Churn Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayur Maheshwari\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "C:\\Users\\Mayur Maheshwari\\Anaconda3\\lib\\site-packages\\sklearn\\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import datetime\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import sklearn.tree as dt\n",
    "import sklearn.ensemble as en\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>PDATE</th>\n",
       "      <th>PAMT</th>\n",
       "      <th>EXTRELNO</th>\n",
       "      <th>PAYTYPECD</th>\n",
       "      <th>STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>b'X'</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.0</td>\n",
       "      <td>1993-12-31</td>\n",
       "      <td>13.10</td>\n",
       "      <td>b'0000020'</td>\n",
       "      <td>b'X'</td>\n",
       "      <td>b'CO'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54091.0</td>\n",
       "      <td>1994-01-01</td>\n",
       "      <td>2.02</td>\n",
       "      <td>b'0000020'</td>\n",
       "      <td>b'X'</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104480.0</td>\n",
       "      <td>1994-05-19</td>\n",
       "      <td>0.50</td>\n",
       "      <td>b'0000020'</td>\n",
       "      <td>b'D'</td>\n",
       "      <td>b'OK'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PID      PDATE   PAMT    EXTRELNO PAYTYPECD STATUS\n",
       "0      38.0 1993-12-31   9.57  b'0000019'      b'X'  b'CO'\n",
       "1      39.0 1993-12-31  41.32  b'0000020'      b'X'  b'CO'\n",
       "2      40.0 1993-12-31  13.10  b'0000020'      b'X'  b'CO'\n",
       "3   54091.0 1994-01-01   2.02  b'0000020'      b'X'  b'CO'\n",
       "4  104480.0 1994-05-19   0.50  b'0000020'      b'D'  b'OK'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payhistory=pd.read_sas(\"1 - Data for Churn Modeling/payhistory.sas7bdat\")\n",
    "payhistory.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>PDATE</th>\n",
       "      <th>PAMT</th>\n",
       "      <th>EXTRELNO</th>\n",
       "      <th>PAYTYPECD</th>\n",
       "      <th>STATUS</th>\n",
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       "      <td>9.57</td>\n",
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       "      <td>CO</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.0</td>\n",
       "      <td>1993-12-31</td>\n",
       "      <td>41.32</td>\n",
       "      <td>0000020</td>\n",
       "      <td>X</td>\n",
       "      <td>CO</td>\n",
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       "      <th>2</th>\n",
       "      <td>40.0</td>\n",
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       "      <td>CO</td>\n",
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       "      <td>0000020</td>\n",
       "      <td>D</td>\n",
       "      <td>OK</td>\n",
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      "text/plain": [
       "        PID      PDATE   PAMT EXTRELNO PAYTYPECD STATUS\n",
       "0      38.0 1993-12-31   9.57  0000019         X     CO\n",
       "1      39.0 1993-12-31  41.32  0000020         X     CO\n",
       "2      40.0 1993-12-31  13.10  0000020         X     CO\n",
       "3   54091.0 1994-01-01   2.02  0000020         X     CO\n",
       "4  104480.0 1994-05-19   0.50  0000020         D     OK"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payhistory.iloc[:,3]=payhistory.iloc[:,3].str.decode(\"utf-8\")\n",
    "payhistory.iloc[:,4]=payhistory.iloc[:,4].str.decode(\"utf-8\")\n",
    "payhistory.iloc[:,5]=payhistory.iloc[:,5].str.decode(\"utf-8\")\n",
    "payhistory.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID                 float64\n",
       "PDATE        datetime64[ns]\n",
       "PAMT                float64\n",
       "EXTRELNO             object\n",
       "PAYTYPECD            object\n",
       "STATUS               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payhistory.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLASCODE</th>\n",
       "      <th>CLASDESC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'CT'</td>\n",
       "      <td>b'CTS-item'</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'RI'</td>\n",
       "      <td>b'Request for information'</td>\n",
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       "      <th>2</th>\n",
       "      <td>b'CM'</td>\n",
       "      <td>b'Complaint'</td>\n",
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       "      <th>3</th>\n",
       "      <td>b'TA'</td>\n",
       "      <td>b'Threath / Accusation'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'AK'</td>\n",
       "      <td>b'Acknowledgement'</td>\n",
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      "text/plain": [
       "  CLASCODE                    CLASDESC\n",
       "0    b'CT'                 b'CTS-item'\n",
       "1    b'RI'  b'Request for information'\n",
       "2    b'CM'                b'Complaint'\n",
       "3    b'TA'     b'Threath / Accusation'\n",
       "4    b'AK'          b'Acknowledgement'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comclas=pd.read_sas(\"1 - Data for Churn Modeling/comclas.sas7bdat\")\n",
    "comclas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in comclas.columns:\n",
    "    comclas[col]=comclas[col].str.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>CLASCODE</th>\n",
       "      <th>CLASDESC</th>\n",
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       "  <tbody>\n",
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       "      <td>CT</td>\n",
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       "      <td>TA</td>\n",
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       "      <th>4</th>\n",
       "      <td>AK</td>\n",
       "      <td>Acknowledgement</td>\n",
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      "text/plain": [
       "  CLASCODE                 CLASDESC\n",
       "0       CT                 CTS-item\n",
       "1       RI  Request for information\n",
       "2       CM                Complaint\n",
       "3       TA     Threath / Accusation\n",
       "4       AK          Acknowledgement"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comclas.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reminder                   1\n",
       "Threath / Accusation       1\n",
       "Information delivery       1\n",
       "Complaint                  1\n",
       "CTS-item                   1\n",
       "Acknowledgement            1\n",
       "Request for information    1\n",
       "Answer                     1\n",
       "Name: CLASDESC, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comclas['CLASDESC'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CLASCODE    object\n",
       "CLASDESC    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comclas.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>MNTOPCODE</th>\n",
       "      <th>MNTOPDESC</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'CAN'</td>\n",
       "      <td>b'Cancellations/replacements'</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'DIS'</td>\n",
       "      <td>b'Discontinuations'</td>\n",
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       "      <th>2</th>\n",
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       "      <td>b'Marketing - Enquiries'</td>\n",
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       "      <th>3</th>\n",
       "      <td>b'GEN'</td>\n",
       "      <td>b'General'</td>\n",
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      "text/plain": [
       "  MNTOPCODE                      MNTOPDESC\n",
       "0    b'CAN'  b'Cancellations/replacements'\n",
       "1    b'DIS'            b'Discontinuations'\n",
       "2    b'MKT'       b'Marketing - Enquiries'\n",
       "3    b'GEN'                     b'General'\n",
       "4    b'NEW'               b'New relations'"
      ]
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "commaint=pd.read_sas(\"1 - Data for Churn Modeling/commaint.sas7bdat\")\n",
    "commaint.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in commaint.columns:\n",
    "    commaint[col]=commaint[col].str.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>CAN</td>\n",
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       "      <td>Discontinuations</td>\n",
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       "      <th>2</th>\n",
       "      <td>MKT</td>\n",
       "      <td>Marketing - Enquiries</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>GEN</td>\n",
       "      <td>General</td>\n",
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       "      <th>4</th>\n",
       "      <td>NEW</td>\n",
       "      <td>New relations</td>\n",
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      ],
      "text/plain": [
       "  MNTOPCODE                   MNTOPDESC\n",
       "0       CAN  Cancellations/replacements\n",
       "1       DIS            Discontinuations\n",
       "2       MKT       Marketing - Enquiries\n",
       "3       GEN                     General\n",
       "4       NEW               New relations"
      ]
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "commaint.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "General                            2\n",
       "Web related Actions: E-commerce    1\n",
       "Correspondence FP/FC               1\n",
       "Change in information Relation     1\n",
       "Information about FF/FC            1\n",
       "Merchandising                      1\n",
       "Translation                        1\n",
       "Finance                            1\n",
       "Public Relations activities        1\n",
       "New relations                      1\n",
       "TV-Show                            1\n",
       "Reports                            1\n",
       "Arrears                            1\n",
       "Volunteers                         1\n",
       "Special Forms                      1\n",
       "Projects                           1\n",
       "Catastrophes                       1\n",
       "Marketing - Enquiries              1\n",
       "Discontinuations                   1\n",
       "Action                             1\n",
       "Visits                             1\n",
       "Cancellations/replacements         1\n",
       "Enrollment                         1\n",
       "Mail met FC, klachten en info      1\n",
       "Tax forms                          1\n",
       "Contacts for sponsoring            1\n",
       "Communication Tracking System      1\n",
       "information about Plan             1\n",
       "Fiscal certificates                1\n",
       "Name: MNTOPDESC, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commaint['MNTOPDESC'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>MEDIUMDESC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'FF'</td>\n",
       "      <td>b'Face to Face contact'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'FX'</td>\n",
       "      <td>b'Fax'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'TX'</td>\n",
       "      <td>b'Telex'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'EM'</td>\n",
       "      <td>b'Electronic Mail'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'TE'</td>\n",
       "      <td>b'Telephone call'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MEDIUMCODE               MEDIUMDESC\n",
       "0      b'FF'  b'Face to Face contact'\n",
       "1      b'FX'                   b'Fax'\n",
       "2      b'TX'                 b'Telex'\n",
       "3      b'EM'       b'Electronic Mail'\n",
       "4      b'TE'        b'Telephone call'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commediu=pd.read_sas(\"1 - Data for Churn Modeling/commediu.sas7bdat\")\n",
    "commediu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in commediu.columns:\n",
    "    commediu[col]=commediu[col].str.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FF</td>\n",
       "      <td>Face to Face contact</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>FX</td>\n",
       "      <td>Fax</td>\n",
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       "      <th>2</th>\n",
       "      <td>TX</td>\n",
       "      <td>Telex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EM</td>\n",
       "      <td>Electronic Mail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TE</td>\n",
       "      <td>Telephone call</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MEDIUMCODE            MEDIUMDESC\n",
       "0         FF  Face to Face contact\n",
       "1         FX                   Fax\n",
       "2         TX                 Telex\n",
       "3         EM       Electronic Mail\n",
       "4         TE        Telephone call"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commediu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>CONTID</th>\n",
       "      <th>MEDIUMCODE</th>\n",
       "      <th>MNTOPCODE</th>\n",
       "      <th>CLASCODE</th>\n",
       "      <th>EXTRELNO</th>\n",
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       "      <th>0</th>\n",
       "      <td>b'000405402'</td>\n",
       "      <td>b'CI'</td>\n",
       "      <td>b'CTS'</td>\n",
       "      <td>b'CT'</td>\n",
       "      <td>b'0000020'</td>\n",
       "      <td>b'O'</td>\n",
       "      <td>1994-05-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'000430904'</td>\n",
       "      <td>b'CI'</td>\n",
       "      <td>b'CTS'</td>\n",
       "      <td>b'CT'</td>\n",
       "      <td>b'0000020'</td>\n",
       "      <td>b'O'</td>\n",
       "      <td>1994-06-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'000592732'</td>\n",
       "      <td>b'CI'</td>\n",
       "      <td>b'CTS'</td>\n",
       "      <td>b'CT'</td>\n",
       "      <td>b'0000020'</td>\n",
       "      <td>b'O'</td>\n",
       "      <td>1994-10-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'000593375'</td>\n",
       "      <td>b'CI'</td>\n",
       "      <td>b'CTS'</td>\n",
       "      <td>b'CT'</td>\n",
       "      <td>b'0000020'</td>\n",
       "      <td>b'O'</td>\n",
       "      <td>1994-10-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'000618514'</td>\n",
       "      <td>b'CI'</td>\n",
       "      <td>b'CTS'</td>\n",
       "      <td>b'CT'</td>\n",
       "      <td>b'0000020'</td>\n",
       "      <td>b'O'</td>\n",
       "      <td>1994-11-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CONTID MEDIUMCODE MNTOPCODE CLASCODE    EXTRELNO CONTDIREC   CONTDATE\n",
       "0  b'000405402'      b'CI'    b'CTS'    b'CT'  b'0000020'      b'O' 1994-05-09\n",
       "1  b'000430904'      b'CI'    b'CTS'    b'CT'  b'0000020'      b'O' 1994-06-14\n",
       "2  b'000592732'      b'CI'    b'CTS'    b'CT'  b'0000020'      b'O' 1994-10-21\n",
       "3  b'000593375'      b'CI'    b'CTS'    b'CT'  b'0000020'      b'O' 1994-10-21\n",
       "4  b'000618514'      b'CI'    b'CTS'    b'CT'  b'0000020'      b'O' 1994-11-28"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communication=pd.read_sas(\"1 - Data for Churn Modeling/communication.sas7bdat\")\n",
    "communication.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in communication.columns:\n",
    "    if col != 'CONTDATE':\n",
    "        communication[col]=communication[col].str.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>MEDIUMCODE</th>\n",
       "      <th>MNTOPCODE</th>\n",
       "      <th>CLASCODE</th>\n",
       "      <th>EXTRELNO</th>\n",
       "      <th>CONTDIREC</th>\n",
       "      <th>CONTDATE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000405402</td>\n",
       "      <td>CI</td>\n",
       "      <td>CTS</td>\n",
       "      <td>CT</td>\n",
       "      <td>0000020</td>\n",
       "      <td>O</td>\n",
       "      <td>1994-05-09</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000430904</td>\n",
       "      <td>CI</td>\n",
       "      <td>CTS</td>\n",
       "      <td>CT</td>\n",
       "      <td>0000020</td>\n",
       "      <td>O</td>\n",
       "      <td>1994-06-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000592732</td>\n",
       "      <td>CI</td>\n",
       "      <td>CTS</td>\n",
       "      <td>CT</td>\n",
       "      <td>0000020</td>\n",
       "      <td>O</td>\n",
       "      <td>1994-10-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000593375</td>\n",
       "      <td>CI</td>\n",
       "      <td>CTS</td>\n",
       "      <td>CT</td>\n",
       "      <td>0000020</td>\n",
       "      <td>O</td>\n",
       "      <td>1994-10-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000618514</td>\n",
       "      <td>CI</td>\n",
       "      <td>CTS</td>\n",
       "      <td>CT</td>\n",
       "      <td>0000020</td>\n",
       "      <td>O</td>\n",
       "      <td>1994-11-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CONTID MEDIUMCODE MNTOPCODE CLASCODE EXTRELNO CONTDIREC   CONTDATE\n",
       "0  000405402         CI       CTS       CT  0000020         O 1994-05-09\n",
       "1  000430904         CI       CTS       CT  0000020         O 1994-06-14\n",
       "2  000592732         CI       CTS       CT  0000020         O 1994-10-21\n",
       "3  000593375         CI       CTS       CT  0000020         O 1994-10-21\n",
       "4  000618514         CI       CTS       CT  0000020         O 1994-11-28"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communication.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>b'0026424'</td>\n",
       "      <td>b'FP'</td>\n",
       "      <td>1992-03-01</td>\n",
       "      <td>1996-12-24</td>\n",
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       "      <th>3</th>\n",
       "      <td>b'0026430'</td>\n",
       "      <td>b'FP'</td>\n",
       "      <td>1992-03-01</td>\n",
       "      <td>2006-01-31</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'0026430'</td>\n",
       "      <td>b'CT'</td>\n",
       "      <td>1997-03-07</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EXTRELNO EXRELACTCD EXTRELSTDT EXRELDATEN\n",
       "0  b'0026414'      b'CT' 1995-02-02        NaT\n",
       "1  b'0026419'      b'FP' 1992-03-01        NaT\n",
       "2  b'0026424'      b'FP' 1992-03-01 1996-12-24\n",
       "3  b'0026430'      b'FP' 1992-03-01 2006-01-31\n",
       "4  b'0026430'      b'CT' 1997-03-07        NaT"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrel=pd.read_sas(\"1 - Data for Churn Modeling/extrel.sas7bdat\")\n",
    "extrel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "extrel['EXTRELNO']=extrel.iloc[:,0].str.decode(\"utf-8\")\n",
    "extrel['EXRELACTCD']=extrel.iloc[:,1].str.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0026424</td>\n",
       "      <td>FP</td>\n",
       "      <td>1992-03-01</td>\n",
       "      <td>1996-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0026430</td>\n",
       "      <td>FP</td>\n",
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       "      <td>2006-01-31</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0026430</td>\n",
       "      <td>CT</td>\n",
       "      <td>1997-03-07</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EXTRELNO EXRELACTCD EXTRELSTDT EXRELDATEN\n",
       "0  0026414         CT 1995-02-02        NaT\n",
       "1  0026419         FP 1992-03-01        NaT\n",
       "2  0026424         FP 1992-03-01 1996-12-24\n",
       "3  0026430         FP 1992-03-01 2006-01-31\n",
       "4  0026430         CT 1997-03-07        NaT"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrel.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EXTRELNO              object\n",
       "EXRELACTCD            object\n",
       "EXTRELSTDT    datetime64[ns]\n",
       "EXRELDATEN    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrel.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'FP'</td>\n",
       "      <td>b'CommitmentDo'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'PS'</td>\n",
       "      <td>b'Prospect'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'PF'</td>\n",
       "      <td>b'Prosp Fundra'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'PP'</td>\n",
       "      <td>b'Prosp Promot'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'GR'</td>\n",
       "      <td>b'Grantor'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EXRELACTCD       EXRELACTDE\n",
       "0      b'FP'  b'CommitmentDo'\n",
       "1      b'PS'      b'Prospect'\n",
       "2      b'PF'  b'Prosp Fundra'\n",
       "3      b'PP'  b'Prosp Promot'\n",
       "4      b'GR'       b'Grantor'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrelty=pd.read_sas(\"1 - Data for Churn Modeling/extrelty.sas7bdat\")\n",
    "extrelty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in extrelty.columns:\n",
    "    extrelty[col]=extrelty[col].str.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>EXRELACTCD</th>\n",
       "      <th>EXRELACTDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FP</td>\n",
       "      <td>CommitmentDo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PS</td>\n",
       "      <td>Prospect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PF</td>\n",
       "      <td>Prosp Fundra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PP</td>\n",
       "      <td>Prosp Promot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GR</td>\n",
       "      <td>Grantor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EXRELACTCD    EXRELACTDE\n",
       "0         FP  CommitmentDo\n",
       "1         PS      Prospect\n",
       "2         PF  Prosp Fundra\n",
       "3         PP  Prosp Promot\n",
       "4         GR       Grantor"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrelty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>EXTRELNO</th>\n",
       "      <th>NAME1TITLE</th>\n",
       "      <th>POSTCODE</th>\n",
       "      <th>LANGUACODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'0000019'</td>\n",
       "      <td>b'HR'</td>\n",
       "      <td>b'2500'</td>\n",
       "      <td>b'NL'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'0000020'</td>\n",
       "      <td>b'HR'</td>\n",
       "      <td>b'9550'</td>\n",
       "      <td>b'NL'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'0000035'</td>\n",
       "      <td>b'HM'</td>\n",
       "      <td>b'2150'</td>\n",
       "      <td>b'NL'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'0000042'</td>\n",
       "      <td>b'FA'</td>\n",
       "      <td>b'3012'</td>\n",
       "      <td>b'NL'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'0000043'</td>\n",
       "      <td>b'MW'</td>\n",
       "      <td>b'3000'</td>\n",
       "      <td>b'NL'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EXTRELNO NAME1TITLE POSTCODE LANGUACODE\n",
       "0  b'0000019'      b'HR'  b'2500'      b'NL'\n",
       "1  b'0000020'      b'HR'  b'9550'      b'NL'\n",
       "2  b'0000035'      b'HM'  b'2150'      b'NL'\n",
       "3  b'0000042'      b'FA'  b'3012'      b'NL'\n",
       "4  b'0000043'      b'MW'  b'3000'      b'NL'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naeaddr=pd.read_sas(\"1 - Data for Churn Modeling/nameaddr.sas7bdat\")\n",
    "naeaddr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "naeaddr['NAME1TITLE']=naeaddr.iloc[:,1].str.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "naeaddr['EXTRELNO']=naeaddr.iloc[:,0].str.decode(\"utf-8\")\n",
    "naeaddr['POSTCODE']=naeaddr.iloc[:,2].str.decode(\"utf-8\")\n",
    "naeaddr['LANGUACODE']=naeaddr.iloc[:,3].str.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EXTRELNO      object\n",
       "NAME1TITLE    object\n",
       "POSTCODE      object\n",
       "LANGUACODE    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naeaddr.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>EXTRELNO</th>\n",
       "      <th>NAME1TITLE</th>\n",
       "      <th>POSTCODE</th>\n",
       "      <th>LANGUACODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000019</td>\n",
       "      <td>HR</td>\n",
       "      <td>2500</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000020</td>\n",
       "      <td>HR</td>\n",
       "      <td>9550</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000035</td>\n",
       "      <td>HM</td>\n",
       "      <td>2150</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000042</td>\n",
       "      <td>FA</td>\n",
       "      <td>3012</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000043</td>\n",
       "      <td>MW</td>\n",
       "      <td>3000</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EXTRELNO NAME1TITLE POSTCODE LANGUACODE\n",
       "0  0000019         HR     2500         NL\n",
       "1  0000020         HR     9550         NL\n",
       "2  0000035         HM     2150         NL\n",
       "3  0000042         FA     3012         NL\n",
       "4  0000043         MW     3000         NL"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naeaddr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1205720 entries, 0 to 1205719\n",
      "Data columns (total 6 columns):\n",
      "PID          1205720 non-null float64\n",
      "PDATE        1205720 non-null datetime64[ns]\n",
      "PAMT         1205720 non-null float64\n",
      "EXTRELNO     1205720 non-null object\n",
      "PAYTYPECD    1187546 non-null object\n",
      "STATUS       1205719 non-null object\n",
      "dtypes: datetime64[ns](1), float64(2), object(3)\n",
      "memory usage: 55.2+ MB\n"
     ]
    }
   ],
   "source": [
    "payhistory.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>PID</th>\n",
       "      <th>PAMT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.205720e+06</td>\n",
       "      <td>1.205720e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.655267e+06</td>\n",
       "      <td>2.579925e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.480799e+06</td>\n",
       "      <td>3.725114e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.800000e+01</td>\n",
       "      <td>-2.436960e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.377468e+06</td>\n",
       "      <td>2.033000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.709602e+06</td>\n",
       "      <td>2.033000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.946880e+06</td>\n",
       "      <td>2.300000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.101978e+06</td>\n",
       "      <td>2.436960e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PID          PAMT\n",
       "count  1.205720e+06  1.205720e+06\n",
       "mean   2.655267e+06  2.579925e+01\n",
       "std    1.480799e+06  3.725114e+01\n",
       "min    3.800000e+01 -2.436960e+03\n",
       "25%    1.377468e+06  2.033000e+01\n",
       "50%    2.709602e+06  2.033000e+01\n",
       "75%    3.946880e+06  2.300000e+01\n",
       "max    5.101978e+06  2.436960e+03"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payhistory.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 2 columns):\n",
      "CLASCODE    8 non-null object\n",
      "CLASDESC    8 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 208.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "comclas.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLASCODE</th>\n",
       "      <th>CLASDESC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>AN</td>\n",
       "      <td>Reminder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CLASCODE  CLASDESC\n",
       "count         8         8\n",
       "unique        8         8\n",
       "top          AN  Reminder\n",
       "freq          1         1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comclas.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 2 columns):\n",
      "CLASCODE    8 non-null object\n",
      "CLASDESC    8 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 208.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "comclas.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLASCODE</th>\n",
       "      <th>CLASDESC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>AN</td>\n",
       "      <td>Reminder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CLASCODE  CLASDESC\n",
       "count         8         8\n",
       "unique        8         8\n",
       "top          AN  Reminder\n",
       "freq          1         1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comclas.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      "MNTOPCODE    30 non-null object\n",
      "MNTOPDESC    30 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 560.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "commaint.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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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       "\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>MNTOPCODE</th>\n",
       "      <th>MNTOPDESC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>DIS</td>\n",
       "      <td>General</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MNTOPCODE MNTOPDESC\n",
       "count         30        30\n",
       "unique        30        29\n",
       "top          DIS   General\n",
       "freq           1         2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commaint.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16 entries, 0 to 15\n",
      "Data columns (total 2 columns):\n",
      "MEDIUMCODE    16 non-null object\n",
      "MEDIUMDESC    16 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 336.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "commediu.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "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",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MEDIUMCODE</th>\n",
       "      <th>MEDIUMDESC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>TE</td>\n",
       "      <td>Telex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MEDIUMCODE MEDIUMDESC\n",
       "count          16         16\n",
       "unique         16         16\n",
       "top            TE      Telex\n",
       "freq            1          1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commediu.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16 entries, 0 to 15\n",
      "Data columns (total 2 columns):\n",
      "MEDIUMCODE    16 non-null object\n",
      "MEDIUMDESC    16 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 336.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "commediu.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "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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       "    }\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>CONTID</th>\n",
       "      <th>MEDIUMCODE</th>\n",
       "      <th>MNTOPCODE</th>\n",
       "      <th>CLASCODE</th>\n",
       "      <th>EXTRELNO</th>\n",
       "      <th>CONTDIREC</th>\n",
       "      <th>CONTDATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>763569</td>\n",
       "      <td>763569</td>\n",
       "      <td>763569</td>\n",
       "      <td>763569</td>\n",
       "      <td>763569</td>\n",
       "      <td>763569</td>\n",
       "      <td>763569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>763328</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>21932</td>\n",
       "      <td>2</td>\n",
       "      <td>3290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>004832282</td>\n",
       "      <td>CI</td>\n",
       "      <td>CTS</td>\n",
       "      <td>CT</td>\n",
       "      <td>0181194</td>\n",
       "      <td>O</td>\n",
       "      <td>2001-11-20 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2</td>\n",
       "      <td>446595</td>\n",
       "      <td>453839</td>\n",
       "      <td>459032</td>\n",
       "      <td>1008</td>\n",
       "      <td>733423</td>\n",
       "      <td>10760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</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>1993-11-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</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>2007-01-25 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           CONTID MEDIUMCODE MNTOPCODE CLASCODE EXTRELNO CONTDIREC  \\\n",
       "count      763569     763569    763569   763569   763569    763569   \n",
       "unique     763328         15        24        9    21932         2   \n",
       "top     004832282         CI       CTS       CT  0181194         O   \n",
       "freq            2     446595    453839   459032     1008    733423   \n",
       "first         NaN        NaN       NaN      NaN      NaN       NaN   \n",
       "last          NaN        NaN       NaN      NaN      NaN       NaN   \n",
       "\n",
       "                   CONTDATE  \n",
       "count                763569  \n",
       "unique                 3290  \n",
       "top     2001-11-20 00:00:00  \n",
       "freq                  10760  \n",
       "first   1993-11-02 00:00:00  \n",
       "last    2007-01-25 00:00:00  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communication.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26 entries, 0 to 25\n",
      "Data columns (total 2 columns):\n",
      "EXRELACTCD    26 non-null object\n",
      "EXRELACTDE    26 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 496.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "extrelty.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "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",
       "        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>EXRELACTCD</th>\n",
       "      <th>EXRELACTDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>PV</td>\n",
       "      <td>Board Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       EXRELACTCD    EXRELACTDE\n",
       "count          26            26\n",
       "unique         26            26\n",
       "top            PV  Board Member\n",
       "freq            1             1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrelty.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CONTID', 'MEDIUMCODE', 'MNTOPCODE', 'CLASCODE', 'EXTRELNO',\n",
       "       'CONTDIREC', 'CONTDATE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communication.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "763569"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(communication)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting data into timewindow defined.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "payhistory.sort_values('PDATE',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "communication.sort_values('CONTDATE',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "extrel.sort_values('EXRELDATEN',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data=payhistory[payhistory.PDATE<=pd.Timestamp(2005,12,3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data4=communication[communication.CONTDATE<=pd.Timestamp(2005,12,3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data5=extrel[extrel.EXRELDATEN<=pd.Timestamp(2005,12,3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "donors=(new_data4['EXTRELNO'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_data=(new_data4['EXTRELNO'].value_counts()).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_data.index.names = ['EXTRELNO']\n",
    "ind_data.rename(index={None:'EXTRELNO'},columns={'EXTRELNO':'Frequency'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayur Maheshwari\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "new_data4['recency']=pd.Timestamp(2005,12,3)-new_data4['CONTDATE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_data['recency']=new_data4.groupby('EXTRELNO').recency.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXTRELNO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0181194</th>\n",
       "      <td>760</td>\n",
       "      <td>11 days</td>\n",
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       "    <tr>\n",
       "      <th>0001773</th>\n",
       "      <td>545</td>\n",
       "      <td>10 days</td>\n",
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       "    <tr>\n",
       "      <th>0025455</th>\n",
       "      <td>476</td>\n",
       "      <td>10 days</td>\n",
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       "    <tr>\n",
       "      <th>0035750</th>\n",
       "      <td>435</td>\n",
       "      <td>12 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035231</th>\n",
       "      <td>373</td>\n",
       "      <td>40 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Frequency recency\n",
       "EXTRELNO                   \n",
       "0181194         760 11 days\n",
       "0001773         545 10 days\n",
       "0025455         476 10 days\n",
       "0035750         435 12 days\n",
       "0035231         373 40 days"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_data['sum']=new_data.groupby('EXTRELNO').PAMT.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXTRELNO</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>0181194</th>\n",
       "      <td>760</td>\n",
       "      <td>11 days</td>\n",
       "      <td>7412.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0001773</th>\n",
       "      <td>545</td>\n",
       "      <td>10 days</td>\n",
       "      <td>52168.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0025455</th>\n",
       "      <td>476</td>\n",
       "      <td>10 days</td>\n",
       "      <td>28243.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035750</th>\n",
       "      <td>435</td>\n",
       "      <td>12 days</td>\n",
       "      <td>14648.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035231</th>\n",
       "      <td>373</td>\n",
       "      <td>40 days</td>\n",
       "      <td>27620.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Frequency recency       sum\n",
       "EXTRELNO                             \n",
       "0181194         760 11 days   7412.56\n",
       "0001773         545 10 days  52168.79\n",
       "0025455         476 10 days  28243.57\n",
       "0035750         435 12 days  14648.45\n",
       "0035231         373 40 days  27620.49"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_data['avg']=new_data.groupby('EXTRELNO').PAMT.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>sum</th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXTRELNO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0181194</th>\n",
       "      <td>760</td>\n",
       "      <td>11 days</td>\n",
       "      <td>7412.56</td>\n",
       "      <td>23.020373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0001773</th>\n",
       "      <td>545</td>\n",
       "      <td>10 days</td>\n",
       "      <td>52168.79</td>\n",
       "      <td>20.259724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0025455</th>\n",
       "      <td>476</td>\n",
       "      <td>10 days</td>\n",
       "      <td>28243.57</td>\n",
       "      <td>110.759098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035750</th>\n",
       "      <td>435</td>\n",
       "      <td>12 days</td>\n",
       "      <td>14648.45</td>\n",
       "      <td>57.899012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035231</th>\n",
       "      <td>373</td>\n",
       "      <td>40 days</td>\n",
       "      <td>27620.49</td>\n",
       "      <td>20.160942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Frequency recency       sum         avg\n",
       "EXTRELNO                                         \n",
       "0181194         760 11 days   7412.56   23.020373\n",
       "0001773         545 10 days  52168.79   20.259724\n",
       "0025455         476 10 days  28243.57  110.759098\n",
       "0035750         435 12 days  14648.45   57.899012\n",
       "0035231         373 40 days  27620.49   20.160942"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=new_data\n",
    "temp.index=new_data.EXTRELNO\n",
    "temp1=temp['PAYTYPECD'].to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp2=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "curr_val=pd.get_dummies(temp1['PAYTYPECD'],prefix='PAYTYPECD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAYTYPECD_D    139\n",
       "PAYTYPECD_E      0\n",
       "PAYTYPECD_O      0\n",
       "PAYTYPECD_X      3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curr_val.loc['0000020'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "pay_type=curr_val.groupby('EXTRELNO').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15900"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pay_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_data=ind_data.join(pay_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "naeaddr.index=naeaddr['EXTRELNO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>sum</th>\n",
       "      <th>avg</th>\n",
       "      <th>PAYTYPECD_D</th>\n",
       "      <th>PAYTYPECD_E</th>\n",
       "      <th>PAYTYPECD_O</th>\n",
       "      <th>PAYTYPECD_X</th>\n",
       "      <th>LANGUACODE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXTRELNO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0181194</th>\n",
       "      <td>760</td>\n",
       "      <td>11 days</td>\n",
       "      <td>7412.56</td>\n",
       "      <td>23.020373</td>\n",
       "      <td>322.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0001773</th>\n",
       "      <td>545</td>\n",
       "      <td>10 days</td>\n",
       "      <td>52168.79</td>\n",
       "      <td>20.259724</td>\n",
       "      <td>2463.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0025455</th>\n",
       "      <td>476</td>\n",
       "      <td>10 days</td>\n",
       "      <td>28243.57</td>\n",
       "      <td>110.759098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035750</th>\n",
       "      <td>435</td>\n",
       "      <td>12 days</td>\n",
       "      <td>14648.45</td>\n",
       "      <td>57.899012</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035231</th>\n",
       "      <td>373</td>\n",
       "      <td>40 days</td>\n",
       "      <td>27620.49</td>\n",
       "      <td>20.160942</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0000955</th>\n",
       "      <td>312</td>\n",
       "      <td>162 days</td>\n",
       "      <td>5687.19</td>\n",
       "      <td>15.005778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0009970</th>\n",
       "      <td>308</td>\n",
       "      <td>54 days</td>\n",
       "      <td>2811.97</td>\n",
       "      <td>19.943050</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0033799</th>\n",
       "      <td>296</td>\n",
       "      <td>59 days</td>\n",
       "      <td>4157.86</td>\n",
       "      <td>50.705610</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0174473</th>\n",
       "      <td>296</td>\n",
       "      <td>16 days</td>\n",
       "      <td>5606.39</td>\n",
       "      <td>14.910612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0007417</th>\n",
       "      <td>294</td>\n",
       "      <td>19 days</td>\n",
       "      <td>8441.48</td>\n",
       "      <td>20.639315</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0033814</th>\n",
       "      <td>292</td>\n",
       "      <td>9 days</td>\n",
       "      <td>2801.57</td>\n",
       "      <td>19.869291</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0026161</th>\n",
       "      <td>283</td>\n",
       "      <td>65 days</td>\n",
       "      <td>6572.97</td>\n",
       "      <td>20.734921</td>\n",
       "      <td>314.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0176844</th>\n",
       "      <td>281</td>\n",
       "      <td>39 days</td>\n",
       "      <td>5481.74</td>\n",
       "      <td>21.002835</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0015180</th>\n",
       "      <td>277</td>\n",
       "      <td>184 days</td>\n",
       "      <td>5093.78</td>\n",
       "      <td>19.367985</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0012725</th>\n",
       "      <td>277</td>\n",
       "      <td>39 days</td>\n",
       "      <td>8399.14</td>\n",
       "      <td>19.624159</td>\n",
       "      <td>419.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0182761</th>\n",
       "      <td>273</td>\n",
       "      <td>10 days</td>\n",
       "      <td>2630.39</td>\n",
       "      <td>20.549922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0004858</th>\n",
       "      <td>267</td>\n",
       "      <td>199 days</td>\n",
       "      <td>5702.34</td>\n",
       "      <td>33.741657</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0206627</th>\n",
       "      <td>261</td>\n",
       "      <td>12 days</td>\n",
       "      <td>1735.87</td>\n",
       "      <td>21.698375</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0005613</th>\n",
       "      <td>261</td>\n",
       "      <td>23 days</td>\n",
       "      <td>2776.63</td>\n",
       "      <td>47.061525</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0003878</th>\n",
       "      <td>256</td>\n",
       "      <td>106 days</td>\n",
       "      <td>5662.43</td>\n",
       "      <td>20.008587</td>\n",
       "      <td>278.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0032313</th>\n",
       "      <td>255</td>\n",
       "      <td>18 days</td>\n",
       "      <td>11104.64</td>\n",
       "      <td>103.781682</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0003592</th>\n",
       "      <td>253</td>\n",
       "      <td>2 days</td>\n",
       "      <td>5449.78</td>\n",
       "      <td>20.487895</td>\n",
       "      <td>253.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0016448</th>\n",
       "      <td>248</td>\n",
       "      <td>3 days</td>\n",
       "      <td>2809.71</td>\n",
       "      <td>133.795714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0001964</th>\n",
       "      <td>247</td>\n",
       "      <td>141 days</td>\n",
       "      <td>6285.30</td>\n",
       "      <td>13.634056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0037931</th>\n",
       "      <td>246</td>\n",
       "      <td>9 days</td>\n",
       "      <td>7008.33</td>\n",
       "      <td>84.437711</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0022535</th>\n",
       "      <td>246</td>\n",
       "      <td>586 days</td>\n",
       "      <td>15638.70</td>\n",
       "      <td>110.131690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0036272</th>\n",
       "      <td>244</td>\n",
       "      <td>913 days</td>\n",
       "      <td>1876.18</td>\n",
       "      <td>41.692889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0009585</th>\n",
       "      <td>239</td>\n",
       "      <td>23 days</td>\n",
       "      <td>4857.83</td>\n",
       "      <td>17.794249</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0031118</th>\n",
       "      <td>236</td>\n",
       "      <td>8 days</td>\n",
       "      <td>9927.17</td>\n",
       "      <td>42.243277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035402</th>\n",
       "      <td>235</td>\n",
       "      <td>680 days</td>\n",
       "      <td>2066.99</td>\n",
       "      <td>48.069535</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NL</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0214311</th>\n",
       "      <td>1</td>\n",
       "      <td>2166 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0158767</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0184075</th>\n",
       "      <td>1</td>\n",
       "      <td>3722 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0159397</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0153062</th>\n",
       "      <td>1</td>\n",
       "      <td>4301 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0176629</th>\n",
       "      <td>1</td>\n",
       "      <td>3977 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0158591</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0159695</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0221526</th>\n",
       "      <td>1</td>\n",
       "      <td>1845 days</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>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0191129</th>\n",
       "      <td>1</td>\n",
       "      <td>3552 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0217259</th>\n",
       "      <td>1</td>\n",
       "      <td>2075 days</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>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0211088</th>\n",
       "      <td>1</td>\n",
       "      <td>2322 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0183859</th>\n",
       "      <td>1</td>\n",
       "      <td>3726 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0185040</th>\n",
       "      <td>1</td>\n",
       "      <td>3718 days</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>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0178400</th>\n",
       "      <td>1</td>\n",
       "      <td>3944 days</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>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0184326</th>\n",
       "      <td>1</td>\n",
       "      <td>3720 days</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>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0160449</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0229740</th>\n",
       "      <td>1</td>\n",
       "      <td>1058 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0163674</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0162251</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0180066</th>\n",
       "      <td>1</td>\n",
       "      <td>3874 days</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>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0163188</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0192065</th>\n",
       "      <td>1</td>\n",
       "      <td>3196 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0179385</th>\n",
       "      <td>1</td>\n",
       "      <td>3902 days</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>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0180803</th>\n",
       "      <td>1</td>\n",
       "      <td>3827 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0187235</th>\n",
       "      <td>1</td>\n",
       "      <td>3672 days</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>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0191072</th>\n",
       "      <td>1</td>\n",
       "      <td>3553 days</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>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0160266</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0211207</th>\n",
       "      <td>1</td>\n",
       "      <td>2307 days</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>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0190649</th>\n",
       "      <td>1</td>\n",
       "      <td>3574 days</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>FR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21306 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Frequency   recency       sum         avg  PAYTYPECD_D  PAYTYPECD_E  \\\n",
       "EXTRELNO                                                                        \n",
       "0181194         760   11 days   7412.56   23.020373        322.0          0.0   \n",
       "0001773         545   10 days  52168.79   20.259724       2463.0         42.0   \n",
       "0025455         476   10 days  28243.57  110.759098          0.0         10.0   \n",
       "0035750         435   12 days  14648.45   57.899012        232.0         10.0   \n",
       "0035231         373   40 days  27620.49   20.160942       1332.0          0.0   \n",
       "0000955         312  162 days   5687.19   15.005778          0.0          7.0   \n",
       "0009970         308   54 days   2811.97   19.943050        138.0          0.0   \n",
       "0033799         296   59 days   4157.86   50.705610         45.0          0.0   \n",
       "0174473         296   16 days   5606.39   14.910612          0.0          7.0   \n",
       "0007417         294   19 days   8441.48   20.639315        187.0          0.0   \n",
       "0033814         292    9 days   2801.57   19.869291        139.0          0.0   \n",
       "0026161         283   65 days   6572.97   20.734921        314.0          0.0   \n",
       "0176844         281   39 days   5481.74   21.002835        260.0          0.0   \n",
       "0015180         277  184 days   5093.78   19.367985        256.0          0.0   \n",
       "0012725         277   39 days   8399.14   19.624159        419.0          0.0   \n",
       "0182761         273   10 days   2630.39   20.549922          0.0          7.0   \n",
       "0004858         267  199 days   5702.34   33.741657        162.0          0.0   \n",
       "0206627         261   12 days   1735.87   21.698375         79.0          0.0   \n",
       "0005613         261   23 days   2776.63   47.061525         30.0          2.0   \n",
       "0003878         256  106 days   5662.43   20.008587        278.0          0.0   \n",
       "0032313         255   18 days  11104.64  103.781682          0.0          0.0   \n",
       "0003592         253    2 days   5449.78   20.487895        253.0          0.0   \n",
       "0016448         248    3 days   2809.71  133.795714          0.0          0.0   \n",
       "0001964         247  141 days   6285.30   13.634056          0.0         11.0   \n",
       "0037931         246    9 days   7008.33   84.437711          0.0          1.0   \n",
       "0022535         246  586 days  15638.70  110.131690          0.0          0.0   \n",
       "0036272         244  913 days   1876.18   41.692889          0.0          0.0   \n",
       "0009585         239   23 days   4857.83   17.794249        152.0          0.0   \n",
       "0031118         236    8 days   9927.17   42.243277          0.0          2.0   \n",
       "0035402         235  680 days   2066.99   48.069535         41.0          0.0   \n",
       "...             ...       ...       ...         ...          ...          ...   \n",
       "0214311           1 2166 days       NaN         NaN          NaN          NaN   \n",
       "0158767           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0184075           1 3722 days       NaN         NaN          NaN          NaN   \n",
       "0159397           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0153062           1 4301 days       NaN         NaN          NaN          NaN   \n",
       "0176629           1 3977 days       NaN         NaN          NaN          NaN   \n",
       "0158591           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0159695           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0221526           1 1845 days       NaN         NaN          NaN          NaN   \n",
       "0191129           1 3552 days       NaN         NaN          NaN          NaN   \n",
       "0217259           1 2075 days       NaN         NaN          NaN          NaN   \n",
       "0211088           1 2322 days       NaN         NaN          NaN          NaN   \n",
       "0183859           1 3726 days       NaN         NaN          NaN          NaN   \n",
       "0185040           1 3718 days       NaN         NaN          NaN          NaN   \n",
       "0178400           1 3944 days       NaN         NaN          NaN          NaN   \n",
       "0184326           1 3720 days       NaN         NaN          NaN          NaN   \n",
       "0160449           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0229740           1 1058 days       NaN         NaN          NaN          NaN   \n",
       "0163674           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0162251           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0180066           1 3874 days       NaN         NaN          NaN          NaN   \n",
       "0163188           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0192065           1 3196 days       NaN         NaN          NaN          NaN   \n",
       "0179385           1 3902 days       NaN         NaN          NaN          NaN   \n",
       "0180803           1 3827 days       NaN         NaN          NaN          NaN   \n",
       "0187235           1 3672 days       NaN         NaN          NaN          NaN   \n",
       "0191072           1 3553 days       NaN         NaN          NaN          NaN   \n",
       "0160266           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0211207           1 2307 days       NaN         NaN          NaN          NaN   \n",
       "0190649           1 3574 days       NaN         NaN          NaN          NaN   \n",
       "\n",
       "          PAYTYPECD_O  PAYTYPECD_X LANGUACODE  \n",
       "EXTRELNO                                       \n",
       "0181194           0.0          0.0         NL  \n",
       "0001773          15.0         25.0         NL  \n",
       "0025455         214.0         10.0         NL  \n",
       "0035750           0.0          4.0         NL  \n",
       "0035231           0.0         28.0         NL  \n",
       "0000955         361.0          9.0         NL  \n",
       "0009970           1.0          2.0         NL  \n",
       "0033799          24.0          4.0         NL  \n",
       "0174473         366.0          0.0         NL  \n",
       "0007417         212.0          8.0         NL  \n",
       "0033814           0.0          2.0         NL  \n",
       "0026161           0.0          2.0         NL  \n",
       "0176844           0.0          0.0         NL  \n",
       "0015180           0.0          6.0         NL  \n",
       "0012725           0.0          6.0         NL  \n",
       "0182761         117.0          0.0         NL  \n",
       "0004858           0.0          7.0         FR  \n",
       "0206627           1.0          0.0         NL  \n",
       "0005613          16.0          4.0         NL  \n",
       "0003878           0.0          4.0         NL  \n",
       "0032313          93.0         11.0         NL  \n",
       "0003592          10.0          2.0         NL  \n",
       "0016448          15.0          3.0         NL  \n",
       "0001964         437.0          9.0         NL  \n",
       "0037931          71.0          2.0         NL  \n",
       "0022535         112.0          9.0         NL  \n",
       "0036272          40.0          2.0         NL  \n",
       "0009585         110.0         10.0         NL  \n",
       "0031118         215.0         14.0         NL  \n",
       "0035402           0.0          2.0         NL  \n",
       "...               ...          ...        ...  \n",
       "0214311           NaN          NaN         NL  \n",
       "0158767           NaN          NaN         NL  \n",
       "0184075           NaN          NaN         NL  \n",
       "0159397           NaN          NaN         NL  \n",
       "0153062           NaN          NaN         NL  \n",
       "0176629           NaN          NaN         NL  \n",
       "0158591           NaN          NaN         NL  \n",
       "0159695           NaN          NaN         NL  \n",
       "0221526           NaN          NaN         FR  \n",
       "0191129           NaN          NaN         NL  \n",
       "0217259           NaN          NaN         FR  \n",
       "0211088           NaN          NaN         NL  \n",
       "0183859           NaN          NaN         NL  \n",
       "0185040           NaN          NaN         FR  \n",
       "0178400           NaN          NaN         FR  \n",
       "0184326           NaN          NaN         FR  \n",
       "0160449           NaN          NaN         NL  \n",
       "0229740           NaN          NaN         NL  \n",
       "0163674           NaN          NaN         NL  \n",
       "0162251           NaN          NaN         NL  \n",
       "0180066           NaN          NaN         FR  \n",
       "0163188           NaN          NaN         NL  \n",
       "0192065           NaN          NaN         NL  \n",
       "0179385           NaN          NaN         FR  \n",
       "0180803           NaN          NaN         NL  \n",
       "0187235           NaN          NaN         FR  \n",
       "0191072           NaN          NaN         FR  \n",
       "0160266           NaN          NaN         NL  \n",
       "0211207           NaN          NaN         NL  \n",
       "0190649           NaN          NaN         FR  \n",
       "\n",
       "[21306 rows x 9 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_data.join(naeaddr['LANGUACODE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "complaint_data=new_data4[new_data4.CLASCODE=='CM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "complaint_data.index=complaint_data['EXTRELNO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=ind_data\n",
    "temp=temp.join(complaint_data['CLASCODE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "curr_val1=pd.get_dummies(temp['CLASCODE'],prefix='CLASCODE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "curr_val1=curr_val1.groupby(curr_val1.index).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_data=ind_data.join(curr_val1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>Frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>sum</th>\n",
       "      <th>avg</th>\n",
       "      <th>PAYTYPECD_D</th>\n",
       "      <th>PAYTYPECD_E</th>\n",
       "      <th>PAYTYPECD_O</th>\n",
       "      <th>PAYTYPECD_X</th>\n",
       "      <th>CLASCODE_CM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXTRELNO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0181194</th>\n",
       "      <td>760</td>\n",
       "      <td>11 days</td>\n",
       "      <td>7412.56</td>\n",
       "      <td>23.020373</td>\n",
       "      <td>322.0</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>0001773</th>\n",
       "      <td>545</td>\n",
       "      <td>10 days</td>\n",
       "      <td>52168.79</td>\n",
       "      <td>20.259724</td>\n",
       "      <td>2463.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0025455</th>\n",
       "      <td>476</td>\n",
       "      <td>10 days</td>\n",
       "      <td>28243.57</td>\n",
       "      <td>110.759098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035750</th>\n",
       "      <td>435</td>\n",
       "      <td>12 days</td>\n",
       "      <td>14648.45</td>\n",
       "      <td>57.899012</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035231</th>\n",
       "      <td>373</td>\n",
       "      <td>40 days</td>\n",
       "      <td>27620.49</td>\n",
       "      <td>20.160942</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0000955</th>\n",
       "      <td>312</td>\n",
       "      <td>162 days</td>\n",
       "      <td>5687.19</td>\n",
       "      <td>15.005778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0009970</th>\n",
       "      <td>308</td>\n",
       "      <td>54 days</td>\n",
       "      <td>2811.97</td>\n",
       "      <td>19.943050</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0033799</th>\n",
       "      <td>296</td>\n",
       "      <td>59 days</td>\n",
       "      <td>4157.86</td>\n",
       "      <td>50.705610</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0174473</th>\n",
       "      <td>296</td>\n",
       "      <td>16 days</td>\n",
       "      <td>5606.39</td>\n",
       "      <td>14.910612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0007417</th>\n",
       "      <td>294</td>\n",
       "      <td>19 days</td>\n",
       "      <td>8441.48</td>\n",
       "      <td>20.639315</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0033814</th>\n",
       "      <td>292</td>\n",
       "      <td>9 days</td>\n",
       "      <td>2801.57</td>\n",
       "      <td>19.869291</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0026161</th>\n",
       "      <td>283</td>\n",
       "      <td>65 days</td>\n",
       "      <td>6572.97</td>\n",
       "      <td>20.734921</td>\n",
       "      <td>314.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0176844</th>\n",
       "      <td>281</td>\n",
       "      <td>39 days</td>\n",
       "      <td>5481.74</td>\n",
       "      <td>21.002835</td>\n",
       "      <td>260.0</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>0015180</th>\n",
       "      <td>277</td>\n",
       "      <td>184 days</td>\n",
       "      <td>5093.78</td>\n",
       "      <td>19.367985</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0012725</th>\n",
       "      <td>277</td>\n",
       "      <td>39 days</td>\n",
       "      <td>8399.14</td>\n",
       "      <td>19.624159</td>\n",
       "      <td>419.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0182761</th>\n",
       "      <td>273</td>\n",
       "      <td>10 days</td>\n",
       "      <td>2630.39</td>\n",
       "      <td>20.549922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0004858</th>\n",
       "      <td>267</td>\n",
       "      <td>199 days</td>\n",
       "      <td>5702.34</td>\n",
       "      <td>33.741657</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0206627</th>\n",
       "      <td>261</td>\n",
       "      <td>12 days</td>\n",
       "      <td>1735.87</td>\n",
       "      <td>21.698375</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0005613</th>\n",
       "      <td>261</td>\n",
       "      <td>23 days</td>\n",
       "      <td>2776.63</td>\n",
       "      <td>47.061525</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0003878</th>\n",
       "      <td>256</td>\n",
       "      <td>106 days</td>\n",
       "      <td>5662.43</td>\n",
       "      <td>20.008587</td>\n",
       "      <td>278.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0032313</th>\n",
       "      <td>255</td>\n",
       "      <td>18 days</td>\n",
       "      <td>11104.64</td>\n",
       "      <td>103.781682</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0003592</th>\n",
       "      <td>253</td>\n",
       "      <td>2 days</td>\n",
       "      <td>5449.78</td>\n",
       "      <td>20.487895</td>\n",
       "      <td>253.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0016448</th>\n",
       "      <td>248</td>\n",
       "      <td>3 days</td>\n",
       "      <td>2809.71</td>\n",
       "      <td>133.795714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0001964</th>\n",
       "      <td>247</td>\n",
       "      <td>141 days</td>\n",
       "      <td>6285.30</td>\n",
       "      <td>13.634056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0037931</th>\n",
       "      <td>246</td>\n",
       "      <td>9 days</td>\n",
       "      <td>7008.33</td>\n",
       "      <td>84.437711</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0022535</th>\n",
       "      <td>246</td>\n",
       "      <td>586 days</td>\n",
       "      <td>15638.70</td>\n",
       "      <td>110.131690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0036272</th>\n",
       "      <td>244</td>\n",
       "      <td>913 days</td>\n",
       "      <td>1876.18</td>\n",
       "      <td>41.692889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0009585</th>\n",
       "      <td>239</td>\n",
       "      <td>23 days</td>\n",
       "      <td>4857.83</td>\n",
       "      <td>17.794249</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0031118</th>\n",
       "      <td>236</td>\n",
       "      <td>8 days</td>\n",
       "      <td>9927.17</td>\n",
       "      <td>42.243277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035402</th>\n",
       "      <td>235</td>\n",
       "      <td>680 days</td>\n",
       "      <td>2066.99</td>\n",
       "      <td>48.069535</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0214311</th>\n",
       "      <td>1</td>\n",
       "      <td>2166 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0158767</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0184075</th>\n",
       "      <td>1</td>\n",
       "      <td>3722 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0159397</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0153062</th>\n",
       "      <td>1</td>\n",
       "      <td>4301 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0176629</th>\n",
       "      <td>1</td>\n",
       "      <td>3977 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0158591</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0159695</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0221526</th>\n",
       "      <td>1</td>\n",
       "      <td>1845 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0191129</th>\n",
       "      <td>1</td>\n",
       "      <td>3552 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0217259</th>\n",
       "      <td>1</td>\n",
       "      <td>2075 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0211088</th>\n",
       "      <td>1</td>\n",
       "      <td>2322 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0183859</th>\n",
       "      <td>1</td>\n",
       "      <td>3726 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0185040</th>\n",
       "      <td>1</td>\n",
       "      <td>3718 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0178400</th>\n",
       "      <td>1</td>\n",
       "      <td>3944 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0184326</th>\n",
       "      <td>1</td>\n",
       "      <td>3720 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0160449</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0229740</th>\n",
       "      <td>1</td>\n",
       "      <td>1058 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0163674</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0162251</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0180066</th>\n",
       "      <td>1</td>\n",
       "      <td>3874 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0163188</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0192065</th>\n",
       "      <td>1</td>\n",
       "      <td>3196 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0179385</th>\n",
       "      <td>1</td>\n",
       "      <td>3902 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0180803</th>\n",
       "      <td>1</td>\n",
       "      <td>3827 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0187235</th>\n",
       "      <td>1</td>\n",
       "      <td>3672 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0191072</th>\n",
       "      <td>1</td>\n",
       "      <td>3553 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0160266</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0211207</th>\n",
       "      <td>1</td>\n",
       "      <td>2307 days</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0190649</th>\n",
       "      <td>1</td>\n",
       "      <td>3574 days</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21306 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Frequency   recency       sum         avg  PAYTYPECD_D  PAYTYPECD_E  \\\n",
       "EXTRELNO                                                                        \n",
       "0181194         760   11 days   7412.56   23.020373        322.0          0.0   \n",
       "0001773         545   10 days  52168.79   20.259724       2463.0         42.0   \n",
       "0025455         476   10 days  28243.57  110.759098          0.0         10.0   \n",
       "0035750         435   12 days  14648.45   57.899012        232.0         10.0   \n",
       "0035231         373   40 days  27620.49   20.160942       1332.0          0.0   \n",
       "0000955         312  162 days   5687.19   15.005778          0.0          7.0   \n",
       "0009970         308   54 days   2811.97   19.943050        138.0          0.0   \n",
       "0033799         296   59 days   4157.86   50.705610         45.0          0.0   \n",
       "0174473         296   16 days   5606.39   14.910612          0.0          7.0   \n",
       "0007417         294   19 days   8441.48   20.639315        187.0          0.0   \n",
       "0033814         292    9 days   2801.57   19.869291        139.0          0.0   \n",
       "0026161         283   65 days   6572.97   20.734921        314.0          0.0   \n",
       "0176844         281   39 days   5481.74   21.002835        260.0          0.0   \n",
       "0015180         277  184 days   5093.78   19.367985        256.0          0.0   \n",
       "0012725         277   39 days   8399.14   19.624159        419.0          0.0   \n",
       "0182761         273   10 days   2630.39   20.549922          0.0          7.0   \n",
       "0004858         267  199 days   5702.34   33.741657        162.0          0.0   \n",
       "0206627         261   12 days   1735.87   21.698375         79.0          0.0   \n",
       "0005613         261   23 days   2776.63   47.061525         30.0          2.0   \n",
       "0003878         256  106 days   5662.43   20.008587        278.0          0.0   \n",
       "0032313         255   18 days  11104.64  103.781682          0.0          0.0   \n",
       "0003592         253    2 days   5449.78   20.487895        253.0          0.0   \n",
       "0016448         248    3 days   2809.71  133.795714          0.0          0.0   \n",
       "0001964         247  141 days   6285.30   13.634056          0.0         11.0   \n",
       "0037931         246    9 days   7008.33   84.437711          0.0          1.0   \n",
       "0022535         246  586 days  15638.70  110.131690          0.0          0.0   \n",
       "0036272         244  913 days   1876.18   41.692889          0.0          0.0   \n",
       "0009585         239   23 days   4857.83   17.794249        152.0          0.0   \n",
       "0031118         236    8 days   9927.17   42.243277          0.0          2.0   \n",
       "0035402         235  680 days   2066.99   48.069535         41.0          0.0   \n",
       "...             ...       ...       ...         ...          ...          ...   \n",
       "0214311           1 2166 days       NaN         NaN          NaN          NaN   \n",
       "0158767           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0184075           1 3722 days       NaN         NaN          NaN          NaN   \n",
       "0159397           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0153062           1 4301 days       NaN         NaN          NaN          NaN   \n",
       "0176629           1 3977 days       NaN         NaN          NaN          NaN   \n",
       "0158591           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0159695           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0221526           1 1845 days       NaN         NaN          NaN          NaN   \n",
       "0191129           1 3552 days       NaN         NaN          NaN          NaN   \n",
       "0217259           1 2075 days       NaN         NaN          NaN          NaN   \n",
       "0211088           1 2322 days       NaN         NaN          NaN          NaN   \n",
       "0183859           1 3726 days       NaN         NaN          NaN          NaN   \n",
       "0185040           1 3718 days       NaN         NaN          NaN          NaN   \n",
       "0178400           1 3944 days       NaN         NaN          NaN          NaN   \n",
       "0184326           1 3720 days       NaN         NaN          NaN          NaN   \n",
       "0160449           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0229740           1 1058 days       NaN         NaN          NaN          NaN   \n",
       "0163674           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0162251           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0180066           1 3874 days       NaN         NaN          NaN          NaN   \n",
       "0163188           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0192065           1 3196 days       NaN         NaN          NaN          NaN   \n",
       "0179385           1 3902 days       NaN         NaN          NaN          NaN   \n",
       "0180803           1 3827 days       NaN         NaN          NaN          NaN   \n",
       "0187235           1 3672 days       NaN         NaN          NaN          NaN   \n",
       "0191072           1 3553 days       NaN         NaN          NaN          NaN   \n",
       "0160266           1 4022 days       NaN         NaN          NaN          NaN   \n",
       "0211207           1 2307 days       NaN         NaN          NaN          NaN   \n",
       "0190649           1 3574 days       NaN         NaN          NaN          NaN   \n",
       "\n",
       "          PAYTYPECD_O  PAYTYPECD_X  CLASCODE_CM  \n",
       "EXTRELNO                                         \n",
       "0181194           0.0          0.0            0  \n",
       "0001773          15.0         25.0            0  \n",
       "0025455         214.0         10.0            0  \n",
       "0035750           0.0          4.0            0  \n",
       "0035231           0.0         28.0            1  \n",
       "0000955         361.0          9.0            0  \n",
       "0009970           1.0          2.0            2  \n",
       "0033799          24.0          4.0            0  \n",
       "0174473         366.0          0.0            0  \n",
       "0007417         212.0          8.0            1  \n",
       "0033814           0.0          2.0            0  \n",
       "0026161           0.0          2.0            0  \n",
       "0176844           0.0          0.0            0  \n",
       "0015180           0.0          6.0            1  \n",
       "0012725           0.0          6.0            0  \n",
       "0182761         117.0          0.0            0  \n",
       "0004858           0.0          7.0            0  \n",
       "0206627           1.0          0.0            1  \n",
       "0005613          16.0          4.0            1  \n",
       "0003878           0.0          4.0            0  \n",
       "0032313          93.0         11.0            0  \n",
       "0003592          10.0          2.0            0  \n",
       "0016448          15.0          3.0            2  \n",
       "0001964         437.0          9.0            0  \n",
       "0037931          71.0          2.0            1  \n",
       "0022535         112.0          9.0            0  \n",
       "0036272          40.0          2.0            1  \n",
       "0009585         110.0         10.0            3  \n",
       "0031118         215.0         14.0            1  \n",
       "0035402           0.0          2.0            0  \n",
       "...               ...          ...          ...  \n",
       "0214311           NaN          NaN            0  \n",
       "0158767           NaN          NaN            0  \n",
       "0184075           NaN          NaN            0  \n",
       "0159397           NaN          NaN            0  \n",
       "0153062           NaN          NaN            0  \n",
       "0176629           NaN          NaN            0  \n",
       "0158591           NaN          NaN            0  \n",
       "0159695           NaN          NaN            0  \n",
       "0221526           NaN          NaN            0  \n",
       "0191129           NaN          NaN            0  \n",
       "0217259           NaN          NaN            0  \n",
       "0211088           NaN          NaN            0  \n",
       "0183859           NaN          NaN            0  \n",
       "0185040           NaN          NaN            0  \n",
       "0178400           NaN          NaN            0  \n",
       "0184326           NaN          NaN            0  \n",
       "0160449           NaN          NaN            0  \n",
       "0229740           NaN          NaN            0  \n",
       "0163674           NaN          NaN            0  \n",
       "0162251           NaN          NaN            0  \n",
       "0180066           NaN          NaN            0  \n",
       "0163188           NaN          NaN            0  \n",
       "0192065           NaN          NaN            0  \n",
       "0179385           NaN          NaN            0  \n",
       "0180803           NaN          NaN            0  \n",
       "0187235           NaN          NaN            0  \n",
       "0191072           NaN          NaN            0  \n",
       "0160266           NaN          NaN            0  \n",
       "0211207           NaN          NaN            0  \n",
       "0190649           NaN          NaN            0  \n",
       "\n",
       "[21306 rows x 9 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "inco_com_data=new_data4[new_data4.CONTDIREC=='I']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "inco_com_data.index=inco_com_data['EXTRELNO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=ind_data\n",
    "temp=temp.join(inco_com_data['CONTDIREC'])\n",
    "curr_val2=pd.get_dummies(temp['CONTDIREC'],prefix='CONTDIREC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['0000020', '0000035', '0000042', '0000043', '0000060', '0000072',\n",
       "       '0000081', '0000089', '0000091', '0000100',\n",
       "       ...\n",
       "       '0240664', '0240678', '0240679', '0240684', '0240685', '0240689',\n",
       "       '0240696', '0240698', '0240700', '0240752'],\n",
       "      dtype='object', name='EXTRELNO', length=21306)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curr_val2.index.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21306"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ind_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Frequency', 'recency', 'sum', 'avg', 'PAYTYPECD_D', 'PAYTYPECD_E',\n",
       "       'PAYTYPECD_O', 'PAYTYPECD_X', 'CLASCODE_CM'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_X</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_O</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_E</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_D</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLASCODE_CM</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recency</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frequency</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total    Percent\n",
       "PAYTYPECD_X   6393  30.005632\n",
       "PAYTYPECD_O   6393  30.005632\n",
       "PAYTYPECD_E   6393  30.005632\n",
       "PAYTYPECD_D   6393  30.005632\n",
       "avg           6393  30.005632\n",
       "sum           6393  30.005632\n",
       "CLASCODE_CM      0   0.000000\n",
       "recency          0   0.000000\n",
       "Frequency        0   0.000000"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = ind_data.isnull().sum(axis=0).sort_values(ascending=False)\n",
    "percent = ((ind_data.isnull().sum(axis=0)/ind_data.isnull().count(axis=0))*100).sort_values(ascending=False)\n",
    "\n",
    "# count the number of null values in the column and their perecentage of the total data\n",
    "missing_data_columns = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data_columns.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>sum</th>\n",
       "      <th>avg</th>\n",
       "      <th>PAYTYPECD_D</th>\n",
       "      <th>PAYTYPECD_E</th>\n",
       "      <th>PAYTYPECD_O</th>\n",
       "      <th>PAYTYPECD_X</th>\n",
       "      <th>CLASCODE_CM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXTRELNO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0181194</th>\n",
       "      <td>760</td>\n",
       "      <td>11 days</td>\n",
       "      <td>7412.56</td>\n",
       "      <td>23.020373</td>\n",
       "      <td>322.0</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>0001773</th>\n",
       "      <td>545</td>\n",
       "      <td>10 days</td>\n",
       "      <td>52168.79</td>\n",
       "      <td>20.259724</td>\n",
       "      <td>2463.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0025455</th>\n",
       "      <td>476</td>\n",
       "      <td>10 days</td>\n",
       "      <td>28243.57</td>\n",
       "      <td>110.759098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035750</th>\n",
       "      <td>435</td>\n",
       "      <td>12 days</td>\n",
       "      <td>14648.45</td>\n",
       "      <td>57.899012</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035231</th>\n",
       "      <td>373</td>\n",
       "      <td>40 days</td>\n",
       "      <td>27620.49</td>\n",
       "      <td>20.160942</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0000955</th>\n",
       "      <td>312</td>\n",
       "      <td>162 days</td>\n",
       "      <td>5687.19</td>\n",
       "      <td>15.005778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0009970</th>\n",
       "      <td>308</td>\n",
       "      <td>54 days</td>\n",
       "      <td>2811.97</td>\n",
       "      <td>19.943050</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0033799</th>\n",
       "      <td>296</td>\n",
       "      <td>59 days</td>\n",
       "      <td>4157.86</td>\n",
       "      <td>50.705610</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0174473</th>\n",
       "      <td>296</td>\n",
       "      <td>16 days</td>\n",
       "      <td>5606.39</td>\n",
       "      <td>14.910612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0007417</th>\n",
       "      <td>294</td>\n",
       "      <td>19 days</td>\n",
       "      <td>8441.48</td>\n",
       "      <td>20.639315</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0033814</th>\n",
       "      <td>292</td>\n",
       "      <td>9 days</td>\n",
       "      <td>2801.57</td>\n",
       "      <td>19.869291</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0026161</th>\n",
       "      <td>283</td>\n",
       "      <td>65 days</td>\n",
       "      <td>6572.97</td>\n",
       "      <td>20.734921</td>\n",
       "      <td>314.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0176844</th>\n",
       "      <td>281</td>\n",
       "      <td>39 days</td>\n",
       "      <td>5481.74</td>\n",
       "      <td>21.002835</td>\n",
       "      <td>260.0</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>0015180</th>\n",
       "      <td>277</td>\n",
       "      <td>184 days</td>\n",
       "      <td>5093.78</td>\n",
       "      <td>19.367985</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0012725</th>\n",
       "      <td>277</td>\n",
       "      <td>39 days</td>\n",
       "      <td>8399.14</td>\n",
       "      <td>19.624159</td>\n",
       "      <td>419.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0182761</th>\n",
       "      <td>273</td>\n",
       "      <td>10 days</td>\n",
       "      <td>2630.39</td>\n",
       "      <td>20.549922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0004858</th>\n",
       "      <td>267</td>\n",
       "      <td>199 days</td>\n",
       "      <td>5702.34</td>\n",
       "      <td>33.741657</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0206627</th>\n",
       "      <td>261</td>\n",
       "      <td>12 days</td>\n",
       "      <td>1735.87</td>\n",
       "      <td>21.698375</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0005613</th>\n",
       "      <td>261</td>\n",
       "      <td>23 days</td>\n",
       "      <td>2776.63</td>\n",
       "      <td>47.061525</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0003878</th>\n",
       "      <td>256</td>\n",
       "      <td>106 days</td>\n",
       "      <td>5662.43</td>\n",
       "      <td>20.008587</td>\n",
       "      <td>278.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Frequency  recency       sum         avg  PAYTYPECD_D  PAYTYPECD_E  \\\n",
       "EXTRELNO                                                                       \n",
       "0181194         760  11 days   7412.56   23.020373        322.0          0.0   \n",
       "0001773         545  10 days  52168.79   20.259724       2463.0         42.0   \n",
       "0025455         476  10 days  28243.57  110.759098          0.0         10.0   \n",
       "0035750         435  12 days  14648.45   57.899012        232.0         10.0   \n",
       "0035231         373  40 days  27620.49   20.160942       1332.0          0.0   \n",
       "0000955         312 162 days   5687.19   15.005778          0.0          7.0   \n",
       "0009970         308  54 days   2811.97   19.943050        138.0          0.0   \n",
       "0033799         296  59 days   4157.86   50.705610         45.0          0.0   \n",
       "0174473         296  16 days   5606.39   14.910612          0.0          7.0   \n",
       "0007417         294  19 days   8441.48   20.639315        187.0          0.0   \n",
       "0033814         292   9 days   2801.57   19.869291        139.0          0.0   \n",
       "0026161         283  65 days   6572.97   20.734921        314.0          0.0   \n",
       "0176844         281  39 days   5481.74   21.002835        260.0          0.0   \n",
       "0015180         277 184 days   5093.78   19.367985        256.0          0.0   \n",
       "0012725         277  39 days   8399.14   19.624159        419.0          0.0   \n",
       "0182761         273  10 days   2630.39   20.549922          0.0          7.0   \n",
       "0004858         267 199 days   5702.34   33.741657        162.0          0.0   \n",
       "0206627         261  12 days   1735.87   21.698375         79.0          0.0   \n",
       "0005613         261  23 days   2776.63   47.061525         30.0          2.0   \n",
       "0003878         256 106 days   5662.43   20.008587        278.0          0.0   \n",
       "\n",
       "          PAYTYPECD_O  PAYTYPECD_X  CLASCODE_CM  \n",
       "EXTRELNO                                         \n",
       "0181194           0.0          0.0            0  \n",
       "0001773          15.0         25.0            0  \n",
       "0025455         214.0         10.0            0  \n",
       "0035750           0.0          4.0            0  \n",
       "0035231           0.0         28.0            1  \n",
       "0000955         361.0          9.0            0  \n",
       "0009970           1.0          2.0            2  \n",
       "0033799          24.0          4.0            0  \n",
       "0174473         366.0          0.0            0  \n",
       "0007417         212.0          8.0            1  \n",
       "0033814           0.0          2.0            0  \n",
       "0026161           0.0          2.0            0  \n",
       "0176844           0.0          0.0            0  \n",
       "0015180           0.0          6.0            1  \n",
       "0012725           0.0          6.0            0  \n",
       "0182761         117.0          0.0            0  \n",
       "0004858           0.0          7.0            0  \n",
       "0206627           1.0          0.0            1  \n",
       "0005613          16.0          4.0            1  \n",
       "0003878           0.0          4.0            0  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_data['PAYTYPECD_X'].fillna(value=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Percent</th>\n",
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       "  <tbody>\n",
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       "      <th>Frequency</th>\n",
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       "      <th>recency</th>\n",
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       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
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       "    <tr>\n",
       "      <th>avg</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_D</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_E</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_O</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_X</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLASCODE_CM</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total    Percent\n",
       "Frequency        0   0.000000\n",
       "recency          0   0.000000\n",
       "sum           6393  30.005632\n",
       "avg           6393  30.005632\n",
       "PAYTYPECD_D   6393  30.005632\n",
       "PAYTYPECD_E   6393  30.005632\n",
       "PAYTYPECD_O   6393  30.005632\n",
       "PAYTYPECD_X      0   0.000000\n",
       "CLASCODE_CM      0   0.000000"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = ind_data.isnull().sum()\n",
    "percent = ((total/ind_data.isnull().count())*100)\n",
    "missing_data_columns = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data_columns.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_data['PAYTYPECD_O'].fillna(value=0,inplace=True)\n",
    "ind_data['PAYTYPECD_D'].fillna(value=0,inplace=True)\n",
    "ind_data['PAYTYPECD_E'].fillna(value=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Frequency</th>\n",
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       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg</th>\n",
       "      <td>6393</td>\n",
       "      <td>30.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_D</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_E</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>PAYTYPECD_O</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYTYPECD_X</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLASCODE_CM</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total    Percent\n",
       "Frequency        0   0.000000\n",
       "recency          0   0.000000\n",
       "sum           6393  30.005632\n",
       "avg           6393  30.005632\n",
       "PAYTYPECD_D      0   0.000000\n",
       "PAYTYPECD_E      0   0.000000\n",
       "PAYTYPECD_O      0   0.000000\n",
       "PAYTYPECD_X      0   0.000000\n",
       "CLASCODE_CM      0   0.000000"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = ind_data.isnull().sum()\n",
    "percent = ((total/ind_data.isnull().count())*100)\n",
    "missing_data_columns = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data_columns.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_mean = Imputer(missing_values = 'NaN', strategy='mean')\n",
    "imp_mean.fit(ind_data[['avg']])\n",
    "ind_data['avg'] = imp_mean.transform(ind_data[['avg']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_mean = Imputer(missing_values = 'NaN', strategy='mean')\n",
    "imp_mean.fit(ind_data[['sum']])\n",
    "ind_data['sum'] = imp_mean.transform(ind_data[['sum']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>PAYTYPECD_D</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "             Total  Percent\n",
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       "recency          0      0.0\n",
       "sum              0      0.0\n",
       "avg              0      0.0\n",
       "PAYTYPECD_D      0      0.0\n",
       "PAYTYPECD_E      0      0.0\n",
       "PAYTYPECD_O      0      0.0\n",
       "PAYTYPECD_X      0      0.0\n",
       "CLASCODE_CM      0      0.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = ind_data.isnull().sum()\n",
    "percent = ((total/ind_data.isnull().count())*100)\n",
    "missing_data_columns = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data_columns.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data5.index=new_data5['EXTRELNO']\n",
    "ind_data.drop(new_data5.index,errors='ignore',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep_data=extrel[((extrel['EXRELDATEN']>=pd.Timestamp(2006,1,1)) & (extrel['EXRELDATEN']<=pd.Timestamp(2006,12,31))) | (extrel['EXRELDATEN'].isnull()==True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep_data.index=dep_data['EXTRELNO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep_data=dep_data[dep_data['EXTRELSTDT']<pd.Timestamp(2006,1,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp3=dep_data[dep_data['EXRELDATEN'].isnull()==False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dep_data=pd.DataFrame(index=ind_data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayur Maheshwari\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "temp3['EXRELDATEN']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dep_data=new_dep_data.join(temp3['EXRELDATEN'])\n",
    "new_dep_data.fillna(value=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dep_data['EXTRELNO']=new_dep_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dep_data.drop_duplicates(subset='EXTRELNO',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "          EXRELDATEN EXTRELNO\n",
       "EXTRELNO                     \n",
       "0000020          0.0  0000020\n",
       "0000035          0.0  0000035\n",
       "0000042          0.0  0000042\n",
       "0000043          0.0  0000043\n",
       "0000060          0.0  0000060"
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     "metadata": {},
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    "new_dep_data.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "13843"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
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    "len(new_dep_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dep_data.drop('EXTRELNO',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0001773</th>\n",
       "      <td>545</td>\n",
       "      <td>10 days</td>\n",
       "      <td>52168.790000</td>\n",
       "      <td>20.259724</td>\n",
       "      <td>2463.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>0025455</th>\n",
       "      <td>476</td>\n",
       "      <td>10 days</td>\n",
       "      <td>28243.570000</td>\n",
       "      <td>110.759098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>0035750</th>\n",
       "      <td>435</td>\n",
       "      <td>12 days</td>\n",
       "      <td>14648.450000</td>\n",
       "      <td>57.899012</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0035231</th>\n",
       "      <td>373</td>\n",
       "      <td>40 days</td>\n",
       "      <td>27620.490000</td>\n",
       "      <td>20.160942</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>0000955</th>\n",
       "      <td>312</td>\n",
       "      <td>162 days</td>\n",
       "      <td>5687.190000</td>\n",
       "      <td>15.005778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0009970</th>\n",
       "      <td>308</td>\n",
       "      <td>54 days</td>\n",
       "      <td>2811.970000</td>\n",
       "      <td>19.943050</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0033799</th>\n",
       "      <td>296</td>\n",
       "      <td>59 days</td>\n",
       "      <td>4157.860000</td>\n",
       "      <td>50.705610</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>0174473</th>\n",
       "      <td>296</td>\n",
       "      <td>16 days</td>\n",
       "      <td>5606.390000</td>\n",
       "      <td>14.910612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0007417</th>\n",
       "      <td>294</td>\n",
       "      <td>19 days</td>\n",
       "      <td>8441.480000</td>\n",
       "      <td>20.639315</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0033814</th>\n",
       "      <td>292</td>\n",
       "      <td>9 days</td>\n",
       "      <td>2801.570000</td>\n",
       "      <td>19.869291</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0026161</th>\n",
       "      <td>283</td>\n",
       "      <td>65 days</td>\n",
       "      <td>6572.970000</td>\n",
       "      <td>20.734921</td>\n",
       "      <td>314.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0015180</th>\n",
       "      <td>277</td>\n",
       "      <td>184 days</td>\n",
       "      <td>5093.780000</td>\n",
       "      <td>19.367985</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0012725</th>\n",
       "      <td>277</td>\n",
       "      <td>39 days</td>\n",
       "      <td>8399.140000</td>\n",
       "      <td>19.624159</td>\n",
       "      <td>419.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0004858</th>\n",
       "      <td>267</td>\n",
       "      <td>199 days</td>\n",
       "      <td>5702.340000</td>\n",
       "      <td>33.741657</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0005613</th>\n",
       "      <td>261</td>\n",
       "      <td>23 days</td>\n",
       "      <td>2776.630000</td>\n",
       "      <td>47.061525</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0003878</th>\n",
       "      <td>256</td>\n",
       "      <td>106 days</td>\n",
       "      <td>5662.430000</td>\n",
       "      <td>20.008587</td>\n",
       "      <td>278.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0003592</th>\n",
       "      <td>253</td>\n",
       "      <td>2 days</td>\n",
       "      <td>5449.780000</td>\n",
       "      <td>20.487895</td>\n",
       "      <td>253.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0016448</th>\n",
       "      <td>248</td>\n",
       "      <td>3 days</td>\n",
       "      <td>2809.710000</td>\n",
       "      <td>133.795714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0001964</th>\n",
       "      <td>247</td>\n",
       "      <td>141 days</td>\n",
       "      <td>6285.300000</td>\n",
       "      <td>13.634056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0037931</th>\n",
       "      <td>246</td>\n",
       "      <td>9 days</td>\n",
       "      <td>7008.330000</td>\n",
       "      <td>84.437711</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0022535</th>\n",
       "      <td>246</td>\n",
       "      <td>586 days</td>\n",
       "      <td>15638.700000</td>\n",
       "      <td>110.131690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0009585</th>\n",
       "      <td>239</td>\n",
       "      <td>23 days</td>\n",
       "      <td>4857.830000</td>\n",
       "      <td>17.794249</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0031118</th>\n",
       "      <td>236</td>\n",
       "      <td>8 days</td>\n",
       "      <td>9927.170000</td>\n",
       "      <td>42.243277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0024839</th>\n",
       "      <td>234</td>\n",
       "      <td>124 days</td>\n",
       "      <td>2787.640000</td>\n",
       "      <td>19.631268</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0001156</th>\n",
       "      <td>232</td>\n",
       "      <td>2095 days</td>\n",
       "      <td>2385.280000</td>\n",
       "      <td>37.270000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0021623</th>\n",
       "      <td>230</td>\n",
       "      <td>17 days</td>\n",
       "      <td>5625.210000</td>\n",
       "      <td>66.178941</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0003892</th>\n",
       "      <td>229</td>\n",
       "      <td>102 days</td>\n",
       "      <td>2843.810000</td>\n",
       "      <td>58.036939</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0010151</th>\n",
       "      <td>227</td>\n",
       "      <td>106 days</td>\n",
       "      <td>2671.210000</td>\n",
       "      <td>78.565000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0004286</th>\n",
       "      <td>227</td>\n",
       "      <td>234 days</td>\n",
       "      <td>2820.040000</td>\n",
       "      <td>19.859437</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0010617</th>\n",
       "      <td>227</td>\n",
       "      <td>89 days</td>\n",
       "      <td>2767.140000</td>\n",
       "      <td>18.824082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>2.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0214311</th>\n",
       "      <td>1</td>\n",
       "      <td>2166 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0158767</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0184075</th>\n",
       "      <td>1</td>\n",
       "      <td>3722 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0159397</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0153062</th>\n",
       "      <td>1</td>\n",
       "      <td>4301 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0176629</th>\n",
       "      <td>1</td>\n",
       "      <td>3977 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0158591</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0159695</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0221526</th>\n",
       "      <td>1</td>\n",
       "      <td>1845 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0191129</th>\n",
       "      <td>1</td>\n",
       "      <td>3552 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0217259</th>\n",
       "      <td>1</td>\n",
       "      <td>2075 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0211088</th>\n",
       "      <td>1</td>\n",
       "      <td>2322 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0183859</th>\n",
       "      <td>1</td>\n",
       "      <td>3726 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0185040</th>\n",
       "      <td>1</td>\n",
       "      <td>3718 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0178400</th>\n",
       "      <td>1</td>\n",
       "      <td>3944 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0184326</th>\n",
       "      <td>1</td>\n",
       "      <td>3720 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0160449</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0229740</th>\n",
       "      <td>1</td>\n",
       "      <td>1058 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0163674</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0162251</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0180066</th>\n",
       "      <td>1</td>\n",
       "      <td>3874 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0163188</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0192065</th>\n",
       "      <td>1</td>\n",
       "      <td>3196 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0179385</th>\n",
       "      <td>1</td>\n",
       "      <td>3902 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0180803</th>\n",
       "      <td>1</td>\n",
       "      <td>3827 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0187235</th>\n",
       "      <td>1</td>\n",
       "      <td>3672 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0191072</th>\n",
       "      <td>1</td>\n",
       "      <td>3553 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0160266</th>\n",
       "      <td>1</td>\n",
       "      <td>4022 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0211207</th>\n",
       "      <td>1</td>\n",
       "      <td>2307 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0190649</th>\n",
       "      <td>1</td>\n",
       "      <td>3574 days</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13843 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Frequency   recency           sum         avg  PAYTYPECD_D  \\\n",
       "EXTRELNO                                                               \n",
       "0001773         545   10 days  52168.790000   20.259724       2463.0   \n",
       "0025455         476   10 days  28243.570000  110.759098          0.0   \n",
       "0035750         435   12 days  14648.450000   57.899012        232.0   \n",
       "0035231         373   40 days  27620.490000   20.160942       1332.0   \n",
       "0000955         312  162 days   5687.190000   15.005778          0.0   \n",
       "0009970         308   54 days   2811.970000   19.943050        138.0   \n",
       "0033799         296   59 days   4157.860000   50.705610         45.0   \n",
       "0174473         296   16 days   5606.390000   14.910612          0.0   \n",
       "0007417         294   19 days   8441.480000   20.639315        187.0   \n",
       "0033814         292    9 days   2801.570000   19.869291        139.0   \n",
       "0026161         283   65 days   6572.970000   20.734921        314.0   \n",
       "0015180         277  184 days   5093.780000   19.367985        256.0   \n",
       "0012725         277   39 days   8399.140000   19.624159        419.0   \n",
       "0004858         267  199 days   5702.340000   33.741657        162.0   \n",
       "0005613         261   23 days   2776.630000   47.061525         30.0   \n",
       "0003878         256  106 days   5662.430000   20.008587        278.0   \n",
       "0003592         253    2 days   5449.780000   20.487895        253.0   \n",
       "0016448         248    3 days   2809.710000  133.795714          0.0   \n",
       "0001964         247  141 days   6285.300000   13.634056          0.0   \n",
       "0037931         246    9 days   7008.330000   84.437711          0.0   \n",
       "0022535         246  586 days  15638.700000  110.131690          0.0   \n",
       "0009585         239   23 days   4857.830000   17.794249        152.0   \n",
       "0031118         236    8 days   9927.170000   42.243277          0.0   \n",
       "0024839         234  124 days   2787.640000   19.631268        139.0   \n",
       "0001156         232 2095 days   2385.280000   37.270000         38.0   \n",
       "0021623         230   17 days   5625.210000   66.178941         74.0   \n",
       "0003892         229  102 days   2843.810000   58.036939         43.0   \n",
       "0010151         227  106 days   2671.210000   78.565000          0.0   \n",
       "0004286         227  234 days   2820.040000   19.859437        139.0   \n",
       "0010617         227   89 days   2767.140000   18.824082          0.0   \n",
       "...             ...       ...           ...         ...          ...   \n",
       "0214311           1 2166 days   1863.360956   39.434505          0.0   \n",
       "0158767           1 4022 days   1863.360956   39.434505          0.0   \n",
       "0184075           1 3722 days   1863.360956   39.434505          0.0   \n",
       "0159397           1 4022 days   1863.360956   39.434505          0.0   \n",
       "0153062           1 4301 days   1863.360956   39.434505          0.0   \n",
       "0176629           1 3977 days   1863.360956   39.434505          0.0   \n",
       "0158591           1 4022 days   1863.360956   39.434505          0.0   \n",
       "0159695           1 4022 days   1863.360956   39.434505          0.0   \n",
       "0221526           1 1845 days   1863.360956   39.434505          0.0   \n",
       "0191129           1 3552 days   1863.360956   39.434505          0.0   \n",
       "0217259           1 2075 days   1863.360956   39.434505          0.0   \n",
       "0211088           1 2322 days   1863.360956   39.434505          0.0   \n",
       "0183859           1 3726 days   1863.360956   39.434505          0.0   \n",
       "0185040           1 3718 days   1863.360956   39.434505          0.0   \n",
       "0178400           1 3944 days   1863.360956   39.434505          0.0   \n",
       "0184326           1 3720 days   1863.360956   39.434505          0.0   \n",
       "0160449           1 4022 days   1863.360956   39.434505          0.0   \n",
       "0229740           1 1058 days   1863.360956   39.434505          0.0   \n",
       "0163674           1 4022 days   1863.360956   39.434505          0.0   \n",
       "0162251           1 4022 days   1863.360956   39.434505          0.0   \n",
       "0180066           1 3874 days   1863.360956   39.434505          0.0   \n",
       "0163188           1 4022 days   1863.360956   39.434505          0.0   \n",
       "0192065           1 3196 days   1863.360956   39.434505          0.0   \n",
       "0179385           1 3902 days   1863.360956   39.434505          0.0   \n",
       "0180803           1 3827 days   1863.360956   39.434505          0.0   \n",
       "0187235           1 3672 days   1863.360956   39.434505          0.0   \n",
       "0191072           1 3553 days   1863.360956   39.434505          0.0   \n",
       "0160266           1 4022 days   1863.360956   39.434505          0.0   \n",
       "0211207           1 2307 days   1863.360956   39.434505          0.0   \n",
       "0190649           1 3574 days   1863.360956   39.434505          0.0   \n",
       "\n",
       "          PAYTYPECD_E  PAYTYPECD_O  PAYTYPECD_X  CLASCODE_CM  \n",
       "EXTRELNO                                                      \n",
       "0001773          42.0         15.0         25.0            0  \n",
       "0025455          10.0        214.0         10.0            0  \n",
       "0035750          10.0          0.0          4.0            0  \n",
       "0035231           0.0          0.0         28.0            1  \n",
       "0000955           7.0        361.0          9.0            0  \n",
       "0009970           0.0          1.0          2.0            2  \n",
       "0033799           0.0         24.0          4.0            0  \n",
       "0174473           7.0        366.0          0.0            0  \n",
       "0007417           0.0        212.0          8.0            1  \n",
       "0033814           0.0          0.0          2.0            0  \n",
       "0026161           0.0          0.0          2.0            0  \n",
       "0015180           0.0          0.0          6.0            1  \n",
       "0012725           0.0          0.0          6.0            0  \n",
       "0004858           0.0          0.0          7.0            0  \n",
       "0005613           2.0         16.0          4.0            1  \n",
       "0003878           0.0          0.0          4.0            0  \n",
       "0003592           0.0         10.0          2.0            0  \n",
       "0016448           0.0         15.0          3.0            2  \n",
       "0001964          11.0        437.0          9.0            0  \n",
       "0037931           1.0         71.0          2.0            1  \n",
       "0022535           0.0        112.0          9.0            0  \n",
       "0009585           0.0        110.0         10.0            3  \n",
       "0031118           2.0        215.0         14.0            1  \n",
       "0024839           0.0          0.0          2.0            0  \n",
       "0001156           0.0         16.0          8.0            1  \n",
       "0021623           0.0          0.0          5.0            0  \n",
       "0003892           3.0          0.0          3.0            0  \n",
       "0010151           1.0         28.0          3.0            0  \n",
       "0004286           0.0          0.0          2.0            0  \n",
       "0010617           7.0        135.0          2.0            0  \n",
       "...               ...          ...          ...          ...  \n",
       "0214311           0.0          0.0          1.0            0  \n",
       "0158767           0.0          0.0          1.0            0  \n",
       "0184075           0.0          0.0          1.0            0  \n",
       "0159397           0.0          0.0          1.0            0  \n",
       "0153062           0.0          0.0          1.0            0  \n",
       "0176629           0.0          0.0          1.0            0  \n",
       "0158591           0.0          0.0          1.0            0  \n",
       "0159695           0.0          0.0          1.0            0  \n",
       "0221526           0.0          0.0          1.0            0  \n",
       "0191129           0.0          0.0          1.0            0  \n",
       "0217259           0.0          0.0          1.0            0  \n",
       "0211088           0.0          0.0          1.0            0  \n",
       "0183859           0.0          0.0          1.0            0  \n",
       "0185040           0.0          0.0          1.0            0  \n",
       "0178400           0.0          0.0          1.0            0  \n",
       "0184326           0.0          0.0          1.0            0  \n",
       "0160449           0.0          0.0          1.0            0  \n",
       "0229740           0.0          0.0          1.0            0  \n",
       "0163674           0.0          0.0          1.0            0  \n",
       "0162251           0.0          0.0          1.0            0  \n",
       "0180066           0.0          0.0          1.0            0  \n",
       "0163188           0.0          0.0          1.0            0  \n",
       "0192065           0.0          0.0          1.0            0  \n",
       "0179385           0.0          0.0          1.0            0  \n",
       "0180803           0.0          0.0          1.0            0  \n",
       "0187235           0.0          0.0          1.0            0  \n",
       "0191072           0.0          0.0          1.0            0  \n",
       "0160266           0.0          0.0          1.0            0  \n",
       "0211207           0.0          0.0          1.0            0  \n",
       "0190649           0.0          0.0          1.0            0  \n",
       "\n",
       "[13843 rows x 9 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "e=pd.Series(index=ind_data.index)\n",
    "for num in range(len(ind_data['recency'])):\n",
    "    e[num]=ind_data['recency'][num].days\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_data['recency']=e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here ends the data pre-processing phase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>Frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>sum</th>\n",
       "      <th>avg</th>\n",
       "      <th>PAYTYPECD_D</th>\n",
       "      <th>PAYTYPECD_E</th>\n",
       "      <th>PAYTYPECD_O</th>\n",
       "      <th>PAYTYPECD_X</th>\n",
       "      <th>CLASCODE_CM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13843.000000</td>\n",
       "      <td>13843.000000</td>\n",
       "      <td>13843.000000</td>\n",
       "      <td>13843.000000</td>\n",
       "      <td>13843.000000</td>\n",
       "      <td>13843.000000</td>\n",
       "      <td>13843.000000</td>\n",
       "      <td>13843.000000</td>\n",
       "      <td>13843.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.471357</td>\n",
       "      <td>1443.940331</td>\n",
       "      <td>2052.044434</td>\n",
       "      <td>38.563599</td>\n",
       "      <td>37.488622</td>\n",
       "      <td>0.875966</td>\n",
       "      <td>10.255725</td>\n",
       "      <td>1.558333</td>\n",
       "      <td>0.079607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>35.446168</td>\n",
       "      <td>1560.562357</td>\n",
       "      <td>1111.473332</td>\n",
       "      <td>30.874239</td>\n",
       "      <td>63.694098</td>\n",
       "      <td>4.378611</td>\n",
       "      <td>32.920834</td>\n",
       "      <td>1.345406</td>\n",
       "      <td>0.393220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-276.000000</td>\n",
       "      <td>-138.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>19.922908</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>1863.360956</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>3006.000000</td>\n",
       "      <td>2782.690000</td>\n",
       "      <td>39.434505</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>545.000000</td>\n",
       "      <td>4414.000000</td>\n",
       "      <td>52168.790000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>2463.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>829.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Frequency       recency           sum           avg   PAYTYPECD_D  \\\n",
       "count  13843.000000  13843.000000  13843.000000  13843.000000  13843.000000   \n",
       "mean      30.471357   1443.940331   2052.044434     38.563599     37.488622   \n",
       "std       35.446168   1560.562357   1111.473332     30.874239     63.694098   \n",
       "min        1.000000      1.000000   -276.000000   -138.000000      0.000000   \n",
       "25%        1.000000    102.000000   1863.360956     19.922908      0.000000   \n",
       "50%       18.000000    470.000000   1863.360956     39.434505      0.000000   \n",
       "75%       55.000000   3006.000000   2782.690000     39.434505     57.000000   \n",
       "max      545.000000   4414.000000  52168.790000    360.000000   2463.000000   \n",
       "\n",
       "        PAYTYPECD_E   PAYTYPECD_O   PAYTYPECD_X   CLASCODE_CM  \n",
       "count  13843.000000  13843.000000  13843.000000  13843.000000  \n",
       "mean       0.875966     10.255725      1.558333      0.079607  \n",
       "std        4.378611     32.920834      1.345406      0.393220  \n",
       "min        0.000000      0.000000      0.000000      0.000000  \n",
       "25%        0.000000      0.000000      1.000000      0.000000  \n",
       "50%        0.000000      0.000000      1.000000      0.000000  \n",
       "75%        0.000000      0.000000      2.000000      0.000000  \n",
       "max      137.000000    829.000000     28.000000      6.000000  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2326e8dffd0>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ind_data['recency'],new_dep_data['EXRELDATEN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2326e9cbda0>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ind_data['avg'],new_dep_data['EXRELDATEN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2326ea42b00>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H64DjI5pFff370x8GPlBVHxn1PMPQ/+vyp4Dl+/H0y+cKYHv/XvUe4OeS/O1oR1q8qjre//0E8FFmbs+Oo2lgeuBvg3cxE/tlMW5xPwBsSnJZ/8WIncDeEc/0pNZ/EfI9wOGq+pNRz7MUSSaSrO5/vQp4OfDl0U61cFV1fVWtq6qNzPwZ+WRVvX7EYy1Kkqf3X6infwvj54GxfIdZVf0XcCzJ5f1TLwOW7Y0HK5frgZdDVZ1Oci2wH1gB3F5Vh0Y81qIk+RDwUmBNkmngbVX1ntFOtShXAL8MPNC/Vw3wu1W1b4QzLdalwPv678p6CnBnVY312wgb8KPAR2euIVgJfLCqPjHakZbkTcAH+henR4FfX64nGqu3QkqSuhm32zKSpA6MuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ16P8Bn/M+NTkvj6AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ind_data['CLASCODE_CM'],new_dep_data['EXRELDATEN'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modelling Phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2326eaf8ba8>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gwbzTlsxcRPrW/TQccw2p63eQ/NUPNHl0MEGVQ2n+7/sBOH4giU1DnnI4+UlyvaT88yWiX3oKgjykzf6C7J17qPb3YWT9vJWMpSuoMmggFTu2hZwcvKnHSJkQYH0A8Ho5/sk0wkY+Bh4P2d9/jTd+LyH9biB37zZyN68iuGt/KjQ/P6/qm57K8fdfcDp1YbleDj4yjcbvPAZBHg5/+DWZ2/ZS694byNi4jaNfr6L2uJvwhIVyxit5BaTsA4nsvvVxh4P/QYF1YuGOwHZr7U4AY8wH5BWBCn4jbgb89qG2CCh8nqI/wBQ378ofjDHHrLXhJ902DGhvrb2zwG13A7f4rh4DbrTW7jDGPAgMAfYA+4GfrLXPGGOaAh/62n7ja9/QGOMBHidv8GeARGAgefPnRllrL/M931TgB2vtW8aYDsBLQCXy5s/1stYe87XbAtxjrf2yDN212Uk7f+dfKLAERzWmQkjdUzcMYDlZeeP39GduOUXLwBY26t+8Xi8Q5x2V3a37ZwCQNmWow0nKp/K4twFYXOtqh5OUT2zCR+xt39PpGOXS4IeFAKTeU/QUSG5S5fk5bGjo7j603D0Hip2I+OfJ3LzQb4OZis17lto3Y8zfgL7W2lt81wcD5580tnkf+N5a+4Ix5krgEyDKWlvyHIVTcKxCd/JgznfbW5w0J81a+wJQ5KuPtXYyMBnAGDOhwO1bgIJHnT7ku90LjPddClrsu/x2/zsL/H810Onk5zbG1CFvt25gnUVSRETkr8iP56EzxowACs6lmm6tLThZvrgB38kDzlHAVF8hayl5ewzLNYkx0He5BhxjzBDyBpL3+QaJIiIi8hfhG7yVdrTjfqB+gev1gIMFG1hrD5J3lg6MMeHAVdback1k/J8Y0FlrJ/jxud4B3vHX84mIiMgpBNYcutXAWb5Tox0g7zRr1xdsYIyJAlJ8haFxwJvlfdJAPMpVRERExJV8B0/eSd7BmD8DH1prNxtjJhpjLvc1iwW2GmN+AWrhm0JWHv8TFToRERH567I2sH7BwVo7D5h30m2PFPj/x8DHp/M5VaETERERcTlV6ERERMTddIyiKnQiIiIibqcBnYiIiIjLaZeriIiIuFtgnbbEEarQiYiIiLicKnQiIiLibjooQhU6EREREbdThU5ERETczRtYJxZ2gip0IiIiIi6nCp2IiIi4m+bQqUInIiIi4naq0ImIiIi76Tx0qtCJiIiIuJ0qdCIiIuJumkOnCp2IiIiI26lCJyIiIu6mOXSq0ImIiIi4nSp0IiIi4m6q0KlCJyIiIuJ2xlrrdIa/Av2RRUTkr8T488mOL3vXb5+zoV0G+7VvZaVdriIiIuJq1uY6HcFxGtD5SYWQuk5HKJecrANkJ+10Oka5BEc1BqBnvT4OJymfhfvnM+WMG52OUS7j9swAoElUW4eTlM+OpDUA3NnwGoeTlM/U3bO45oyBTscol1l7PgXgLpevixd3z2Jsw+ucjlEuT+2e6XSEvyQN6ERERMTddFCEDooQERERcTtV6ERERMTd9NNfqtCJiIiIuJ0qdCIiIuJumkOnCp2IiIiI26lCJyIiIu6mOXSq0ImIiIi4nSp0IiIi4m6aQ6cKnYiIiIjbqUInIiIi7qY5dKrQiYiIiLidKnQiIiLibppDpwqdiIiIiNtpQCciIiLictrlKiIiIu6mXa6q0ImIiIi4nSp0IiIi4m46bYkqdCIiIiJupwqdiIiIuJvm0KlCJyIiIuJ2qtCJiIiIu2kOnSp0IiIiIm6nCp3L/Ou5ifTr24P0jAyGD7+Xtes2FWkTHBzMiy88TrduF+L1enn4kaf473/nOZC2qIeeeI6ly1cRGVGdT2dMK7LcWsuU56exbMVqQkMrMvnB+2l2zpkOJC2bOybezvk9OpCZkcnT9z7Dtk3bi7SZMmMyNaIjCQoKYuOqTbz44FS8ATTfo/eEwTTp3prsjEw+HzWdhE27i7TpOvpqWlzZmdBqlXm22S3+D1kGjzwxmthencnIOM6YfzzK5g1bSmz72ox/0eCMuvTrMsiPCU/tb48Oo3n3NmRlZPLuqFfZv3lXoeXBoSEMf+Veos6ohc31snHhj8x+aqZDaUs2bMIttOnejsyMTF4d9SK7Nu0stDwkNIR7Xx1DrQYxeL1efvx6NTOfetehtCW76tFhNPOtj/eKWR8Al466ho5XdiWsWjijmw91IGXpLn90KOd0b012RhYfjnqVg5t3F2lz8ahBtL2yK5WqVeaR5jf5P+TpEEDvqU5Rhc5F+vXtwVlnNqJps87cdttYXp46pdh248fdRWJiMs2ad6FFy1iWLl3h56QlG3hJb6Y993iJy5etWM3e/QeZN+sNJoy5i0nPTPVjut+nY48O1GtUlyGdb+K5sc9z95S7im03aeRkRvS5jeE9R1CtRjW6XdbVz0lL1qR7KyIaxTCt2/18Me4N+j4+rNh2279ew1sDHvVvuN8httdFNGzcgB4dB/DgfY8z8Z/jSmzb59IepKel+zFd2TSLbU3NRjE8Fns3M8e/zrWThxfbbuHrn/N4z/t48tKxNG53Ds1iW/s5aelad29HTKPa3N3tNl4f9wrDHx9ZbLvPp3/KfT3vZOwl93FO+3NpHdvWz0lL99v6mBR7N7PGv86gEtbH5oVreHbAg35OVzbnxLYmqlEM/4y9l/+Mf50rSujDzwvXMHXAQ35OJ6ebawd0Jo9r8/8R/ftfzLvvfQzA96vWUK16NWJioou0Gzb0Wp586iUgr+KVnHzYrzlL0751C6pVrVLi8kXfruTyvj0xxtDqvHNJTT1GYlKKHxOW3UV9LmT+xwsA+HnNFsKrViYyOrJIu/RjeYOHoApBBAcHY631a87SnNW7HZs++RaAg2t3ULFqZSpHVy/S7uDaHaQdOuLveGXWq18s//3wcwDW/biRqtWqULNWVJF2YZUrMfy2G3j52X/7O+IptezTgVX/WQrA7rXbqFSlMlVrFl4X2cez2LZiMwC52bns27yL6jFFX3NO6tC7I0s/WQzAtrW/ULlqZapHRxRqk3U8i80r8vYu5GbnsGvTDiJjavg7aqlalGF9/LbsaGJgbhvN+7Tjx/8sA2Dv2u1UqhJGlWL6sHftdlIDtA9lZr3+uwQoVw2IjDENjTE/G2NeAdYAg40xK4wxa4wxHxljwn3tOhhjvjPGrDfGrDLGVDHGBBlj/mmMWW2M2WCM+buvbawxZrEx5mNjzBZjzHvGGFPK4ywzxrQukGm5MaalP/pft04M+/cdPHH9wP446taJKdSmWrWqAEycMIZV33/JBzNfIzq66AdboEpITCamQN5a0VEkJCY5mKhkUTE1SDyYeOJ6YlwSUSV8KD054wk+Wfch6WnpLJ27zF8RT6lKTARHDyafuJ4an0KVWhGl3CMw1aodzcEDCSeuxx88REztmkXa3Tvudt54ZQYZGcf9Ga9MqteK4HCBdXEkPrnUwVqlqmG06NmOrcuLTrtwUkRMJMkH87fZ5PhkImuV3I+wqpVp16sDm5Zv8Ee8MqtWK4IjJ62PagE2eD6VqrUi+bVAH36NT6Gqy/ogZeeqAZ3POcA7QG9gONDLWtsW+AG4zxgTAswC7rbWtgJ6ARm+tr9aazsAHYBbjTGNfI/ZBrgHaAY0Bi4q5XH+DQwDMMacDVS01vrlncg3zizk5GpPhQpB1K9fh+UrVtPx/L6sXPkjTz/1iD/inRbFVa+K63dAKHZ9FN/0gRvHc3W7awkOCabNRQG0i6y4v20AVRDLqizdOPe8szmjUX3mz1vkn1C/Vxm27994gjwMe/EuFr/1Jcn7Dv3ZyX6X4t+nim/rCfJw10v38eX/zeXQvoTiGzmk2Pcdt20b/wt9KCuv13+XAOXGgyL2WGtXGmMuI28Atty34YUAK8gb8MVZa1cDWGuPAhhj+gAtjTF/8z1ONeAsIAtYZa3d72u3DmgI/FrC43wEPGyMGQ3cDLxVXEhjzAhgBMBrr732hzt728ihDB9+AwA//LCOevXrnFhWt15tDsYVfhNMTj5MWlo6n376BQAff/I5N9107R9+fn+LiY4i/lD+t/uEQ0lERwXOrpgBQ/tzyfWXALB1/VZq1smvAtWsHUVyQnJJdyU7M5sV81dy4cUX8OOyNX961pK0HdKL1td2ByBuw06q1sn/+1aJiSQ1gHetFnTjzYO4ZvAVAGxct5k6dWvxo29ZTJ1oEuITC7Vv06El57U6lyVrPieoQhA1oiJ577Pp3DBghJ+T5+s6uA8XXtcTgD3rdxBRYF1Uj6nBrwnFT5e4bsoIEnfFs/jNwDjYqc+QfvS8tg8AOzZso0ad/Cp7jZgaHD5U/LSJEU/eTvyuOOa9OccvOU+ly+A+XOBbH3vX76B6GddHILlgcG86XtcDgP3rd1KtQB+qxURy1AV9kD/GjQO6NN+/Blhgrb2u4ELf7s/ivoIY4B/W2q9Oah8LZBa4KZe8v4sp7nGstenGmAXAAGAQ0L64kNba6cD0367efudjpfeqBK9Oe5tXp70NwCX9enL7bcOYNeszzu/YlqO/HiU+vui388/nLiC224UsWrycHt078/PP2/7QczshtnMnZn4yh369urFh8xbCwytTMypwdhF89vYcPns778Pn/B4dGXjTABZ9tphz2zYlLTWNlJM+uELDQgkLDyPlUAqeIA8de3Rg4ypnd5Gteedr1rzzNQBNerSm3dDe/DR7BXXaNCEzNT2g58oVNOPND5nx5ocAxPbuzJDh1zDnP1/Rul0LUo8eIzGh8K769//vY97/v7w5qHXr1+bf77/g6GAOYOm781n67nwAmndvQ9ehF/Pj7O9o2OYsMlLTi52bddn911CpShjvj/3jXxRPt/nvfMH8d/K+RLbp0Y6Lh17Cd7OXcVabs0lPTePIoaKDiGtGXU9Ylcq8NuZlf8ct0bJ357PMtz6a+dbHGt/6OF7C+gg0K95dwIp38+b2Nu3ehguH9mH97O9o0OZMjqemu3+uXEkCuHLmL24c0P1mJfCyMeZMa+12Y0wYUA/YAtQxxnSw1q42xlQhb1fpV8BtxphvrLXZvt2lB0p5/GIfx1qbQ95u1znAMmut32bsz/tiIX379mDrz8tJz8jgllvuO7Hsh9Xzad8h7xvyuPGTefv/XuTZZyeQlJjC8Fvv9VfEUxr96JOsXruBI0eO0nPgjdw+fDA5OTkAXHPFpXS9oAPLVqym36CbqRQayqTxgZP9ZN9/s4rze3Tk3W/f4vjxTP553zMnlr321av8/eLbqBQWyqQ3HyOkYjAej4e1361jzrufO5i6sB3frKNJ91aMXPos2RlZzB01/cSym+dN5s1L8o7e6z7uWpoNuJDgSiHcsfJF1n+wmG+f/49TsYtYvOBbYnt15pvVn3E84zhj75pwYtmcRTPp3/26ku8cIDYvWkvz7m14dMkLZGdkMWP0qyeWPTDvKZ68ZCzVYyLp+48rid9+gLFznwRgydtfsWLWN07FLmLtNz/Spns7Xlg6jSzfaUt+89S8fzH2knuJjKnBlf8YxIHt+3hy7nMAfPXOXL754GunYhfxk299PLLkBbIysnivwPoYM+8pnr5kLACXP3AD7QdcRHClECaueIUVs77hi+c/dip2IVsWreWc7q0Zs+R5sjIy+Wh0/peAu+dN4YVL8o4G7/fA9bTxbd/jV0xl1axFfP38J07Flj/IBNIRd6dijGkIfG6tPc93vQfwFFDR1+Qha+1sY0wH4CWgEnmDuV5AOvA40J+86lsiMJC8+XOjrLWX+R5zKvCDtfat4h7HWnvM124LcI+19ssyRLcVQuqWs/fOysk6QHbSzlM3DGDBUY0B6Fmvj8NJymfh/vlMOeNGp2OUy7g9MwBoEhVYp6r4vXYk5e06v7PhNQ4nKZ+pu2dxzRkDnY5RLrP2fArAXS5fFy/unsXYhoH/BaQ0T+2eCXmfs36T8eFEvw1mKg16JCAndruqQmet3Q2cV+D6N+Qd4HByu9VAp2IeYrzvUtBi3+W3+955qscxxtQh74CS+b8jvoiIiPwZXFSc+rO48ShXRxljhgDfAw9aG8AnpBEREZG/DFdV6AKBtfYd8k6bIiIiIoFAB0WoQiciIiLidqrQiYiIiLupQqcKnYgdoj6yAAAgAElEQVSIiIjbqUInIiIi7qZjFFWhExEREXE7VehERETE3TSHThU6EREREbdThU5ERETcTb8UoQqdiIiIiNupQiciIiLupjl0qtCJiIiIuJ0qdCIiIuJuqtCpQiciIiLidqrQiYiIiLvplyJUoRMRERFxOw3oRERERFxOu1xFRETE1axXJxZWhU5ERETE5VShExEREXfTaUtUoRMRERFxO1XoRERExN102hJV6ERERETcThU6ERERcTcd5YqxVn8EP9AfWURE/kqMP58s/eU7/fY5G3bHVL/2raxUofOT9GducTpCuYSN+jc96/VxOka5LNw/H4DspJ0OJymf4KjGfFT7BqdjlMvVce8BkDZ5iMNJyqfyg+8A8FnM9Q4nKZ8B8e+zsVF/p2OUS4tdcwBIvb2fw0nKp8orXzAn5jqnY5RL//iZ/n9SHeWqOXQiIiIibqcKnYiIiLibKnSq0ImIiIi4nSp0IiIi4m46wFMVOhERERG3U4VORERE3E1z6FShExEREXE7DehEREREXE67XEVERMTd9NNfqtCJiIiIuJ0qdCIiIuJuVgdFqEInIiIichoZY/oaY7YaY7YbYx4ooc0gY8xPxpjNxpj3y/ucqtCJiIiIuwXQHDpjTBDwMtAb2A+sNsbMttb+VKDNWcA44CJr7WFjTHR5n1cVOhEREZHTpyOw3Vq701qbBXwADDipza3Ay9bawwDW2kPlfVJV6ERERMTVbGCdWLgusK/A9f3A+Se1ORvAGLMcCAImWGu/LM+TakAnIiIiUkbGmBHAiAI3TbfWTi/YpJi7nbxPuAJwFhAL1AOWGWPOs9Ye+aO5NKATERERd/PjHDrf4G16KU32A/ULXK8HHCymzUprbTawyxizlbwB3uo/mktz6EREREROn9XAWcaYRsaYEOBaYPZJbT4FugMYY6LI2wW7szxPqgqdiIiIuFsAnYfOWptjjLkT+Iq8+XFvWms3G2MmAj9Ya2f7lvUxxvwE5AKjrbXJ5XleDehERERETiNr7Txg3km3PVLg/xa4z3c5LTSgExEREXcLoPPQOUVz6ERERERcThU6ERERcbfAOg+dI1ShExEREXE5VehcxNOwOSE9rgPjIWfjMnJWfVGkTdA57Qm+8HKwFm/ifrLmvu5A0lO7Y+LtnN+jA5kZmTx97zNs27S9SJspMyZTIzqSoKAgNq7axIsPTsUbIN/CHnriOZYuX0VkRHU+nTGtyHJrLVOen8ayFasJDa3I5Afvp9k5ZzqQtHS1urekzcTBmCAPO99fzNapcwotj+rUlNYTb6TauQ1YOXIqB+auciboKQQ1bkFInxvzto11S8he8XnRNud2JKTLFYDFm7CPzM9e9X/QUkR3b0mLSUMgyMPe9xax7aR1UaNTU86bOJiqzRrww8iXiPs8MNdFeNe21Hn0VvB4ODxrAYnTPi60PGr4ACKu6YPNzSU3+Sj7x75A9oFEh9KWLKhZO0KvHgnGQ/Z3X5I1/6NCyyt06kXFK27BHkkCIHvJHLK/+8qJqCWq2b0V500agvG9prZPLXzmjMhOTTlv4hCqNGvAmpEvBuxrSspGAzq3MIaQXjeQ+dFz2NTDhN74ELk71mGT4/KbVI8muOMlHH//SchMh7AqDgYuWcceHajXqC5DOt/EuW2bcveUu7iz/11F2k0aOZn0Y+kAPDr9Ybpd1pVFsxf7OW3xBl7Sm+uvupzxk54pdvmyFavZu/8g82a9wYbNW5j0zFRmvv68n1OegsfQ9olhLL1mCulxKfT6YhIH568h9ZcDJ5qk709i9d2vcfZtlzoY9BSMIaTvEI6//zT2aAqhNz9GzrY12KT883iaiFoEX9ifjHcmwfEA3DY8hpZTbuK7QVPIiEum25ePE3/yujiQxNq7p3Hm7Zc5GPQUPB7qTBzJrsEPkxOfTJPPnuPo19+TuT3/V5AyNu8k+fL7sMczibyhHzEP3MS+fzztYOhiGA+h19xB+ovjsUeSCBv7Ajkbvscbv7dQs5wfl5D5YWB9MTjBY2gx5SZWDnqCjLhkunw5mfj5P3KswGsqw/eaanJ7AG/fZaWDIrTL1S08MY2whw9hf00Cby45W1YR1KR1oTYVWnYle92ivMEcQHqqA0lP7aI+FzL/4wUA/LxmC+FVKxMZHVmk3W+DuaAKQQQHB5N3lHdgaN+6BdWqljwoWPTtSi7v2xNjDK3OO5fU1GMkJqX4MeGpRbZpwrHdCaTtTcRm57Lvs5XUvbhdoTbp+5P49ed9Af1m6anTBG/KIeyRRPDmkvvTSiqc3bZQmwptYsn58eu8wRwE3LYR0eZM0nYlkL73EDY7lwOfriDmpHWRsS+Joz/vC7TfrCwkrNVZZO2JI3tfAjY7h1/nLKVq78I/YZm2ciP2eCYA6Wu3EhxTw4mopfI0PBtv4kFscjzk5pDz4xIqtOrkdKzfJe81FX/iNXXw0xXEXNy+UJuMfUmk/rw3oLdvKbu/5IDOGFPZGDPXGLPeGLPJGHONMWa372zNGGPaG2MW+/4/wRjztjFmvq/NlcaYp40xG40xXxpjgv2SuUoENvXwiev22GFMlYjCbSJq4YmoRcXrHqDi9ePwNGzuj2i/W1RMDRIP5u9iSYxLIqqEN/UnZzzBJ+s+JD0tnaVzl/krYrklJCYTEx114nqt6CgSEpMcTFRUpZhI0g/kn8cyPS6FSjERpdwjMOVtG/n9sEdTimwbnsgYTGQMoUMeInTYIwQ1buHvmKUKrR1BxsH8PmTEpRBau+iXnEBXIaYG2XH5r/Ps+ORSB2yR1/QmdcmP/oj2u3iqR+E9nP8e5T2chKlWtB8V2nQm7MFXCL3lQUxEVJHlTjr5NXU8LpnQ2u7bvsvMev13CVB/yQEd0Bc4aK1tZa09D/jyFO2bAJcCA4AZwCJrbQsgw3e7M06qWBmPBxMRTeasf5I193VCLh4KFSs5FK4UpujvFpdUfHvgxvFc3e5agkOCaXNR6+IbBaDiqommmH47qbg4gVQFLZeTu+EJwhNZi+MzppD531cIuXQ4VAxzJFpxin1tuHFdFLttF9+P6gNjqdTiTJKm/+fPTvWnyNn4PWkPDyN98u3kbl1L6JD7nY5UWLGvKf/HEP/5qw7oNgK9jDFPGWO6WGt/PUX7L3w/oLuRvJ/x+G0AuBFoWNwdjDEjjDE/GGN+mD69tN/wLRubWrgiZ8IjsMeOFGrjTT1M7vZ14M3F/pqETUnAE1Gr3M99OgwY2p/XvnqV1756leSEZGrWqXliWc3aUSQnlPyLJ9mZ2ayYv5ILL77AH1FPi5joKOIP5VcqEg4lER0VWLuW0uNSCKubnymsdiTHE46Uco/AlLdt5PfDVI3EHjt8UpsUcn9Zk79tJMfhiQyMbQMg42AKlerk96FS7UiOxx8u5R6BKScuieDa+ZWq4Jga5CQUnWpQ+aJW1LxjELtvfRyblePPiGXiPZKEJyL/PcoTEYX99aT3qLRUyMkGIPvbLwlqcJY/I57S8ZNeU6G1a7jyNVVmXuu/S4D6Sw7orLW/AO3IG5BNMcY8AuSQ//cIPekumb77eYFsm/+V00sJB5ZYa6dba9tba9uPGDGi3Jm98bsxEbUw1aLAE0SFph3J3bG+UJvc7WsJatA070qlcExELbxHAuPosc/ensPfL76Nv198G8u//I4+f+sNwLltm5KWmkbKocJv+qFhoSfm1XmCPHTs0YG9BSZWB7rYzp2Y/eVCrLWs3/Qz4eGVqRkVWLvQDq/bSXijGMLq18QEB1F/QCcOfhV4u79OxXtwJ57I/G0jqFkncn5ZW6hN7tYf8ZzRLO9KpXBMjZiA2TYAjqzbQeXGMYQ1yFsXdQdeQPx8962L9A3bqNiwDsH1amGCK1Ctf1eOfl34yMnQZo2pO/kO9tw6idzkU32XdoZ3zy94outgatSCoApUaNeNnA0rC7UxVfO/YFdo2QlvfGC9P/32mqrke03VcelrSsruL3mUqzGmDpBirZ1hjDkGDAN2kzfI+wK4yrl0JbBesha+T8Wr7gGPh5yNy7HJBwm+aADe+N3k7liPd/dmbMPmhN40Ebxespd8BMfTnE5exPffrOL8Hh1599u3OH48k3/el3+k6GtfvcrfL76NSmGhTHrzMUIqBuPxeFj73TrmvFv0VBROGf3ok6xeu4EjR47Sc+CN3D58MDk5eZWGa664lK4XdGDZitX0G3QzlUJDmTT+XocTF2Vzvawd/xZdZ47FBHnY9cESjv5ygOajryJl/S7i5q8holVjLnzzXkKqh1G7dxuaj76K+bFjnY5emPWS9dU7hF43BjyGnPVLsUkHCO56Jd64XeRuW0vuzo0ENW5BpRFTfNvSB5BxzOnkJ9hcLxvGv8UFMx/IO8XEzMWkbj1A0zF/48i6ncTPX0P11o3p+Oa9BFevTEzvtjQd/TcWdRvjdPTCcr0cfHQajd55LO+0JR99Tea2vUTfewMZG7eR+vUqao+7CU/lUBq8/AAA2QcT2XPr4w4HP4nXy/FZrxJ25+PgCSJ7xXy8cXsJuWwwuXt+IXfj9wR3H0CFFp3yqr7pqRx/51mnUxdic71sGv8WnWaOwwR52DdzMce27uecMX/jyLpdJMz/kWqtG9PhzfsIrl6ZWr3bcs7oq1ncbbTT0f+QQD5YyF/M/8ycmd/BGHMx8E/yKmzZwG1AJeANIAH4HmhvrY01xkwAjllrn/Hd95i1Ntz3/0LLSmHTn7nlz+iK34SN+jc96/VxOka5LNw/H4DspJ0OJymf4KjGfFT7BqdjlMvVce8BkDZ5iMNJyqfyg+8A8FnM9Q4nKZ8B8e+zsVF/p2OUS4tdeeftS729n8NJyqfKK18wJ+Y6p2OUS//4mQB+nTR8bNxVfhvMhE/5JLAmRPv8JSt01tqvgOLOAHl2MW0nnHQ9vKRlIiIi4oAAntvmL3/JOXQiIiIi/0v+khU6ERER+R+iCp0qdCIiIiJupwqdiIiIuFsA/4KDv6hCJyIiIuJyqtCJiIiIu2kOnSp0IiIiIm6nAZ2IiIiIy2mXq4iIiLia1S5XVehERERE3E4VOhEREXE3VehUoRMRERFxO1XoRERExN28OrGwKnQiIiIiLqcKnYiIiLib5tCpQiciIiLidqrQiYiIiLupQqcKnYiIiIjbqUInIiIirmatKnSq0ImIiIi4nCp0IiIi4m6aQ6cKnYiIiIjbqUInIiIi7qYKnSp0IiIiIm5ndGSIX+iPLCIifyXGn092dHhvv33OVn1jgV/7Vlba5SoiIiKuZrXLVQM6f3m93o1ORyiXW/fPYMoZ7u7DuD0zAPio9g0OJymfq+PeIztpp9MxyiU4qjEA/er3czhJ+Xyx7wsAxjW83uEk5TNl9/uMaXid0zHK5endMwF4oYG736fu3juDqfXd3Yc7981wOsJfkgZ0IiIi4m6q0OmgCBERERG3U4VORERE3M3rdADnqUInIiIi4nKq0ImIiIir6ShXVehEREREXE8VOhEREXE3VehUoRMRERFxO1XoRERExN10lKsqdCIiIiJupwqdiIiIuJqOclWFTkRERMT1VKETERERd9McOlXoRERERNxOAzoRERERl9MuVxEREXE1HRShCp2IiIiI66lCJyIiIu6mgyJUoRMRERFxO1XoRERExNWsKnSq0ImIiIi4nSp0IiIi4m6q0KlCJyIiIuJ2qtC5SL3Yllzw2GBMkIetMxez/uU5hZZ7QioQ+/xIolo2IvNwKgtvm8qx/UkOpS1d7wmDadK9NdkZmXw+ajoJm3YXadN19NW0uLIzodUq82yzW/wf8hRqdW9Jm4l562Pn+4vZOrXw+ojq1JTWE2+k2rkNWDlyKgfmrnImaCkeeuI5li5fRWREdT6dMa3IcmstU56fxrIVqwkNrcjkB++n2TlnOpD01EY+NpIOPTqQmZHJs/c9y45NO4q0mfTuJCKjIwkKCmLTqk288tAreL2B89W+/6NDOKd7a7Iysvh41DQObt5dpE2fUYNoc2UXKlWrzITmN/s/ZBlc/uhQmnZvTXZGFh+OepUDxfTj4lGDaHdlVypVq8zDzW/yf8gy6PbYYBp2b01ORibz759OYjHvU9EtGtL72b9TITSE3YvWseTRd/0ftBRdHhvMGT3y+rDwvuL7ULNFQ3o993eCQkPY8806lgVYH8pCc+hUoXMN4zFc9PhQvhz8NB93H0OTAZ2ofladQm3OuTaWrF/T+LDz/Wx8/Us6jr/WobSla9K9FRGNYpjW7X6+GPcGfR8fVmy77V+v4a0Bj/o3XFl5DG2fGMayG57my25jaDDwAqqcXbdQk/T9Say++zX2/vc7h0Ke2sBLejPtucdLXL5sxWr27j/IvFlvMGHMXUx6Zqof05Vdh+4dqNOoDsO7DOfFsS9y5xN3Fttuym1TuOPiOxjZayTValSjy2Vd/Jy0ZOfEtqZGoxieib2P/47/NwMnFz9Y+3nhGl4Z8LCf05Vd09jWRDWK4enYe/lk/OtcMXl4se1+XriGlwY85Od0ZdeweyuqN4zh7a73s/CBN+gxeVix7bpPvomFD7zB213vp3rDGM6IbenfoKU4o3srqjeKYUaX+1k09g26PTGs2HaxT9zEorFvMKPL/VRvFEODAOqDlJ0GdC5Rs3UTju5OIHVvIt7sXHZ8tpIz+rQr1KZhn7b88tEyAHbNXUXdzs2diHpKZ/Vux6ZPvgXg4NodVKxamcrR1Yu0O7h2B2mHjvg7XplEtmnCsd0JpO1NxGbnsu+zldS9uPD6SN+fxK8/74MAPoN5+9YtqFa1SonLF327ksv79sQYQ6vzziU19RiJSSl+TFg2nfp0YuEnCwHYsnYL4VXDiYiOKNIu/Vg6AEEVgggODsbawFk35/Zpx9r/5G2/+9ZuJ7RKGFVqFt0u9q3dTmpiYG4XAM36tGONrx97126nUgn92Bvg/Wjcpx0/+96n4n3vU2EnvU+FRVcnJLwS8Wu2A/DzJ9/S5OL2fs9akkZ92rHF14eEMvZhyyff0jiA+lBmXj9eApQGdIAx5lNjzI/GmM3GmBHGmNuMMU8XWD7MGPOS7/8PG2O2GGMWGGNmGmNG+SNj5doRHIvL/yBNi0+hcu3CH1hhMRGk+drYXC9ZR9OpGBHuj3i/S5WYCI4eTD5xPTU+hSq1in74BrJKMZGkH8jvQ3pcCpVi3NWHskhITCYmOurE9VrRUSQkBt5u/BoxNUg6mJ8rKS6JqJioYts+PuNxZq6dSXpaOt/O/dZfEU+pWq0IjhzM38Z/jU+hqgtfU9VqRXKkwPZ9JD6FajGRDib6Y8JjIjgWl9+PY/EphJ+0PsJjIjgWn1JqGyeFx0RwrMC6OBZXQh8KfLYU10bcQQO6PDdba9sB7YG7gP8AVxZYfg0wyxjTHrgKaONb7sevMaboTScVF4w5dZuAUGzOQAxasuK74K4+lEVxfSr2deaw4jKVtD4euvEhbmh/A8EhwbS6qNWfHa3siu2DAznK63esi8B26vcpU0ybgOprWdbF/8j6sl7/XQKVDorIc5cx5grf/+sDjYCdxphOwDbgHGA5cDfwmbU2A8AYM6e4B/MtGwGMAHjttdeKe2v4XdLiUgivnf8tt3JMJGnxh4u0qVw7krS4FEyQh5CqYWQeOVbOZz492g7pRetruwMQt2EnVevUOLGsSkwkqQG6a7Uk6XEphNXN70NY7UiOJ7irD2UREx1F/KH8ylfCoSSio2qUcg//uWzoZfS9ri8Av6z/hag6+RW5qNpRJCckl3RXsjOz+X7B93Tq04m1y9b+6VlL0mlwbzpcl7dd7F+/k+p1ItnjW1YtJpLUhMMl3zmAXDC4N+df1wOAfet3Ur3A9l09JpKjLulHyyG9OM+3PhI27CS8dn4/wmMiOXbSNp4an0J4gepjeEwkaQ6/D7QY2otmvj4cWr+T8ALrIrx20XzHTvpsKa6NuMNffkBnjIkFegEXWGvTjTGLgVBgFjAI2AL811prze8oTVhrpwPTf7v6+sSl5cqZuH4nVRvFUKV+TdLiU2gyoBOL7nylUJs9C9Zw9tVdOLRmO40u7cjB5T+V6zlPpzXvfM2ad74GoEmP1rQb2pufZq+gTpsmZKamB+xcuZIcXreT8EYxhNWvSUZ8CvUHdOL72192OtZpF9u5EzM/mUO/Xt3YsHkL4eGVqRkVGLvPPn/7cz5/+3MAOvToQP9h/Vny2RKatmlKWmoahw8VHkSEhoVSKbwShw8dxhPkoX2P9mz+frMT0U9Y+e4CVr67AIBzurfmgqF9WD97BfXbnMnx1IyAnmNW0Ip3F7DC14+m3dtw4dA+rJv9HQ3anElGarpr+rHhna/Z4HufatijNa2G9uaX2SuI8b1PpZ/0PpV+6AjZaceJadOE+LU7OPeqzqx/a74T0U/Y+PbXbHw7rw9n9GhNy2G92fbZCmq1aUJWCX3ISjtOrTZNSFi7g6ZXdWbD/znbhz8ikCtn/vKXH9AB1YDDvsFcU6CT7/b/AA8Ce4Cxvtu+BV4zxkwh7293KfC6P0LaXC/fPfw2/d4bg/F42DprCYd/OUC7UVeRuH4XexesYesHS4h9YSSDvn2WzCPH+Ob2wDwiccc362jSvRUjlz5LdkYWc0dNP7Hs5nmTefOSBwHoPu5amg24kOBKIdyx8kXWf7CYb5//j1OxC7G5XtaOf4uuM8digjzs+mAJR385QPPRV5Gyfhdx89cQ0aoxF755LyHVw6jduw3NR1/F/Nixp35wPxr96JOsXruBI0eO0nPgjdw+fDA5OTkAXHPFpXS9oAPLVqym36CbqRQayqTx9zqcuHirv1lNhx4dePPbNzmecZx/3f+vE8umfjmVO/veSWhYKBPenEBwSDAej4f1361n7oy5DqYubOuidZzTvTWjlvyL7IxMPh792oll/5j3BC9dMh6Avg9cR2vfdvHAipdYPWsxC5//xKnYRWxZtJam3VszdsnzZGVk8lGBftwzbwrPXzIOgEseuP5EP8avmMrqWYtYEED92P3NOhp2b8XQZc+Sk5HFggLvU9d/MZn3++W9T33z4P/R+9kRVAgNYc+i9exetN6pyEXs+WYdZ/RoxeBv8/qw8P78Plzz5WRm9c3rw5Lx/0fP5/L7sCeA+iBlZ9y4r/x0MsZUBD4F6gJbgZrABGvtYmPM50Aza23jAu0nANeRN9BLBBZba081qLOv17vxz4jvN7fun8GUM9zdh3F7ZgDwUe0bHE5SPlfHvUd20k6nY5RLcFTeJtWvfj+Hk5TPF/u+AGBcw+sdTlI+U3a/z5iG1zkdo1ye3j0TgBcauPt96u69M5ha3919uHPfDCh2EuKf51DPbn4bzEQvXBJ4E4lRhQ5rbSZQ7KeKtfayYm5+xlo7wRgTBiwFnv0z84mIiEjptMtVA7o/Yroxphl58+zettaucTqQiIiI/LVpQPc7WWvdvW9FRETkf40NyL2gfqXz0ImIiIi4nCp0IiIi4mqaQ6cKnYiIiIjrqUInIiIirma9mkOnCp2IiIiIy6lCJyIiIq6mOXSq0ImIiIi4nip0IiIi4mpW56FThU5ERETE7VShExEREVfTHDpV6ERERERcTxU6ERERcTWdh04VOhERERHX04BORERExOW0y1VERERczVqnEzhPFToRERERl1OFTkRERFxNB0WoQiciIiJyWhlj+hpjthpjthtjHihm+UhjzEZjzDpjzLfGmGblfU5V6ERERMTVAqlCZ4wJAl4GegP7gdXGmNnW2p8KNHvfWjvN1/5y4Dmgb3meVxU6ERERkdOnI7DdWrvTWpsFfAAMKNjAWnu0wNXKQLkP61CFTkRERFwtwI5yrQvsK3B9P3D+yY2MMXcA9wEhQI/yPqkqdCIiIiJlZIwZYYz5ocBlxMlNirlbkSGntfZla20TYCzwULlz2QAb1v6P0h9ZRET+Svw6qW1niz5++5xtvHF+qX0zxlwATLDWXuy7Pg7AWjulhPYe4LC1tlp5cqlCJyIiInL6rAbOMsY0MsaEANcCsws2MMacVeDqpcC28j6p5tD5SdqUoU5HKJfK496mSVRbp2OUy46kNQCkTR7icJLyqfzgO/Sr38/pGOXyxb4vAMhO2ulwkvIJjmoMwKQzbnA4Sfk8vOc97mp4jdMxyuXF3bMASL60m8NJyqfG3CV8GnO90zHKZWD8+35/TmsD5yhXa22OMeZO4CsgCHjTWrvZGDMR+MFaOxu40xjTC8gGDgPlHiRoQCciIiJyGllr5wHzTrrtkQL/v/t0P6cGdCIiIuJq1ut0AudpDp2IiIiIy6lCJyIiIq7mDaA5dE5RhU5ERETE5TSgExEREXE57XIVERERVwuk05Y4RRU6EREREZdThU5ERERczXpVoVOFTkRERMTlVKETERERV7PW6QTOU4VORERExOVUoRMRERFX0xw6VehEREREXE8VOhEREXE1/fSXKnQiIiIirqcKnYiIiLiafilCFToRERER11OFTkRERFxN56FThU5ERETE9VShExEREVfTUa6q0ImIiIi4ngZ0IiIiIi6nXa4uEtS4BSG9bgCPh5x1S8heObdom6YdCekyECx4D+0lc/Y0B5Ke2iNPjCa2V2cyMo4z5h+PsnnDlhLbvjbjXzQ4oy79ugzyY8JTC2rcgpA+N4LxrY8Vnxdtc25HQrpcAVi8CfvI/OxV/wc9hZGPjaRDjw5kZmTy7H3PsmPTjiJtJr07icjoSIKCgti0ahOvPPQKXq/XgbRFPfTEcyxdvorIiOp8OqPo691ay5Tnp7FsxWpCQysy+cH7aXbOmQ4kPbWLJwzhzO6tyM7IYvao14jftLtIm+6jr6bFlV2oVK0yTzUb7v+QZXDVo8No1r0NWRmZvDfqVfZv3lWkzaWjrqHjlV0JqxbO6OZDHUhZuuB2Hak84h/g8XB8/lyOf/R+se1CLupGlfETOXL3CHK3b/VzytJFd29Ji0lDMEEe9lHWwCEAACAASURBVLy3iG1T5xRaXqNTU1pMHEzVZg34YeRLHPx8lUNJy0+nLfl/9u47Pqoq/eP450wIJCG0NAZQOor0pmABEhEULIC6UlREWLGLhaJYV1TUtbDqKujqb1dFZFddG6ggIKBERAkgKL2XhBQjJYGUOb8/MpA2CQmBmbnu9+1rXuaec+bOc+bODSfPOfeOMnTOYQzV+43g8L+fJ/v1Bwhp0wMT3bB4k3r1CT33MrLfeYLsf0ziyNczAhRs+eIvOp+mzRtz4TkDefDeJ3j8rw+U2bbfpReSdSjLj9FVkDFUv2QEh99/juzp9xPStgcmxsfxOO9yst+eTPbrkzgy790ABVu2sxPOpmGzhozuOZqXJr7EHU/d4bPdlFuncPvFt3PLRbdQJ7oOPS/r6edIyzZoQF+mvfBEmfVLEpezY9ce5sx6k8cm3MXk517xY3QV1zKhI1HN3Py9933MfuBNBjxxo892G75O4q2Bj/g5uoprE9+J2GZuJsePZdakN7jmSd+DzrXzV/D8wAf9HF0FuVzUvPVu9j86gcxbb6BGrz6EnN6kdLvwcMKuuIrcdWv9H+PxuAwdp9xI4vBnmd9rPKcNPo9aZzQq1iR7dxorxk5j13+XBihIOZkqPaAzxuQbY1YaY9YYY/5jjIkoUjfYGGONMa2922HGmHXGmPZF2kwwxsz37mOlMSbDGLPV+/PXZbSfZoxpaozJ9rb7xVvmKlF+9DHC+9xIY8x0Y8xmY8xaY8xiY0z3Ev1Ya4xZZYy51xhT5vthjIk3xvxujEkyxqz37uuyyr5/J8rVsDme31KwmangySf/12VUO6NLsTbVOvUmb8V8OOwdAGUd8Fd4lXJR/3j++++CbNbKn36mdp1axNaPKdUuomY4o2+9lr8//w9/h3hcroYt8GTsKzwev3xf+nh0jifvp6+D+nj06NeD+R/OB2Bd0joia0dSL65eqXZZBwv6EFIthNDQUGwQ3SOgW6f21Kldq8z6hd9+zxWX9MEYQ8d2Z3HgwEFS0zL8GGHFnNG3K6s/XALA7qRNhNWOIDKubql2u5M2cXBfpr/Dq7D2/c7mh48WA7AtaSPhtWpSO7Z0P7YlbWR/anD2o9oZZ5G/Zzee5L2Ql8eRxQsI7XFBqXYR140m+4OZkJMTgCjLV69zSw5uTSFrxz5sbj67Pk7EfXHXYm2ydqax/9edECTZ9qqw1n+PYHUiGbpsa20na207IAe4pUjdMOBbYCiAtfYwcDfwqinQCLgZuNq7j07Ap8B47/ZFZbQ/msLZ7H1OB6ANMKhoeZHH297yfwAZQCtrbVtgJHB05HC0H22BvsAA4NHj9H2JtbaztfZM4C7gFWNMn0q8dyfMRNbD7i/8R8geyMDUKv4PryvKjYmqT9j1DxE24mFCmrcvuZugUL9BHHt2pxzbTt6zD3eD2FLt7nngNt589V2ysw/7M7wKMbXqYQ+kH9u2+8s6Hm7CRjxE2MhHgvJ4RLujSduTdmw7bW8aMe7Sg2uAJ959gplJM8k6lMW3s7/1V4hVlpKajjuusE/142JISU0r5xmBUcsdxf49hZ+p/ckZ1KpfenAd7OrUr0dmkX5kJqdTxx0VwIgqzxUdgydt37FtT1oqIdHFz4uQ5q1wxcaRuzzR3+FVSHiDemQXOQ6H92YQ3sBZx0Eqp6pTrkuAllCQDQPOB0bjHdABWGu/BPYCI4AXgcestb+VtcOKtLfW5gFLj762L8aYFkB34CFrrcf7vC3W2lILz6y1+4AxwB3GmApNxFtrVwKPA77nqE42X2GV/FPBFYKrnpvDM6Zw5JPXqN5/FNSIKP28AKtIV85qdwZNmp3O3DkL/RPUyVDyLzdXCK6o+hx+dwpH/vsq1S8dHXTHw9fHvazs20PXPcS13a4ltHooHc/veKpDO2l89aeCp7lf+T4vgjgdUAaf763T+uHrvChRX/Om28n6x6t+C6nS/gjHoRI81vjtEaxO+KIIY0w1oD/wpbdoEPCltXaDdxq1i7V2hbfubuAHYKO19p0K7L7c9t5p3j7A0YUkLYwxK4s0uROoB6y01uZXpD/W2i3eKdc4IOV47b1WAON9VRhjxlAwSGT69OlcW8EdlhnfgQxM7cK/rkytKOzB4tMVdn8Gnj2bwZOP/T0Nm7EXV1R9PHtLL0j2t+tGXcOQ6wcD8PPKtTRsVJ+fvHXuhnGkJKcWa9/57A6063gWi1Z8Tki1EKJjopjxyetcO3CMnyP3zR74DVMr+ti2qR2FPfhbiTYZeHZvKjwe6cFxPC674TIuGXYJABtWbSCmYWHmIaZBDOkp6WU9ldwjuSybt4we/XqQtCTplMd6MrjjYkjeV5iRS9mXRlxMdDnP8J9uI/rSeWgCAHtWb6F2w8K4arujgnpqtaie1/fj3GEFkxU7Vm2mbpF+1HVH83tKmX/DByVPWiqumLhj266YWDzphZ8hEx5BSJNm1H56akF9vShqP/IU+x+fFDQXRmTvySC8yHEIaxBFdrKzjoNUzolk6MK9g6cfgR3Am97yYcD73p/f924DYK3dAywAKnSJXzntjw7cvgNmW2u/8JaXnHJdcgL9Aqjs0LvM9tba16213ay13caMqfogxLNnK6569TF1YsAVQshZ3cnbWPwf1PyNK3A1OatgIzwSE+XGk7nPx9787923/s3lCcO4PGEYc+d8w+BrCpYfdurangP7D5KaUnwK7L3/+4Dz2l1M7y6XMeTSUWzbvD1oBnMAnj1bcEUVOR5tepC3ocTxWP8TriZtCjbCIzHRbjyZqT725l+f/+tz7rjkDu645A4Sv0qkz1UF/xC37tyaQwcO8du+4r/0wyLCjq2rc4W46HZhN3Zt2uX3uE9U/AU9+PTL+VhrWbXmVyIjaxIbExxTTz++PY83BkzijQGTWD/3RzpcVXCxSaPOLTl8INsxA7ol78zl2QETeXbARFbPXc45V/YCoGnnVhw+kBW0a+XKkrdhHSGNTsNV3w3VqlGj14XkLvvuWL3NOsRvwweSOWoomaOGkrful6AazAFkrtxMZHM3EY1jMaEhnDboXJLn/nT8JzqUtcZvj2B1Ihm6bO86tmOMMdHAhUA7Y4wFQgBrjJlgC+cMPN5HRflqv7nka5djLdDRGOM6OuVaHmNMcyAfqMwIqDPwayXanzjrIWfeO4QNHV9wm4zVi7FpuwntORjP3m3kb0oif8vPhDRrR/hNT4HHQ86CWZB9yC/hVcY3874l/qILWLD8Ew5nH2biXY8dq/ts4UwuTxhW9pODhfWQ89XbhA2bAC5D3irv8eh1JZ69W8nf6D0ezdsTPmZKQfv570P2wUBHXszyBcs5+8KzeevbtzicfZgX73vxWN0rX77CHZfcQVhEGI+99Rih1UNxuVysWrqK2e+WvmVOoIx/9GmWJ60mM3M/fQZdx22jrycvLw+AIYMvpde5Z7MkcTn9rxlFeFgYkyfdE+CIfdu0YCUtEzpx++IXyPPetuSom+Y8xRsDJgHQ54FhtBt4HqHh1Rn7/cskvb+QxVM/ClTYpfyyMIm2CZ15ZNHfyMnOYcb4wr/LJ8x5hmcHTATgivuvpdvA8wkNr87jia+SOGsBX0z9IFBhF+fJ59BrU6k9+TlwuTgybw75O7YRft0o8jauI3dZ8F8VavM9rJ70T86beX/BbUtmfsOB9btpPeFqMlduIXnuCup2ak73t+4htG5N3H270Hr81SzoPSHQocsJMpVdo2GMOWitjSxRdjPQxVp7c5GyRRSsX1vi3f4n8Lm19oMSz61QuTGmqXe7XYl2Psu9df8G1gOPWGutMaYV0MZa+0nRfhhjYoEZQKK11ueFEcaYeGCctfYy73YH4BPgz9ba+T7frEL20JTgu89SZdR84F+0iOly/IZBbHNawQqAQ0+OCHAkVVPzwbfpf3r/QIdRJV/sLEiu56ZtCXAkVRMa0xyAyU2quqgisB7ePoO7mg4JdBhV8tK2WQCkX9o7wJFUTfTsRXzsHh7oMKpkUPJ7UPkZrypZ1vBKvy0Q7L7no6BM052sGwsPA54uUfYhMJyCCydOtZJr6N6y1r4E/Bl4HthkjMkC0ilc83Z06jgUyAPeAV44zuv0NMYkAREUZPLuqsBgTkREROSUqvSArmR2zlsW76PspRLbI8vYX4XKrbXbgFJZOG95eBn72A/cVEZdiK/yslhrvwHqVOY5IiIicur9ca/frTh9U4SIiIiIw+m7XEswxlwMPFOieKu1dnAg4hEREZHyBfP94fxFA7oSrLVfAV8FOg4RERGRitKATkRERBwtmO8P5y9aQyciIiLicMrQiYiIiKNV5lsL/qiUoRMRERFxOA3oRERERBxOU64iIiLiaNa/3zQWlJShExEREXE4ZehERETE0Tz67i9l6EREREScThk6ERERcTSP1tApQyciIiLidMrQiYiIiKPpKldl6EREREQcTxk6ERERcTR99ZcydCIiIiKOpwydiIiIOJrW0ClDJyIiIuJ4ytCJiIiIo2kNnTJ0IiIiIo6nDJ2IiIg4mjJ0ytCJiIiIOJ6x1gY6hv8FepNFROR/iV8vO51Tf6jf/p0dkPJ+UF5SqylXP/mm/p8CHUKVxKf8hzuaDgl0GFXyyrZZAHziHh7gSKpmYPJ7PNDU2X2Ysu09ACY3uTbAkVTNw9tnAJCbtiXAkVRNaExzUvv2DnQYVRI7bxHwxzi/ZzZ09nkxbM8Mv7+mbluiKVcRERERx1OGTkRERBzNowSdMnQiIiIiTqcMnYiIiDiaR2volKETERERcTpl6ERERMTRdG8wZehEREREHE8ZOhEREXE0ffWXMnQiIiIijqcMnYiIiDiax+gqV2XoRERERBxOGToRERFxNF3lqgydiIiIiOMpQyciIiKOpqtclaETERERcTwN6EREREQcTlOuIiIi4mge3bVEGToRERERp1OGTkRERBzNg1J0ytCJiIiIOJwydCIiIuJourGwMnQiIiIijqcMnYiIiDiarnJVhk5ERETE8ZShc5CohE60fOJGTIiLvTPms+Plj4vVn3bzZTS4tg82P5/c9P2su/tVjuxKC1C05bv60ZG0TehMTvYR3hn3GrvWbi1WHxpWndGv3kNMk/rYfA8/z/+JT5+ZGaBofYtL6ED7ySMgxMWOGQvZ+Mpnxeqje7Sm3ePXU7tNY3685WX2fv5DgCIt3+WPjuDMhE7kZOfwwbhp7Fm7rVSbfuOuofOVPQmvU5PH2o7yf5AVcPFjI2iZ0JHc7Bw+HTed5DXbSrVJGP8n2nv78Uyb0f4PshwPPfUCi7/7gah6dfn43Wml6q21TJk6jSWJywkLq8GTD95HmzNbBiDS8oV2O4fI2+7EuFxkfzGb7Fnv+WxXvWdv6jzyOL/dPoa8Dev9HOXx/RHO7wbxHegy+XqMy8Xmmd/wa4k+xHZvTZfHr6PuWY1Zeusr7JwdfH2oKH31lzJ0zuFy0erp0awe/iQ/9LyHuMHnE3HGacWaHFyzlZ8unsiPCeNI/ex7WjxyfYCCLV+b+E7ENnPzl/ixzJz0BkOf9P0P6/w3PueJPvfy9KUTad71TNrEd/JzpOVwGTpMuZHE4c+yoNd4Gg0+j1pnNCrWJGt3Gkljp7H7v0sDFOTxnRnfiehmbp6Lv5f/TvoHg570PVj7df4KXh34sJ+jq7iWCR2Jaubm773vY/YDbzLgiRt9ttvwdRJvDXzEz9FVzKABfZn2whNl1i9JXM6OXXuYM+tNHptwF5Ofe8WP0VWQy0WtO+/m90kTyPjzDYQl9CGkcZNSzUx4OOGDriL317UBCLIC/gDnt3EZuj41km+ufZY58RNoMvBcarcq3Ydld09ne5D2QSrnhAd0xph8Y8xKY8waY8x/jDERReoGG2OsMaa1dzvMGLPOGNO+SJsJxpj53n2sNMZkGGO2en/+uoz204wxTY0x2d52v3jLXCXKjz5GeJ8baYyZbozZbIxZa4xZbIzpXqIfa40xq4wx9xpjynxfjDHxxpjfS7zORSf6PlZU7S4tyd6azOHt+7C5eez7+DtiLulWrE3md2vxZOcAsP+nDdRoEHWqwzohHfqdzQ8fLQZgW9JGwmvVpHZs3WJtcg/nsDGx4Jd9fm4+O9dupa47ePpTr3NLDm1NIWvHPmxuPrs/TsR9cddibbJ3prH/151YT/D+7XhWv64kfbQEgJ1JmwirFUGtEsfiaN2B1Ex/h1dhZ/TtyuoPC/qxO2kTYbUjiIwr3Y/dSZs4uC84+9GtU3vq1K5VZv3Cb7/nikv6YIyhY7uzOHDgIKlpGX6M8PiqnXkW+Xt240neC3l5HP5mAdXPu6BUu4iRo8n+90xsTk4Aojy+P8L5HdW5BQe3pXBoRyqe3Hx2fPI9p5Xow6FdaWT+uhPrcf41otaPj2BVlQxdtrW2k7W2HZAD3FKkbhjwLTAUwFp7GLgbeNUUaATcDFzt3Ucn4FNgvHf7ojLaP+Dd/2bvczoAbYBBRcuLPN72lv8DyABaWWvbAiOBmBL9aAv0BQYAjx6n70tKvM7XlXrnTkANdxRH9qQf2z6yJ4Ma7ugy2zcY3oeMBUmnOqwTUrd+PX4r0pfM5PRyB2vhtSNo36cr679b44/wKiSsQT2yi/Qhe28GYUE6gC5Pnfr1yNxTOCj4PTmD2u56AYzoxNRyR7G/yPHYn5xBrfrO60d5UlLTccfFHNuuHxdDSmpwLalwxcSQn7rv2LYnLZWQmJhibaq1aEVIbBw5yxL9HV6F/RHO7wh3FFlF+pC1N4PwBn+sc0KKO1lTrkuAllCQDQPOB0bjHdABWGu/BPYCI4AXgcestb+VtcOKtLfW5gFLj762L8aYFkB34CFrrcf7vC3W2tk+XnMfMAa4wxgTXNfM+IjGlvG3Qv2relKrU3N2/P3TUxzUCfLx1lrruy+uEBcjX7qLb/75Jek79/lsEwg+Px5l9CGo+TwWAYijinwfDgd2pBy++hNsv6Z8H4ji9TVvvZ2D01/1W0gn4g9xfvv6aDitD5XgMf57BKsqXxRhjKkG9Ae+9BYNAr601m7wTqN2sdau8NbdDfwAbLTWvlOB3Zfb3jvN2wc4uiimhTFmZZEmdwL1gJXW2vyK9Mdau8U75RoHpJTRrGeJ17nKWru5RGxjKBgcMn36dM6oyIuX48jeDGo0LMzI1WgYRU5y6emWer3a0/juK1k5+FFsTl4VX/Xk6XV9P84b1geA7as2U69IX+q6o/k9xffYftiUMaRuTeabt+b4Jc6Kyt6TQXiRPoQ3iOJwcpl/nwSVHtf35exhCQDsWrWFug2j2O6tq+OO4kAZxyLYdBvRl85DC/qxZ/UWahc5HrXdUUE7tXqi3HExJO8rzMil7EsjLqbsLH0geFJTCYmNO7btioklP70wZhMeQbWmzaj73NSC+qgoaj/+FPsfmRRUF0Y4+fw+KmtvBhFF+hDRIIrs5D/WOSHFVWVAF15kULMEeNP78zBgqvfn973bKwCstXuMMQuAzyvyAuW0Pzpws8An1tovjDFNKZyKPcYYc0WleuV92nHql1hrLyuvgbX2deD1o5vfPDzvBMIodCBpE+HNGxDWOI4jezOIG3Q+v9z6t2JtIts15Yy/jmH10CfJTdtfpdc72Ra/M5fF78wFoG1CZ3rdcDE/fbqUpp1bkX0gi/0+1mdddt8QwmtF8N7E6f4O97gyV26mZnM3EY1jyd6bQaNB5/LTbUG4SN2H79+Zx/fvFHwez0zoxLk39GPVp4mc3rklhw9kB/VauaJ+fHseP75d0I+WF3bi7Bv6sfbTRBp5+/FHG9DFX9CDmR9+Rv+LerN67ToiI2sSGxNc04B569cR0ug0XG43nrQ0wuIvZP+UycfqbdYh0q8eeGy7znNTOfT6a0E1mANnn99HZazcQq1mbmqeHkt2cgaNB/Zg6e1/D3RYp0xwrmT0r6oM6LJ9DJ6igQuBdsYYC4QA1hgzwRbOF3io3Hvvq32pgVs51gIdjTGuo1Ou5THGNAfygeCZ3wNsvoeND7xJh/cfLLhtycyFZK3fRdMJQziwajPpX/1Ii0evJ6RmGG3/cR8Ah3ensWbEMwGOvLS1C5Nom9CZRxf9jdzsHN4d/9qxuvvnPMPTAyZS1x3FJXdeSfKm3Uyc/TQAi/71FYmzFgQq7GJsvofVk/7JuTPvx4S42DHzGw6s303rCVeTuXILyXNXULdTc8556x5C69bE3bcLrcdfzcLeEwIdejHrF67kzIROjFv0IrnZR/hgfOHg+c45T/HygEkAXHL/MDoNPI/Q8Orcn/gyy2d9w/ypHwYq7FI2LVhJy4RO3L74BfK8ty056qY5T/GGtx99HhhGO28/xn7/MknvL2Tx1I8CFXYx4x99muVJq8nM3E+fQddx2+jrycsryLIPGXwpvc49myWJy+l/zSjCw8KYPOmeAEfsgyefg69Mpc6U5zAuF4e/mkP+9m1E3DCKvA3ryEl0xtWUf4Tz2+Z7+PHBfxL/3kRMiIst7y9i/4bdtB9/FRmrtrJ77gqiOjan55v3UL1uBI36dqb9uKuYkzAx0KHLCTInus7EGHPQWhtZouxmoIu19uYiZYsoWL+2xLv9T+Bza+0HJZ5boXJvJu5z78UYRdv5LPfW/RtYDzxirbXGmFZAG2vtJ0X7YYyJBWYAidZanxdGGGPigXHHy9CVYL+p/6dKNA8+8Sn/4Y6mQwIdRpW8sm0WAJ+4hwc4kqoZmPweDzR1dh+mbCu4N9nkJtcGOJKqeXj7DABy07YEOJKqCY1pTmrf3oEOo0pi5y0C/hjn98yGzj4vhu2ZAcef6Tqp3jjtOr8tELxp17tBuZLuZN+Hbhjw3xJlHwL+OsNalLidyF3e8j8DbmCTMeZn4A1gj7cu/OhtS4CvgbnAX47zOj1LvM7Vp6IzIiIicnwePz6C1QlPuZbMznnL4n2UvVRie2QZ+6tQubV2G1AqC+ctDy9jH/uBm8qoC/FVXhZr7TdAnco8R0RERORU0jdFiIiIiKNZ479HRRhjLjHGrDfGbDLG3O+jvoYxZpa3fpl32ViVaEBXBmPMxSWmVVcaY0pOJ4uIiIgcY4wJAf5OwS3d2gDDjDFtSjQbDfxmrW1Jwb12q3wFY5XvQ/dHZa39Cvgq0HGIiIhI+YJsbds5wCZr7RYAY8z7wEDglyJtBgKPeX/+AHjFGGPsiV6pijJ0IiIiIidTI2Bnke1d3jKfbbzfevU7UKU7hStDJyIiIo7mzwxd0W+C8nrd+2UCx5r4eFrJzFtF2lSKBnQiIiIiFVTim6B82QWcXmT7NApvlVayzS7vV6jWAUp/n2claMpVREREHM368VEBy4FWxphmxpjqwFDg0xJtPgVu8P58NbCgKuvnQBk6ERERkZPGWptnjLmDggsrQ4C3rLVrjTGPAz9aaz8F3gTeMcZsoiAzN7Sqr6sBnYiIiDiaJ8i+jMtaOweYU6LskSI/HwZO6neCaspVRERExOGUoRMRERFHC7L70AWEMnQiIiIiDqcMnYiIiDiaMnTK0ImIiIg4ngZ0IiIiIg6nKVcRERFxtCrdkfcPQhk6EREREYdThk5EREQcLdhuLBwIytCJiIiIOJwydCIiIuJoum2JMnQiIiIijqcMnYiIiDiarnJVhk5ERETE8Yy1Gtf6gd5kERH5X+LX606fbHKt3/6dfXD7jKC8plYZOhERERGH0xo6P9nRrU+gQ6iSxj/OZ0iTQYEOo0pmbf8YgJ+bXR7gSKqm/dbPmNB0WKDDqJJnt80E4K6mQwIcSdW8tG0WAKl9ewc4kqqJnbeI3LQtgQ6jSkJjmgOwrOGVAY6karrv+Yi59YcGOowq6Zfyvt9fU1e5KkMnIiIi4njK0ImIiIijaaG6MnQiIiIijqcMnYiIiDia1tApQyciIiLieBrQiYiIiDicplxFRETE0TxBeatf/1KGTkRERMThlKETERERR/PoxiXK0ImIiIg4nTJ0IiIi4mjKzylDJyIiIuJ4ytCJiIiIo+nGwsrQiYiIiDieMnQiIiLiaLrKVRk6EREREcdThk5EREQcTfk5ZehEREREHE8ZOhEREXE0XeWqDJ2IiIiI4ylDJyIiIo6mq1yVoRMRERFxPA3oRERERBxOU64OEnbu2dQbdzu4XBz6eA77//V+sfrIqy4j8k8DId+DJzubjCdfJG/r9gBFW76Rj/2ZzgldOZJ9hNfGvcTWNVuK1VcPq849r02gfmM3Ho+Hn75ezsxn3glQtL5F9upCw0dvApeL32bNI3XaB8XqY0YPpN6Qftj8fPLT97Nr4t/I3Z0aoGjLdsWjN9A6oRO52Tn8e9xr7F67rVSbi8ddQ9crexFepyYPt73R/0FWwFWPjqRNQmdyso8wY9xr7Fq7tVSbS8cN4ZwrexFRJ5LxbW8IQJRlC+12DpG33Ylxucj+YjbZs97z2a56z97UeeRxfrt9DHkb1vs5yvI99NQLLP7uB6Lq1eXjd6eVqrfWMmXqNJYkLicsrAZPPngfbc5sGYBIj69OfGeaTB6FcbnYN/Nr9r7y32L17jGXEzf8ImxePrnp+9ly79/JCbLzOzqhI62fuAET4mLXjAVse/nTYvX1erTmzMk3ENmmMT/f/BIpny8LUKRVpwlXZeicw+Wi3sS72HfXA+z90ygiLr6Qas2aFGty6MsFJA+9ieRrb+bA27Ood88tAQq2fJ0SuuJu1oCxvW/ljQdeZfQTvuP8/PWPubfPHUwccC9ndjuLTvFd/BxpOVwuGj5+C1tHPsbGfrdT54pe1Gh5erEm2Wu3sOmKe9nU/y5+/+I73PcH30CodXwnYpq5eTb+Hj6c9AaDnxzts92v81fw8sCH/BxdxbWJ70RsMzeT48cya9IbXFNGP9bOX8HzAx/0c3QV4HJR6867+X3SBDL+fANhCX0IadykVDMTHk74oKvI/XVtAII8vkED+jLthSfKrF+SuJwdu/YwZ9abPDbhLiY/94of73iS0gAAIABJREFUo6sEl4umT93E+mufYHX8WKIH9iS81WnFmmSt2cqa/uP5+aJ7yZidSOOHRwQo2DK4DGc9PYoVw5/mu5730WDw+dQ8o1GxJtm701kz9jWSP/ouQEHKyVTpAZ0xJt8Ys9IYs8YY8x9jTESRusHGGGuMae3dDjPGrDPGtC/SZoIxZr53HyuNMRnGmK3en78uo/00Y0xTY0y2t90v3jJXifKjjxHe50YaY6YbYzYbY9YaYxYbY7qX6MdaY8wqY8y9xphy3w9jzAXGmB+8Ma4zxoyp7Pt3oqq3bU3ezt3k794LeXlkzV1IRO/zirWxh7IKYw0PC9o/Wc7uew6LP/wGgI1JG6hZuyZ14+oVa5NzOIe1iWsAyM/NY+uazUS5o/0dapkiOrYiZ/tecnemYHPz+P2zxdTu271Ym0Pf/4w9fASArKT1hAZR/Ee16deVFR8tAWBH0ibCa0VQK7ZuqXY7kjZxIDXT3+FVWPt+Z/PDR4sB2Ja0kfBaNantox/bkjayPwj7Ue3Ms8jfsxtPcsH5ffibBVQ/74JS7SJGjib73zOxOTkBiPL4unVqT53atcqsX/jt91xxSR+MMXRsdxYHDhwkNS3DjxFWTGTnlhzetpcjOwrO74xPvqXexecUa7N/6Ro82QXH4eCKDVRvEFznd50uLcnamkz29n3Y3HySP15K3CXdirU5vDOVg7/swHqC9B+LSvD48RGsTiRDl22t7WStbQfkAEXTK8OAb4GhANbaw8DdwKumQCPgZuBq7z46AZ8C473bF5XR/gHv/jd7n9MBaAMMKlpe5PG2t/wfQAbQylrbFhgJxJToR1ugLzAAeLSsThtj3MB7wC3W2tbABcDNxphLK/sGnoiQuBjyUwrT+Xn7UgmJiynVLvJPA2nw8TvUvXMMvwXpX7/13FGk70k7tp2enE5U/agy20fUrknXi85mzXer/RFehVRzR5O7t7APucnp5Q7Yoob05cCin/wRWqXUqR9F5p70Y9uZyRnUcZd9LIJVnfr1SvQj3VH9cMXEkJ+679i2Jy2VkJji53e1Fq0IiY0jZ1miv8M7aVJS03EX+b1VPy6GlNS0cp4RGNXd0eQU+Tzl7E0ntEHZn6fYYX3IXLDCH6FVWJg7isNF+nB4TwY1HHROSOVVdcp1CdASCrJhwPnAaLwDOgBr7ZfAXmAE8CLwmLX2t7J2WJH21to8YOnR1/bFGNMC6A48ZK31eJ+3xVo728dr7gPGAHcYY0wZu7wd+Ke1doX3OWnABOD+smI45Wzpv6oO/ucT9g66nsyX36D26OsCENTx+XqLfXQFAFeIi7tevpcv/282+3amnOLIKsFnH3x3ou6geMLbtyTt9Y9OdVSVV4l+BDOfp62T+uEz/uL1NW+9nYPTX/VbSKeCr89W2b9yA8hXSGV8nKKv7EVkh5bsfe3jUxpSpfl8Wx10TlSS9eN/weqEL4owxlQD+gNfeosGAV9aazd4p1G7HB38UJB1+wHYaK2tyMr2ctt7p3n7AI94i1oYY1YWaXInUA9Yaa3Nr0h/rLVbvFOucYCvkUNb4F8lyn70lpfinY4dAzB9+nQuqUgQ5cjfl0ZI/dhj29XiYslPTS+zfdbchUQ9MJZgmczoN6I/fYb2A2Dz6o1ENyz8Kz3aHc1v+3xHOubp20jeupc5b33mlzgrKm9vGqENCvsQ6o4mL6V0H2qe35HY269hy9AHsDl5/gyxTOde35fuwy4EYOeqLdRtWJhZrOuOYn9KmX9vBZWe1/fj3GF9ANixanOJfkTzu0P6AeBJTSUkNu7Ytismlvz0wsyVCY+gWtNm1H1uakF9VBS1H3+K/Y9MCroLI8rjjosheV9hv1L2pREXE1xTlVCQkate5PNUvUE0ucmlz+/aPTvQaOzV/HLlw0Fzfh91eG8GYUX6ENYwiiPJzjknpPJOJEMX7h08/QjsAN70lg8Djl52+b53GwBr7R5gAfBaRV6gnPZHB27fAbOttV94y0tOuS45gX5BGX/TFKnzNTT3OVy31r5ure1mre02ZkzVl9rl/LKO0NMbEdLQDdWqEdEvgezFS4u1qXZ64YLX8At6kLtjd5Vf92SZ+/YXTBxwDxMH3MPyucvodVU8AK06n0HWgUNk7iv9i2bIuOFE1KrJv/7yZqm6QMtavZEaTRsSelp9TGg16lzei/1f/1CsTVib5jR68na23zSZ/PTfAxRpaYnvzGPqgAeYOuAB1s79kS5X9gSgceeWZB/ICuq1ckUteWcuzw6YyLMDJrJ67nLOubIXAE07t+LwgaygXCtXlrz16whpdBoud8H5HRZ/ITmJhQvVbdYh0q8eSMb1Q8m4fii5v/7iuMEcQPwFPfj0y/lYa1m15lciI2sSGxN804AHV24irFkDapwehwmtRtTAC/ht7vJibSLaNaPZM7ewfuQU8oLo/D5qf9JmIpq7CW8ciwkNwT3oPPZ9FXzLPk4WraE7sQxdtncd2zHGmGjgQqCdMcYCIYA1xkywhTn2yr4XvtpvLvna5VgLdDTGuI5OuZbHGNMcyAf2ldFkLdCNgjV/R3UFfqlgPFWT7yHjry8T9/IzEOLi0KdfkLtlO3VuHknOr+vJXpxIrWsGUeOcLpCXh+fAQTIee8YvoVVW0oKf6JzQlb8tnkaO97YlRz0z50UmDriHKHc0V955Dbs37eTp2S8A8NXbs1nw/teBCru4fA97Hp1Gs7f/UnDbkv98zZGNO4i751qyf97Iga9/oMEDN+KqGUbjvxfMyufuSWX7TWVfARgI6xYm0TqhExMXTSUn+wj/GT/9WN3dc6YwdUDB8tUB9w+n08DzCA2vzqTEV1g+ayHzpn4YqLBL+WVhEm0TOvPIor+Rk53DjPGFfwtOmPMMzw6YCMAV919Lt4HnExpenccTXyVx1gK+mPpBWbv1H08+B1+ZSp0pz2FcLg5/NYf87duIuGEUeRvWkZO49Pj7CALjH32a5UmryczcT59B13Hb6OvJyyvIXA0ZfCm9zj2bJYnL6X/NKMLDwpg86Z4AR1yGfA/bHvwHZ773CCbERer788nesJNG44dyaNVmMucup/HDIwipGUar18cBkLM7jQ0jpwQ48EI238O6B/6PLu9PwoS42D1zIYfW76LFhD+xf9UWUr/6idqdmtPp/+4jtG5NYvt1ocX4q1nae3ygQ5cTZCq7XsYYc9BaG1mi7Gagi7X25iJliyhYv7bEu/1P4HNr7QclnluhcmNMU+92uxLtfJZ76/4NrAcesdZaY0wroI219pOi/TDGxAIzgERrrc8LI4wxDYBlwBXW2pXeQeyXwOPW2uPNB9od3focp0lwa/zjfIY0GXT8hkFs1vaCNS4/N7s8wJFUTfutnzGh6bDjNwxiz26bCcBdTYcEOJKqeWnbLABS+/YOcCRVEztvEblpW47fMIiFxjQHYFnDKwMcSdV03/MRc+sPPX7DINYv5X0of8brpLut6TV+W9z26rZ/B+HCz5N3Y+FhwNMlyj4EhlNw4cSpVnIN3VvW2peAPwPPA5uMMVlAOnD0z4+jU8ehQB7wDvBCWS9grd1rjLkOeMMYU4uCD+vUCgzmRERERE6pSg/oSmbnvGXxPspeKrE9soz9VajcWrsNKJWF85aHl7GP/cBNZdSF+Covj7V2MXB2ZZ8nIiIip07wXnvqP/qmCBERERGH03e5lmCMuRgoeTXBVmvt4EDEIyIiIuXzKEenAV1J1tqvgK8CHYeIiIhIRWlAJyIiIo4WzPeH8xetoRMRERFxOA3oRERERBxOU64iIiLiaFYXRShDJyIiIuJ0ytCJiIiIo+miCGXoRERERBxPGToRERFxNK2hU4ZORERExPGUoRMRERFH0xo6ZehEREREHE8ZOhEREXE0j9UaOmXoRERERBxOGToRERFxNOXnlKETERERcTxl6ERERMTRPMrRKUMnIiIi4nTK0ImIiIij6ZsilKETERERcTwN6EREREQcTlOuIiIi4mj66i8wVndX9ge9ySIi8r/E+PPFhjQZ5Ld/Z2dt/9ivfasoZej85MDdlwc6hCqpNfUz7mo6JNBhVMlL22YBcOC2/gGOpGpqvfoFf2t8XaDDqJKxO94FIP3S3gGOpGqiZy8C4BP38ABHUjUDk99jWcMrAx1GlXTf8xEAuWlbAhxJ1YTGNGdefWf/ru2bMsvvr6nblmgNnYiIiIjjKUMnIiIijqbblihDJyIiIuJ4ytCJiIiIo+kqV2XoRERERBxPGToRERFxNN2CTRk6EREREcdThk5EREQcTfehU4ZORERExPGUoRMRERFH01WuytCJiIiIOJ4ydCIiIuJo+qYIZehEREREHE8DOhERERGH05SriIiIOJpuW6IMnYiIiIjjKUMnIiIijqav/lKGTkRERMQvjDFRxph5xpiN3v/X89GmiTHmJ2PMSmPMWmPMLRXZtwZ0IiIi4mgePz6q6H5gvrW2FTDfu13SXuA8a20noDtwvzGm4fF2rAGdiIiIiH8MBP7l/flfwKCSDay1OdbaI97NGlRwrKY1dCIiIuJoDrqxcH1r7V4Aa+1eY0ycr0bGmNOB2UBLYLy1ds/xdqwBnYiIiEgFGWPGAGOKFL1urX29SP3XgNvHUx+s6GtYa3cCHbxTrR8bYz6w1qaU9xwN6ERERMTR/HkfOu/g7fVy6i8qq84Yk2KMaeDNzjUA9h3ntfYYY9YCPYEPymurNXQiIiIi/vEpcIP35xuAT0o2MMacZowJ9/5cDzgfWH+8HStD5yAhrbsQduVNYFzkfj+PnPnFB+vVzulDjStuxP6eDkDuktnkfj83EKEe11WPjqRNQmdyso8wY9xr7Fq7tVSbS8cN4ZwrexFRJ5LxbW/wsZfACmnTlbA/3VJwPJZ+Sc7c/xSrr9bjImoM/jM2Mw2A3EWfkbv0q0CEWq7ef7mepgmdyMs+wtz7Xid1zbZSbeLaN6Xv8zdTLaw62xauZNGj7/g/0HKEdj2HmmPuBJeLw3Nnc/g/7/lsV/383tSa9DiZY8eQv+m4vx/9Ki6hA+0nj4AQFztmLGTjK58Vq4/u0Zp2j19P7TaN+fGWl9n7+Q8BirR8deI702TyKIzLxb6ZX7P3lf8Wq3ePuZy44Rdh8/LJTd/Plnv/Ts7u1ABF69tDT73A4u9+IKpeXT5+d1qpemstU6ZOY0nicsLCavDkg/fR5syWAYi0fNEJHTnziZGYEBe7Zyxg28vFxw51e5zFmZNvILJNY36++W/s+3xZgCKtOgfdh+5p4N/GmNHADuBPAMaYbsAt1to/A2cBzxtjLGCA56y1Px9vx8rQOYVxEXb1LWRNf4xDT99OtS69cNU/vVSzvKQlZP11LFl/HRu0g7k28Z2IbeZmcvxYZk16g2ueHO2z3dr5K3h+YIWXHPiXcRE25HayXnmYQ5Nvplq3eFzuxqWa5f20iKwpd5A15Y6gHMw1TehI3aZu/tXrPubf/yYXPjnSZ7uEJ29k/v1v8q9e91G3qZsm8R38G2h5XC5q3no3+x+dQOatN1CjVx9CTm9Sul14OGFXXEXuurX+j/F4XIYOU24kcfizLOg1nkaDz6PWGY2KNcnanUbS2Gns/u/SAAVZAS4XTZ+6ifXXPsHq+LFED+xJeKvTijXJWrOVNf3H8/NF95IxO5HGD48IULBlGzSgL9NeeKLM+iWJy9mxaw9zZr3JYxPuYvJzr/gxugpyGVo/PYqk4VNY2vNe3IPPp2aJz9Th3WmsHfsqyR99F6Ag//dYa9OttX2sta28/8/wlv/oHcxhrZ1nre1gre3o/X+Z07tFndCAzhiT773h3RpjzH+MMRFF6gYbY6wxprV3O8wYs84Y075ImwnGmPnefaw0xmQYY7Z6f/66jPbTjDFNjTHZ3na/eMtcJcqPPkZ4nxtpjJlujNnsvUHfYmNM9xL9WGuMWWWMudcYU+Z7Yoy50hgzv8j2Bd7nn/JMp6tJKzxpe7HpKZCfR17SYqq1736qX/aUaN/vbH74aDEA25I2El6rJrVj65Zqty1pI/tTM/0dXoW4mp6BJ3UPNj254Hj8tIhqHXsEOqxKa96vK79++C0AyUmbqVG7JhFxxY9FRFxdqkeGk7xiEwC/fvgtLS7u5vdYy1LtjLPI37MbT/JeyMvjyOIFhPa4oFS7iOtGk/3BTMjJCUCU5avXuSWHtqaQtWMfNjef3R8n4r64a7E22TvT2P/rTqznJNwJ6xSJ7NySw9v2cmRHCjY3j4xPvqXexecUa7N/6Ro82QXH4OCKDVRvEB2IUMvVrVN76tSuVWb9wm+/54pL+mCMoWO7szhw4CCpaRl+jPD46nRpSdbWFLK3F3ymkj9eSuwlZxdrc3hnKgd/2QFB/JmqKA/Wb49gdaIZumxrbSdrbTsgByh6F+NhwLfAUABr7WHgbuBVU6ARcDNwtXcfnSiYUx7v3b6ojPYPePe/2fucDkAbCu/hsvno/ryPt73l/wAygFbW2rbASCCmRD/aAn2BAcCjZXXaWvsRcNgYM9w7iHsVuM1am1fpd7CSXHWi8fyWdmzbk5mOqVP6F2G1DucRMeElwkbej6kbU6o+GNSpX4/MPenHtjOT06njjgpgRJXnqhuD57fCaSLPb2m+j0fnC4h48FXC/vwgpl7wHY9Idz0O7i08FgeTM4h01yvdJjmj3DaB5IqOwZNWuK7Yk5ZKSHTx9zqkeStcsXHkLk/0d3gVEtagHtlFzonsvRmENXDWOQFQ3R1NTpF+5OxNJ7ScfsQO60PmghX+CO2kSklNxx1X+BmrHxdDSmpaOc/wvxruKI4UORZH9qRTI4jOWzn5TsaU6xIK7pOCMSaSgsV7o/EO6ACstV9ScOfjEcCLwGPW2t/K2mFF2nsHUUuPvrYvxpgWFNxl+SFrrcf7vC3W2tk+XnMfBZch32GMMeX0907gCeAvwHJrrZ/mP3yEVGLNQN6aHzj0+Giynr2L/A0rCRt+t39CqySfb69z1j9UWN7Pyzj08EiynryN/PVJhI24L9Ah+XD8Y2F8tAmq9So+Pk+2RH3Nm24n6x+v+i2kyvrDnBO+fnOW0Y3oK3sR2aEle1/7+JSGdCr4+vyX/89GAARbPKeY9eN/wapKU4XeLFV/4Etv0SDgS2vtBu80ahdr7dE/v+4GfgA2WmsrsqK63Pbead4+wCPeohbGmJVFmtwJ1ANWWmvzK9Ifa+0W75RrHODzfi/eNrOAO4AWZe2r6H1qpk+fzrCKBFAOz+9phBbJ8LjqRmP3l0jxZx049mNu4lxqXD6yiq968vS8vh/nDusDwI5Vm6nbsDCbVdcdze8pZY7vg5InM43QerHHtl31Yo5djHLMoSLH49svqTFolL/CK1eHERfRblgCACmrtxBZZMor0h3FwZTi09wHkjOILJJBjXRHcSgleKbCPWmpuGIK783pionFk16YLTHhEYQ0aUbtp6cW1NeLovYjT7H/8UlBc2FE9p4MwoucE+ENojic7KxzAgoyctWL9KN6g2hyk0tPRdbu2YFGY6/mlysfxuac8gmOk84dF0PyvsLPWMq+NOJigmvq+MjedGoUORY1GkZzxIGfKam4E83QhXsHTz9ScJXGm97yYcD73p/f924DBfdSARYAr1XkBcppf3Tg9h0w21r7hbe85JTrkhPoF/j+G7OwsmDAdxFwEPCx8vpY/K9ba7tZa7uNGTOmrGYV5tmxEVdMQ0xUfQipRrXOvchbU/wqN1O7MJ1erd05eFJ2Vvl1T5Yl78zl2QETeXbARFbPXc45V/YCoGnnVhw+kBW0a+XK4tm+AVdcQ0y093h07U3e6u+LtSl2PDr0wJMcHMdj9dtf817/B3mv/4Ns/uonzrqqYL2Zu3MLjhzIImtf8WORtS+T3EOHcXcu+PvlrKsuYMvcn/wed1nyNqwjpNFpuOq7oVo1avS6kNxlhYu8bdYhfhs+kMxRQ8kcNZS8db8E1WAOIHPlZmo2dxPROBYTGkKjQeeSHETvcUUdXLmJsGYNqHF6HCa0GlEDL+C3ucuLtYlo14xmz9zC+pFTyEv/PUCRVk38BT349Mv5WGtZteZXIiNrEhsTXFPk+5M2E9HcTZj3M+UedB6pX/0Y6LBOGY+1fnsEqxPN0GV717EdY4yJBi4E2nkvtQ0BrDFmgi3MT1f2u219td9c8rXLsRboaIxxHZ1yLY8xpjmQT/k3+rsdWAM8DPzdGHOu9cf8k8fD4Q+nEXHLX8DlInfZ13iSd1C9/7Xk79hI/tofCO11OdXadgdPPjbrAIff+9spD+tE/LIwibYJnXlk0d/Iyc5hxvjCMfuEOc/w7ICJAFxx/7V0G3g+oeHVeTzxVRJnLeCLqeXeV9F/PB4Oz3qNiDueAFcIuYlz8ezdQfXLrid/+wbyf15GaMJAqrXvUXg83n4+0FGXsm3BSpomdOSGJc+Tl53DvHGFF1MN/+JJ3utfcJXxggf/j77Pj6FaWHW2L1zFtoWrAhVyaZ58Dr02ldqTnwOXiyPz5pC/Yxvh140ib+M6cpcF8VWhXjbfw+pJ/+TcmfdjQlzsmPkNB9bvpvWEq8lcuYXkuSuo26k557x1D6F1a+Lu24XW469mYe8JgQ69uHwP2x78B2e+9wgmxEXq+/PJ3rCTRuOHcmjVZjLnLqfxwyMIqRlGq9fHAZCzO40NI6cEOPDixj/6NMuTVpOZuZ8+g67jttHXk5dXkEkcMvhSep17NksSl9P/mlGEh4UxedI9AY64NJvvYf0Db9Hl/UmYEBd7Zn7DofW7aDHhT+xftYXUr36idqcWdPy/+witW5OYfl1pMf5PJPYeF+jQ5QSZExmLGGMOWmsjS5TdDHSx1t5cpGwRBevXlni3/wl8bq39oMRzK1RujGnq3W5Xop3Pcm/dvym4Id8j1lprjGkFtLHWflK0H8aYWGAGkGit9XlhhDHGDSQC51hrU40xH1IwxfxGWe+Vlz1w9+XHaRLcak39jLuaDgl0GFXy0rZZABy4rX+AI6maWq9+wd8aXxfoMKpk7I53AUi/tHeAI6ma6NmLAPjEPTzAkVTNwOT3WNbwykCHUSXd93wEQG7algBHUjWhMc2ZV9/Zv2v7psyC48x2nWy9GvXxW+ps8e75QblA8WTeh24Y8N8SZR8C/vpN16LEbUvu8pb/mYLvVNtkjPkZeAM4+iW34UdvWwJ8Dcyl4GKHsrwAPGutPXp5493Ag8aY4Mq1i4iI/A+xfnwEqxOaci2ZnfOWxfsoe6nE9sgy9lehcmvtNqBUFs5bHl7GPvYDN5VRF+KrvCzW2uEltncCTSuzDxEREZGTTV/9JSIiIo4WzDf89RcN6HwwxlwMPFOieKu1dnAg4hEREREpjwZ0PlhrvwKC74s3RUREpBRl6E7uRREiIiIiEgDK0ImIiIijBdXXEQaIMnQiIiIiDqcMnYiIiDia1tApQyciIiLieMrQiYiIiKNZZeiUoRMRERFxOmXoRERExNF0lasydCIiIiKOpwydiIiIOJquclWGTkRERMTxNKATERERcThNuYqIiIij6aIIZehEREREHE8ZOhEREXE0XRShDJ2IiIiI4ylDJyIiIo6mr/5Shk5ERETE8ZShExEREUfz6CpXZehEREREnM7o3i1+oTdZRET+lxh/vljb+t399u/s2pRlfu1bRSlDJyIiIuJwWkPnJ6ubXh7oEKqkw7bPmNh0WKDDqJJnts0E4DO3s/txefJMXjn9ukCHUSV37HwXgI/dwwMcSdUMSn4PgJkNrw1wJFUzbM8M5tYfGugwqqRfyvsAzKs/JMCRVE3flFnkpm0JdBhVEhrT3O+vqTV0ytCJiIiIOJ4ydCIiIuJoug+dMnQiIiIijqcMnYiIiDia1tApQyciIiLieBrQiYiIiDicplxFRETE0XRRhDJ0IiIiIo6nDJ2IiIg4mi6KUIZORERExPGUoRMRERFH0xo6ZehEREREHE8ZOhEREXE0az2BDiHglKETERERcThl6ERERMTRPFpDpwydiIiIiNMpQyciIiKOZnUfOmXoRERERJxOGToRERFxNK2hU4ZORERExPGUoRMRERFH0xo6ZehEREREHE8DOhERERGH04DOQSJ7d+HM+a9x5jfTib316lL1MaMHcsa8v9Pqi5doNuMJQhvFBiDKirni0RsY/82L3P3FMzRs29Rnm4vHXcMDS1/h8bX/59/gKig2oSMJ3z7PhYkv0vKOK0rVR/VoTa+5T3HprndpcNk5AYiwYnr+5XquW/I8Q+c+RWy7pj7bxLZvyrB5U7huyfP0/Mv1/g2wAuISOtDn2+e4KPEFWt1xean66B6tiZ/7JFfseoeGQXosGsR34NIlf+Wy757nLB99iO3emou/eoIhO97m9EuDsw8A0QkdOf+7F7jg+6k0vbP0eVGvR2t6zJvCRbtnUP+y7gGIsGKiEzpy3ncvcv73f6PpnQNL1dftcRbd5z1Nn93vERek/XjoqRfodelQBl13i896ay1Pvfga/a8ZxeARt/LL+k1+jvDk8Vjrt0ew0oDOKVwuGj1+C1tHPsaGvrdT94pe1Gh5erEm2b9sYePl97Kx/138/sV3NHjgxgAFW74z4zsR08zNX+Pv4aNJbzD4ydE+2/06fwWvDHzIz9FVkMvQfsqNLBv+DAt7jaPh4POIPKNRsSbZu9NIGjuN3f/9LkBBHl+ThI7Ubebm3Z73sXDim/R+aqTPdvFP3cjCiW/ybs/7qNvMTeP4Dv4NtDwuQ8cpN5I4/Fnm9xrPaYPPo5aPY7Fi7DR2/XdpgIIsn3EZuj41km+ufZY58RNoMvBcarcq3oes3Wksu3s624O0DwC4DGc9PYoVw5/mu5730WDw+dQsdSzSWTP2NZI/Ct7zApeh9dOjSBo+haU978Xtox+Hd6exduyrQd2PQQP6Mu2FJ8qsX5K4nB279jBn1ps8NuEuJj/3ih+jk5OtQgM6Y4zbGPO+MWazMeYXY8xxLcFfAAAfx0lEQVQcY8wZxpg1ZbSvZoxJM8ZMKVF+mTEmyRizyrufm4vUjTDGrDHGrPXWjfOWG2PMQ8aYjcaYDcaYhcaYtkWet80Y87P38Ysx5gljTA1vXVNjTLYxZmWRx4hy+hlpjJnu7edaY8xiY0x3b501xrxToo+pxpjPK/IeVlVEp1bkbN9Lzs4UbG4emZ8tpna/4n8VHkr8GXv4CABZSesJdUf7I7RKa9uvKz99tASAHUmbCK8VQa3YuqXa7UjaxIHUTH+HVyH1Orfk0NZksnbsw+bms+fjRNwXdyvWJntnGgd+3QGe4P2Lrlm/rqz78FsAUpI2U6N2TSLiih+LiLi6VI8MJ3lFwV/v6z78luYl+hpI9Tq35ODWlGPHYtfHibgv7lqsTdbONPb/uhM8wfkF3lGdW3BwWwqHdqTiyc1nxyffc1qJPhzalUbmrzuxQfx5qtOlJVlbk8neXnAskj9eStwlxT8rh3emcvCXHQ7oR0qxfvx/e3ceb3VV73/89QYRUERFFDBTgxxyQnHI1MoJH445kJoNt24qDWqm5Zi3Mn/9unUbtGzQyqmbaFaWlgNOmJbmiKKpqQwOgAqogIzC+/7x/R7cHPaBsw+H891r8Xny2I/Hd9j7nM9i7bPXZ6/v+q614YG7LvWclnI063sKYJcdt2fdvuu0ef6ue+/nIwfuhySGbvc+Zs2azWvTZnRhhJ3HXfivWa0woZMk4HpgjO0htrcBzgUGLOdlBwDPAMeUr0dSD+BS4DDbQ4GdgDHluYOALwMH2N4WGAa8Wf6sk4A9gKG2twS+A9wgqVfN79vH9vbAbsDg8ve0eN72jjWPq5YT96+AGcAWZRyfAfqX594CtpPUu9wfDry8nJ/VqXoM2ICFk6ct2V84ZTo9BrSdsPU7ZjizxjzcFaE1rO+Afrw5efqS/TenzqDvwH4VRtS4XoPWZ25NGeZNmU6vQetXGFHH9Bm4PrNryjF7ygz6DFx/2edMmbHc51Sp9zJ1MYPeg9J6P601sB9zasowZ8oMeif4fuo1sB/zauti8gx6Jva3DdBzYD/m15Rj/uTp9Gyi93xneeW16QzcqP+S/QEb9eeV16Yt5xWhmbWnh24fYKHtX7QcsD0WeHE5rzkOuAh4Adi9PLYOxTQp08ufMd/2M+W5c4Cv2p5cnptn+5flubOAU2zPKc+NBv4BfKL1L7U9G/g8cISkhj5FJA0B3g+cZ3tx+fPG2/5rzdNuBg6pKeOoRn7HSiny4qW1cS1/vSP2pvcO7+W1S/+4ioPqoAbK0rTqlqHrw1hpdcqxzO3/7XlOlbJ4P9U5lloZoH45UvzDqPeeylC9v2MlWnbbXfZoVu2Zh247oN1dPWUP1n7A54D1KBKf+2zPkHQDMEnSHcBfgFFl8lT3d0jqC6xt+/lWpx4Ctm39fADbMyVNALYAXgGGSBpb85RTbN9T56XbAmNtL1pO8a4Bvl5eZt0BuAz4YL0nShoJjAS45JJLlmS1HbVw6jR6bPzON6kegzZg4avLdo332XMoG518DM8few5e8PZK/tbO84FPDWe34/YF4KXHxrPuxu/0Lq47sB8zX3m9qtA6ZN7kGfSuKUOvQRswb2oaZdj+0/uzzXH7APDqY+PpU1OOPoP68dYrS1/mnj1lBn1qerzqPadKc5epi37MTaQuWsyZMoO1asqw1qB+zJ3aPP/H7TVvygx61dbFxv2Yn1hdAMyfMp2eNeXoufEGSZZjRQZu1J+pr77TI/fKq9PYqH9zDtUJK7Yqboo4FLir7FH7A3CkpO4Atk+gSPYeAL5KkRB1hFj+177arxitL7nWS+baxfbjwOYUSepNK3jupbZ3sb3LyJEjO/orl5jz2LOsufnG9NhkAOqxBusd9iFm3vbAUs/pte1g3vX/T2LiCRewaPqbbfykatz3m9u46OBzuOjgc3hy9EPsfFSRB2+603uZN2tO046Va8sbY59n7cED6b3phqhHdzY+4gNMHd2cl7hbG3fl7Vx74Ne49sCvMf7Wh9l6xF4ADNhpCAtmzWHOq0vXxZxX32DBW/MYsNMQALYesRcTmqisb4x9nj6DB7JWWRebJFQXLWaMHc867xnI2u/ekG49urPp4bvzUmJlAJj56POsVfN3MfCIPXj11nTL0aumHK/d+lDVYXW6vffanRtuuQPbPPbEU/TpszYb9k/vEjkUS3911aNZtaeH7klg2Tky2nYcsKekieX+BhSXbW8HsD0OGFfeYDCBYpzak8DOwJ21P6jsbXtL0mDb42tODQPurvfLJa1DkXT9G1i3gbifBIZK6tZyybUNNwDfB/Yuy9Y1Fi1m8td/weCrzofu3Xj9d7cz/9kXGHDaJ5g77llm3v4Ag875T7qt1YvNfnY2AAtffo2JJ7Z9h1NVnr7rUbbaZ0fOvPtCFsydz3VnXLLk3Kk3fYeLDj4HgIPO/jg7Hb4HPXqvybn3XcwD197F7Rf+oaqwl+JFi3ni3CvYfdQ5qHs3Xhw1htnPvMRWZ36UN8ZO4JXRD7PujoPZ9bLT6bHe2gwYPoytzjiaMR8+o+rQlzLpzrFstu9QPnXvD3h77gLu+Mo7w0+PveXbXHvg1wC4+9zL2e+HI1mj15pMuusxJt31WFUhL8OLFvP4uVewx6izUfduTBo1hlnPvMzWZ36UN8aOZ+roR1hvx8G8/7LT6LHe2gwcPoytz/god374zKpDX8KLFvPQ165g76vPQt27Mf6au5n575fZ/owRzHhsAi+PfoR+QwfzwV+fxprrrcW7hu/E9l8dwU37nFV16EvxosU8fc7lDLvmXNS9Gy+Puou3nnmJIWcezczHxvParQ/Td8fB7Hj5V+ix3tpseMAwhpzxUf7RZH8XXrSYZ865bEk5Jo8aU6ccQxhalqP/ATsz5Iyjue/DX6069KWc8Y3/5sFHH+eNN2ay3xGf5IvHf4q33y6u3Bx75CF86AO7cs99D3LQMZ+ld69eXHDuaRVHHFaGVnQ9uLyp4X7gVy3j2iTtCqwF/NT2djXP7Qs8B7zb9vzy2H8CewGnArvYHlMe3x+40PZ2kg4GvgUcantqeZfq52z/WNKXKG6yONr23PJ1lwLblvsTy587TVIf4OfAYtuflrQ58JfaGFdQ1t9R3MzxdduWtAWwje0/S5ptu4+kTYARti+StDfF2L9DV/Cj/fjmy84rlZIdJt7IWZsfV3UYK+W7E4shjzcOTLsch00dxcXv/mTVYayUk1/8XwD+NPDjFUeyco6YejUAozZeZkhvUo6b/FtGD/hY1WGslANeuQaA2wYcW3EkK2f4K9eycNr4FT+xifXoPxjaGFG5qvTvu2WXdZ1Nm/nvphxouMIeujKxORK4UNLZwDxgIsVdqVtJeqnm6RcBd7Ykc6U/A98DTgfOlHQJMJfirtHPlL/jJkkDgNvLBNK8czn2J8D6FL16i4CpwOG259b8jrvK13WjuCP3gppzrcfQXWb7x20U9wTgB8BzkuZQ3MCx1FdH2y+V5QwhhBBCaArtueRKeffpMXVO9WjHa2cALUsWHLyc510OLLMkgIsuxPPLR73Xbb6cnzkR6N3W+TrPnwmc2Ma5PnWOjaGceiWEEEII1WjmFRy6SqwUEUIIIYSQuHb10OVG0j+Bnq0Of6q8YSOEEEIICWnm+eG6ymqZ0NluzpWUQwghhBA6YLVM6EIIIYSQj2aeH66rxBi6EEIIIYTERUIXQgghhJC4uOQaQgghhKTFTRHRQxdCCCGEkLzooQshhBBC0mJi4eihCyGEEEJIXvTQhRBCCCFpjmlLoocuhBBCCCF10UMXQgghhKTFGLrooQshhBBCSF700IUQQgghaTEPXfTQhRBCCCEkL3roQgghhJC0uMs1euhCCCGEEJIXPXQhhBBCSFqMoYseuhBCCCGE5EUPXQghhBCSFj100UMXQgghhJC8SOhCCCGEEBKn6KbsEvGfHEIIYXWirvxla6z5ri5rZ99e8HKXlq29IqHLhKSRti+tOo6VkUMZII9y5FAGiHI0kxzKAHmUI4cyhGXFJdd8jKw6gE6QQxkgj3LkUAaIcjSTHMoAeZQjhzKEViKhCyGEEEJIXCR0IYQQQgiJi4QuHzmMh8ihDJBHOXIoA0Q5mkkOZYA8ypFDGUIrcVNECCGEEELioocuhBBCCCFxkdCFEEIIISQuEroQQgghhMRFQpcwSdtVHcPKyqEMIXQmSfvWbL+n1bmjuj6i1ZekNttISet1ZSwhrEjcFJEwSfcCawJXAFfbfqPaiBqXSRm6A4cAmwNrtBy3/cOqYuoISafXOfwm8LDtsV0dT0dI2gc4BdiqPPQUcLHtMZUF1SBJj9ge1nq73n6zk3QQcA6wDcUSiP8Cvmv7pkoDaydJjwBfsP3PVsdPAM61PbiayNpP0g3LO2/7I10VS1i11ljxU0Kzsr2XpC2AzwIPSXoAuNz2bRWH1m45lAG4EZgHjAMWVxzLytilfNxY7h8CPAh8XtJ1tr9XWWTtIOkQ4GLgW8D5FGtJDgMuk3RyKkkES6+B2XrNyKZcQ7IeSScCnwPOBB4qD+8C/LekTRJZeupLwKXl59JZwGbAz4CXgA9VGVgDPgC8CIwC/klC76HQmOihy0DZQ3QE8GNgJsUf7Lm2/1hpYA1IuQySHre9Q9VxrCxJtwIjbM8u9/sAvweOpOil26bK+FZE0hjgVNuPtTq+A/AT2x+uJLAG5dJDJ+lfwF62Z7Q6vgFwr+33VRNZY8rPpvOBk4DZwPG2R1cbVfuV8Q8HjgN2AP4KjLL9ZKWBhU4XY+gSJmkHST+iuKy0L3BY+SG5L/CjSoNrpxzKANws6YCqg+gEmwILavYXApvZngvMryakhgxsncwB2H4cGFBBPB01WNINkm6s2W7Zf8+KXtxE1DqZA7A9vYpgVsLRFMnQz4EpwLGS+lUbUvvZXmT7FtufBnYHngPGSDql4tBCJ4tLrmm7GPglRU/W3JaDtidLOq+6sBqSQxnuB64vB1AvpOhdtO2+1YbVsKuB+yX9udw/DBglaW2KsU/N7q0Onms2h9dsf7/Vudb7zWympKF1ekyHArMqiqkhkm4H5gL7254g6WvAycCDkr6byGVjJPWkGEJxHMVY3x8DTX/1IzQmLrkmrLwkNtf2onK/G9DL9pxqI2u/TMownuJy8Tgn/gclaWdgL4qk9F7bD63gJU1D0hvA3+qdorj0t34Xh7RKSfqD7RFVx9EWSXsBvwUuBx6muCliV+DTwCdt31theO0i6Ujb19c5PhD4ge1PVBBWQyRdCWwH3AxcY/uJikMKq0gkdAmTdD/FN8faMU+jbe9RbWTtl0kZbgUOsp3yDRFIugi41vY/qo6lIyQtd4yc7bu7KpauIOlR2ztVHcfySBpAMfZsW4rE+kngp7anVhpYJ2vm5FrSYt7poa5t8FO9khDaEJdc09arJRECsD1b0lpVBtQBOZRhCsWYlJupGWuW2rQlwCPAeZK2BK6nSO6S6aFrb8LWzI1vg5r+27jtV4CvL+85mdRH005fYjvGyq8moqLT9pak2jvgdqYY75GSHMowAbiDYj69dWoeSbF9pe2Dgd2AfwPflfRsxWGtCk3b+K6mcqiPpk2uJe1azgfY+vhh5edtyET00KXty8B1kiaX+4OAYyuMpyOSL4Pt86uOoZO9F9iaYvB0CjdDNKppG98G5TKfWC710az+B/hMneNPAZdSzCgQMhAJXcJsPyhpa4pZ8QU8bXthxWE1JIcySLqLOo2S7aQ+KCV9FzgKeB64FrggxZU7ciJpQwDbr9U5fVYXhxPa1szJ9Qa2J7Y+aPu5ck7AkIlI6NK3K+8sObWTJGxfVW1IDUu9DF+t2e4FjADeriiWlTEB2IPiElhPYIeyLurdOZqyZm58kSTgGxTTYwjoJultismRv9XyvJQmt12Bpq6PFgkn172Xc27tLosirHKR0CVM0m+AIcBYYFF52EAyyVAOZbD9cKtDf5eU4h2Vi4A7gU0o6mN34D4SvCSTcOMLxTCEPYFdbU8AkDQY+Lmk02ynMuH2EqnWRybJ9e2Svg2cVzutkqTzKf7eQyZi2pKESXoK2Cbluc8yKUPtrPHdKNarvMj2Vm28pClJGkfRW3q/7R3LS+Hn205iTGO9xpeip3SpxrfZSXoUGG57WqvjG1JM6dPUU5W0yKE+JJ0GHAyMbJ1cA7ekkFyXE4P/iuJmp7Hl4aEU6+ueUDvLQEhb9NCl7QlgIMW0GanKoQwtk6aKYqWIicDxVQbUQfNsz5OEpJ62n5aUUlKaS89Wj9bJHBS9W5J6VBFQB+VQH/9Bq+Ta9nhJnwRGk8DyhLbfAo4r/++3LQ8/aXt87fMkbRvru6YtErq09Qf+JekBlp7/7CPVhdSwHMpwFsW39ZmS/gsYBiSz0kWNlyStB/wJuE3S68DkFbymmSTf+JYWdPBcs8mhPnJJrikTuPHLecpvKD67QqIioUvbN6sOoBN8s+oAOsF5tn9XLnU0HPgBxSWZ91cbVmNsH1lufrO8c3dd4JYKQ2pULo3vUEkz6xwXxU03qcihPnJJrtsjiZtTQtsioUuY7bslbQZsYfv2coWF7lXH1YgcysA7N3McAvzC9p8lfbPCeFZaostkZdH42k7t/d+WHOojl+S6PZIdxxwKkdAlTNKJwEigH8Wdou8CfgHsV2VcjcihDMDLki4B9qdYXaEnsQpLFbJofCXtCvS3fXOr44cBk+vcVd2skq+PjJLrsBqIRidtJ1EMOp4JYPtZYKNKI2pcDmU4BrgVOLCciLcfcEa1Ia1+bHe33bfOYx3bqVzig2Jm/6fqHH+qPJeEHOpjNVs2K5Ve09CGSOjSNt/2kj9CSWuQXrd58mWwPcf2H8tkFNtTmnxeqixl1Pi2ObM/kMzM/pnUR/LJdXkTSsv2nq3OndyybXv3rowrdL5I6NJ2t6Rzgd6ShgPXATdWHFOjcihDaA7JN76lXGb2z6E+ckiuT6/Z/kmrc5/tykDCqhUJXdrOBl4DxgGfA24Czqs0osblUIbQHHJofKGc2b+cmHeJBGf2z6E+ckiu1cZ2vf2QsLgpImG2FwO/LB+p6g1cZvuXAJK6l8dSnMctVCuHxhfgKxQz+z8nqfXM/idWFlXjcqiPHJbNchvb9fZDwmLpr4RJmkCdP0jbgysIp0Mk3Q/s37L8jKQ+FMsb7VFtZCE1kn4BTKd+4zvI9sjKguuAFc3s3+xyqI8VLJt1ou1ZVcXWXpLmAM9R9MYNKbcp9wfbTiW5DisQPXRp26VmuxdwNMUdlinpVbuWoO3Z5Vx0ITQqi54tSZ+0/b/lqgqDbP+95tzJti+uMr4GJF8f7V02q8m9r+oAQteIHrrMSLrX9l5Vx9Fekv4OnGL7kXJ/Z+Bi2x+oNrKQqsQbXyQ9YntY6+16+ylIuT5akutye8+Ek2vKZf22KHf/bfvNKuMJnS9uikiYpGE1j10kfR5Yp+q4GvRl4DpJ90i6B7gWOHkFrwlhGS3TM5QJwwzbN7YkD7XTMyQgi0HsmdRH8neISlpT0hXAROBSijHXEyVdJmnNKmMLnSsSurT9oObxHWBnikluk2H7QWBr4AvAF4H3JTQTfmguyTe+pVwGsedQHzkk1+cBPYB3297J9o7AphRDrv6r0shCp4oxdAmzvU/VMayscrzc6cBmtk+UtIWkrWz/perYQnJyaHwBtpb0OOUg9nKbcj+ZG57Ioz5ySK6PAnazvWTmANuzJH0RuJ9I6rIRCV3CJJ2+vPO2f9hVsayEy4GHgZYxcy9RTC4cCV1oVA6NL+QziD2H+sghuV5cm8y1KG9AS6UeQjtEQpe2XYBdgRvK/cOAvwEvVhZR44bYPlbScQC257aeUDWEdsqh8cX2JElHAO8Fxtm+teqYOiiH+sghubak9anfK7q4q4MJq04kdGnrDwxrmQtJ0jeB62yfUGlUjVkgqTflN3ZJQ4D51YYUEpVD44ukn1HcFfoP4AJJu9m+oOKwOiL5+sgkuV6X4ipIvYQueugyEtOWJEzS08BQ2/PL/Z7AY7a3rjay9il74j4FHA9sA4wG9gQ+Y3tMhaGFRGXQ+CLpCYq/60XlGNN7bKeymP1SUq+PVsn1fsCNiSbXYTUQPXRp+w3wgKTrKb5pHQlcVW1I7Wfbkk4FDgB2p/gGeartadVGFlKUUc/WAtuLAGzPSXUIQib18SFaJddAamWgnJ7kExT1YeBfwNUtnQEhD9FDlzhJw4APlrt/s/1olfE0StJPgSvK6UtC6LBcerZqlmqCpZdrEsX3oB2qiq0ROdRHJhM7b0MxzvrvvHPpdRjF1ZDDbT9ZYXihE0UPXfrWAmbavlzShpLeY3tC1UE1YB/gc5ImAW+RWKMVmkoWPVtkMPaslEN9tNzYAUvf3JHS59RPgC/Yvq32oKT9gYspPoNDBqKHLmGSvkFxp+tWtreUtDHFTRF7Vhxau0narN5x25O6OpaQtox6tn4GnG17ZtWxrIwc6qOtz6cWKXxOSXq6rXHVkp6yncsXiNVe9NCl7UhgJ+ARANuTJSW19FcKH4ghGbk0TBOBhyV9w/bVVQezEnKoj7NIP7nuJqln6/FyknoROUBWYumvtC1w0cXaMuXH2hXHE0KVzgJetz2p3qPq4NrL9veAvYHDJd0h6aOSjmp5VBxeI3Koj4kUyfXHqw5kJVwF/EHS5i0Hyu3fUdxYFzIRCV3afifpEmA9SScCt1MsvBzC6mgi6Te+ANh+GfgrsCXFhOEtj0OrjKtBE0m8PnJIrm3/P+AW4G+SpkmaBtwN3Gb7W9VGFzpTjKFLnKThFNN+CLi19cDXEFYnkt4F/JBi0u2fUzMTvu0/VhVXIyRtSxH7ZOA021MqDqnDcqgPAEn/AXwbuJN3ymDbn60uqsa1DMlpmYw+5CUSukRJ6k6RwO1fdSwhNJPUG19J/wK+bHt01bF0hpTrI4fkulzz+03bv251/BSgu+0Lq4ksdLYYEJmocm6nOZLWtf1m1fGEULVWje9uKTa+pZdzSOYyqY/rSD+5/izFvHOtXQo8CERCl4lI6NI2Dxgn6TaKOdwAsP2l6kIKoTI5NL4AG1QdQCfJoT5ySK5te0Gdg/MTnRswtCESurT9tXyEEPJofKG4yanNAfcJjT3LoT6ySK4lDbD9SutjVcUTVo1I6BIkaVPbL9i+supYQmgiWTS+wLoUd7PW6z0xkEpCl0N95JBc/w/wV0lfoZyzFNgZ+B7w/cqiCp0uEro0/YlyTISkP9geUXE8ITSDHBpfgBdSuGGgHXKoj+STa9tXSXoN+BawXXn4CeAbtm+uLrLQ2SKhS1Pth8vgyqIIobkk3/iWcpl6IIf6yCK5LhO3SN4yFwldmtzGdgirsywaX2CGpL6JLzcFedRH8p+v5aTzY2w/W+5fBowAJgGfsf3I8l4f0hErRaRpqKSZkmYBO5TbMyXNkpR6IxBCRyXf+JZGk/gKC6Uc6mOGpL5VB7GSTqVYtYPyPTWU4srO6cBF1YUVOlskdAmy3d12X9vr2F6j3G7ZT/3DJ4SOyqHxzWK5qVIO9ZFDcv227YXl9qHAVban274diPW/MxIJXQghFzk0vkA2a7kmXx+ZJNeLJQ2S1AvYj2LN7xa9K4oprAKx9FcIIRs5rB2aw3JTLXKoD0h++bJDgUuA7sCNtk8sj38YONP2IVXGFzpPJHQhhKyk3PgCSHoKODWDSXmBtOsjl+Ra0hrAOrZfrzm2FsVarrOqiyx0prjLNYSQhUzWDgXY0fb81gcl7Ql83PZJFcTUsEzq4/dkkFzbfht4HaBc7msf4OMUl/FjxYhMRA9dCCELufVsAUjakaLhPQaYAPzR9k+qjap9cqgPST1zSK4BJL2f4r10JNAPOAm4obbXLqQteuhCCLnIpWdrS+BjwHHAdOBaii/f+1QaWOOSr4/a+Osl11XF1QhJ36aI+QVgFMWKEQ/F0pH5iYQuhJCFHBrf0tPAPcBhtp8DkHRatSE1Lof6yCS5Hgk8Q3H5+y+250mKS3MZioQuhJCFTBpfKGbx/xhwl6RbgGuov3xWU8ukPnJIrgcCB1DUw4WS7gJ6S1qjHFsXMhFj6EIIWZC0mKLxPb6m8R1vO8n1jiWtDRxB0RDvC1wJXJ/KmLQc6kPSkRRJ6R5AS3L9K9vvqTSwDirnojuU4j21F3CH7WTnCQxLi4mFQwi5GAFMpejZ+qWk/UiwZ6uF7bds/9b2ocAmwFjg7IrDakTy9WH7etvHAlsDY4DTgAGSfi7pgEqD6wDb82z/3vYIYAvg4apjCp0neuhCCFnJoGerF/B54L3AOODXKV8aS70+WpPUDzgaONb2vlXHszIkvWB706rjCJ0jEroQQrZSbHwlXQsspLhceRAwyfap1UbVOVKrj9yS69YkvWj73VXHETpHJHQhhCzk0vhKGmd7+3J7DeAB28MqDqthOdRHzsk1RA9dbuIu1xBCLq5k6cZ3GyDFxndhy4btt4uJ/ZOUQ31sU5Nc/xp4oOJ4GiZpHFCv50bEKhFZiYQuhJCL5Bvf0lBJM8ttUUwxMbPctu2+1YXWkBzqI4fk+iiKxO3FVsc3o1iWLWQiEroQQi5yaHyx3b3qGDpJDvWRQ3L9I+Bc25NqD0rasDx3WCVRhU4XY+hCCFmQtAh4q2UX6A3MIa3GNxtRH81B0hO2t2vj3JLxmiF90UMXQshCRj1bWYj6aBq9lnOud5dFEVa5mFg4hBBCyNeDkk5sfVDS8cTEwlmJS64hhBBCpiQNAK4HFvBOArcLsCZwpO2pVcUWOlckdCGEEELmJO0DtIyle9L2nVXGEzpfJHQhhBBCCImLMXQhhBBCCImLhC6EEEIIIXGR0IUQQgghJC4SuhBCCCGExEVCF0IIIYSQuP8D8jvcHPkR9joAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(ind_data.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1250"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churned=new_dep_data[new_dep_data.EXRELDATEN==1]\n",
    "active=new_dep_data.drop(churned.index,axis=0)\n",
    "mix=pd.concat([active.sample(n=1000),churned.sample(n=250)])\n",
    "li=mix.index\n",
    "li=list(li)\n",
    "rem=ind_data.index.difference(mix.index)\n",
    "ind_data.drop(rem,axis=0,inplace=True)\n",
    "len(ind_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test= train_test_split(ind_data,mix,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayur Maheshwari\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit( X_train,y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=pd.DataFrame(data=y_pred,index=X_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 48,   1],\n",
       "       [  6, 195]], dtype=int64)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "cm = metrics.confusion_matrix( y_test,y_pred, [1,0] )\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.972"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score( y_test, y_pred )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayur Maheshwari\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100,n_jobs=-1)\n",
    "rfe = RFE(rf, n_features_to_select=5)\n",
    "rfe = rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=rfe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=pd.DataFrame(data=y_pred,index=X_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 49,   0],\n",
       "       [  6, 195]], dtype=int64)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "cm = metrics.confusion_matrix( y_test,y_pred,[1,0] )\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score( y_test, y_pred )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayur Maheshwari\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = LinearSVC(random_state=0)\n",
    "svm_clf.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=svm_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=pd.DataFrame(data=y_pred,index=X_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 39,  10],\n",
       "       [ 18, 183]], dtype=int64)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix( y_test, y_pred , [1,0] )\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.888"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score( y_test, y_pred )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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