    train_preds_df.loc[test_index,name] = train_pred[:, 1].tolist()\n",
    "    temp_test_preds_df[temp_name] = test_pred[:, 1].tolist()\n",
    "    count += 1\n",
    "test_preds_df[name] = 0.1*temp_test_preds_df['temp_scaled_k_0'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_1'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_2'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_3'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_4'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_5'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_6'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_7'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_8'.format(k)] + \\\n",
    "                            0.1*temp_test_preds_df['temp_scaled_k_9'.format(k)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_feat_k_rf_xgb_lgb_nn_regr_df = pd.merge(test_preds_df, preddf, left_index=True, right_index=True)\n",
    "train_feat_k_rf_xgb_lgb_nn_regr_df = pd.merge(train_preds_df, scaledX, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_k_rf_xgb_lgb_nn_regr = XGBClassifier(\n",
    " learning_rate =0.01,\n",
    " n_estimators=3000,\n",
    " max_depth=3,\n",
    " min_child_weight=4,\n",
    " gamma=0.3,\n",
    " subsample=0.65,\n",
    " colsample_bytree=0.75,\n",
    " reg_alpha=0.00009,\n",
    " objective= 'binary:logistic',\n",
    " nthread=5,\n",
    " scale_pos_weight=1)\n",
    "\n",
    "modelfit(xgb_k_rf_xgb_lgb_nn_regr, train_feat_k_rf_xgb_lgb_nn_regr_df, Y, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_feat_k_rf_xgb_lgb_nn_regr_df[['scaled_k1' , 'scaled_k3', 'scaled_k5', 'scaled_rf', 'scaled_xgb', 'scaled_X_lgb', 'scaled_X_nn', 'scaled_regr']]"
   ]
  }
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