CiFD_PICKLE_FILE_GENERATION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CREDIT RISK MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#CONTEXT\n",
    "\n",
    "It is important that bank are able to recognize fraudulent customers to maintain proper cashflow and minimize the defaults on loan.\n",
    "\n",
    "CONTENT\n",
    "\n",
    "The datasets contains list of customers who can default on the loan in January 2015 by xyz bank. This dataset \n",
    "\n",
    "This dataset presents details of customers that occurred of 2 year, where we have 10026 frauds out of 150000 customers. The dataset is highly unbalanced, the negative class (frauds) account for 0.06% of all customers.\n",
    "\n",
    "It contains only numerical input variables which are the result data feature enginerring. The feature 'Monthly income' is the transaction Amount, this feature can be used for example  --cdependant cost-senstive learning. Feature 'SeriousDlqin2yrs' is the response variable and it takes value 1 in case of fraud and 0 otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hswamy/anaconda3/lib/python3.6/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"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from math import *\n",
    "from sklearn import ensemble\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import pickle\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn import metrics\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from operator import itemgetter\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "#pd.set_option('display.notebook_repr_html', False)\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn-white')\n",
    "\n",
    "plt.style.use('fivethirtyeight') # Good looking plots\n",
    "pd.set_option('display.max_columns', None) # Display any number of columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading CSV Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"cs-training.csv\")\n",
    "\n",
    "#data = pd.read_csv(\"surrogate_test_data.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>14.771866</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>37500.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.400000e+03</td>\n",
       "      <td>5.000000</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>50%</th>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112500.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>8.249000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  \\\n",
       "count  150000.000000     150000.000000                         150000.000000   \n",
       "mean    75000.500000          0.066840                              6.048438   \n",
       "std     43301.414527          0.249746                            249.755371   \n",
       "min         1.000000          0.000000                              0.000000   \n",
       "25%     37500.750000          0.000000                              0.029867   \n",
       "50%     75000.500000          0.000000                              0.154181   \n",
       "75%    112500.250000          0.000000                              0.559046   \n",
       "max    150000.000000          1.000000                          50708.000000   \n",
       "\n",
       "                 age  NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  \\\n",
       "count  150000.000000                         150000.000000  150000.000000   \n",
       "mean       52.295207                              0.421033     353.005076   \n",
       "std        14.771866                              4.192781    2037.818523   \n",
       "min         0.000000                              0.000000       0.000000   \n",
       "25%        41.000000                              0.000000       0.175074   \n",
       "50%        52.000000                              0.000000       0.366508   \n",
       "75%        63.000000                              0.000000       0.868254   \n",
       "max       109.000000                             98.000000  329664.000000   \n",
       "\n",
       "       MonthlyIncome  NumberOfOpenCreditLinesAndLoans  \\\n",
       "count   1.202690e+05                    150000.000000   \n",
       "mean    6.670221e+03                         8.452760   \n",
       "std     1.438467e+04                         5.145951   \n",
       "min     0.000000e+00                         0.000000   \n",
       "25%     3.400000e+03                         5.000000   \n",
       "50%     5.400000e+03                         8.000000   \n",
       "75%     8.249000e+03                        11.000000   \n",
       "max     3.008750e+06                        58.000000   \n",
       "\n",
       "       NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  \\\n",
       "count            150000.000000                 150000.000000   \n",
       "mean                  0.265973                      1.018240   \n",
       "std                   4.169304                      1.129771   \n",
       "min                   0.000000                      0.000000   \n",
       "25%                   0.000000                      0.000000   \n",
       "50%                   0.000000                      1.000000   \n",
       "75%                   0.000000                      2.000000   \n",
       "max                  98.000000                     54.000000   \n",
       "\n",
       "       NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
       "count                         150000.000000       146076.000000  \n",
       "mean                               0.240387            0.757222  \n",
       "std                                4.155179            1.115086  \n",
       "min                                0.000000            0.000000  \n",
       "25%                                0.000000            0.000000  \n",
       "50%                                0.000000            0.000000  \n",
       "75%                                0.000000            1.000000  \n",
       "max                               98.000000           20.000000  "
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.describe()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop('Unnamed: 0', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data description after dropping Unnamed column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
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       "      <th>count</th>\n",
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       "      <td>0.421033</td>\n",
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       "      <td>0.757222</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <td>249.755371</td>\n",
       "      <td>14.771866</td>\n",
       "      <td>4.192781</td>\n",
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       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\n",
       "count     150000.000000                         150000.000000  150000.000000   \n",
       "mean           0.066840                              6.048438      52.295207   \n",
       "std            0.249746                            249.755371      14.771866   \n",
       "min            0.000000                              0.000000       0.000000   \n",
       "25%            0.000000                              0.029867      41.000000   \n",
       "50%            0.000000                              0.154181      52.000000   \n",
       "75%            0.000000                              0.559046      63.000000   \n",
       "max            1.000000                          50708.000000     109.000000   \n",
       "\n",
       "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
       "count                         150000.000000  150000.000000   1.202690e+05   \n",
       "mean                               0.421033     353.005076   6.670221e+03   \n",
       "std                                4.192781    2037.818523   1.438467e+04   \n",
       "min                                0.000000       0.000000   0.000000e+00   \n",
       "25%                                0.000000       0.175074   3.400000e+03   \n",
       "50%                                0.000000       0.366508   5.400000e+03   \n",
       "75%                                0.000000       0.868254   8.249000e+03   \n",
       "max                               98.000000  329664.000000   3.008750e+06   \n",
       "\n",
       "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "count                    150000.000000            150000.000000   \n",
       "mean                          8.452760                 0.265973   \n",
       "std                           5.145951                 4.169304   \n",
       "min                           0.000000                 0.000000   \n",
       "25%                           5.000000                 0.000000   \n",
       "50%                           8.000000                 0.000000   \n",
       "75%                          11.000000                 0.000000   \n",
       "max                          58.000000                98.000000   \n",
       "\n",
       "       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "count                 150000.000000                         150000.000000   \n",
       "mean                       1.018240                              0.240387   \n",
       "std                        1.129771                              4.155179   \n",
       "min                        0.000000                              0.000000   \n",
       "25%                        0.000000                              0.000000   \n",
       "50%                        1.000000                              0.000000   \n",
       "75%                        2.000000                              0.000000   \n",
       "max                       54.000000                             98.000000   \n",
       "\n",
       "       NumberOfDependents  NoIncome_MI_indicator  \n",
       "count       146076.000000               150000.0  \n",
       "mean             0.757222                    0.0  \n",
       "std              1.115086                    0.0  \n",
       "min              0.000000                    0.0  \n",
       "25%              0.000000                    0.0  \n",
       "50%              0.000000                    0.0  \n",
       "75%              1.000000                    0.0  \n",
       "max             20.000000                    0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning Columns names : Removing '-' from columns names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cleanCol = []\n",
    "for i in range(len(data.columns)):\n",
    "    cleanCol.append(data.columns[i].replace('-', ''))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SeriousDlqin2yrs',\n",
       " 'RevolvingUtilizationOfUnsecuredLines',\n",
       " 'age',\n",
       " 'NumberOfTime3059DaysPastDueNotWorse',\n",
       " 'DebtRatio',\n",
       " 'MonthlyIncome',\n",
       " 'NumberOfOpenCreditLinesAndLoans',\n",
       " 'NumberOfTimes90DaysLate',\n",
       " 'NumberRealEstateLoansOrLines',\n",
       " 'NumberOfTime6089DaysPastDueNotWorse',\n",
       " 'NumberOfDependents',\n",
       " 'NoIncome_MI_indicator']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Cleaned Column names\"\"\"\n",
    "cleanCol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Saving cleaned column names back to the data column\"\"\"\n",
    "data.columns = cleanCol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the top 5 rows of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "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>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime3059DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime6089DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>NoIncome_MI_indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                 1                              0.766127   45   \n",
       "1                 0                              0.957151   40   \n",
       "2                 0                              0.658180   38   \n",
       "3                 0                              0.233810   30   \n",
       "4                 0                              0.907239   49   \n",
       "\n",
       "   NumberOfTime3059DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                    2   0.802982         9120.0   \n",
       "1                                    0   0.121876         2600.0   \n",
       "2                                    1   0.085113         3042.0   \n",
       "3                                    0   0.036050         3300.0   \n",
       "4                                    1   0.024926        63588.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "3                                5                        0   \n",
       "4                                7                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime6089DaysPastDueNotWorse  \\\n",
       "0                             6                                    0   \n",
       "1                             0                                    0   \n",
       "2                             0                                    0   \n",
       "3                             0                                    0   \n",
       "4                             1                                    0   \n",
       "\n",
       "   NumberOfDependents  NoIncome_MI_indicator  \n",
       "0                 2.0                      0  \n",
       "1                 1.0                      0  \n",
       "2                 0.0                      0  \n",
       "3                 0.0                      0  \n",
       "4                 0.0                      0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counting Null/NA values in Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                            0\n",
       "RevolvingUtilizationOfUnsecuredLines        0\n",
       "age                                         0\n",
       "NumberOfTime3059DaysPastDueNotWorse         0\n",
       "DebtRatio                                   0\n",
       "MonthlyIncome                           29731\n",
       "NumberOfOpenCreditLinesAndLoans             0\n",
       "NumberOfTimes90DaysLate                     0\n",
       "NumberRealEstateLoansOrLines                0\n",
       "NumberOfTime6089DaysPastDueNotWorse         0\n",
       "NumberOfDependents                       3924\n",
       "NoIncome_MI_indicator                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Monthly Income and Number of Dependents have null values\"\"\"\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking Class Distribution : This data is highly Imbalanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram of Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a238c88>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H4XBgTU4l3vyhzGWksWhgCBI7BtzR83v6MN+jUSo80jkSmddN2HKmCjsvG2C0AXIJEKyU\n4qEOSrS1luPerhEA4Jy8MO0eDf56Uo8SU3UnrQ7gjcNl+GRke4+2j8hTGEjUqhQZbZj6TYnLBAYA\neGdgCF7oq/FCq4TqBlrNKEoqkWDoXSoMvUsFm716M9e666KWfFPm8jyBcgme6BqID+tMaNh1xYSj\nhWbcG97wbD0ib2IgUatxtNCM8Xt1LpulSiXAXx5qi+eib34IzdMaCp6mkEkluNn+DXWfNyFMgW/a\nyASH+z45X8VAIlHipAZqFT7Lq8Kor1wvIxGslGDr8NAWD6OWIpFIMOQu4SHIf18wwHorU/SIWghH\nSOT3lmv1mH/UdYbZwHAlNgxph671Fr629FTv5ta3nQIqGWD8eZBUaLTjwHUTRnTilkQkLk0aIZnN\nZixatAgjRoxAUlIS0tPTG6179uxZTJw4EYmJiUhJSUFOTo7zPqvVivXr1+PJJ5/EsGHDMG3aNFy4\ncOHOe0HUiM9uyBoMo+mxGnyZ3N4ljMTsdhffKqQSxIcJD9FtO8+FsiQ+TQqkVatWISsrC6tXr8ac\nOXOwefNm7N6926WewWDAzJkzERsbi/T0dCQkJCA1NRWVldUfoA8//BD//e9/MWfOHGzZsgURERGY\nMWMGDAaDZ3tFBGD/NSOWnhN+ESukwF8fbou37w+BQiqerXQ8sdPDzdwbLpzq/cUlI6qsDVxJkMiL\n3AaSwWDA9u3bkZqaij59+mDIkCEYP348tm3b5lI3IyMDcrkcM2fORPfu3ZGamgqNRoOMjAwAwJdf\nfomJEyfiwQcfRNeuXfHaa6+hrKwMx48f93zPqFU7U2pByr5i2OosDw2SS/D5Y+196nyRp4Lq7mA5\nOgTWftwrrA7sumy8ySOIWp7bQMrNzYXZbEZ8fLyzLCEhATk5ObDZhAv1srOzERcXB6m0+mklEgni\n4uKQlZUFAJgzZw6GDBnirC+RSOBwOKDXC7dtIboTZpsDE/YVo9xce+JeAuD9we3wUIc7W2Pkq6QS\nCZ7qESQo++d5HpkgcXEbSEVFRQgODkZAQO0HOTQ0FBaLBSUlJS51w8PDBWWhoaEoKCgAANx///0I\nCwtz3rd9+3ZYrVb079//jjpBVNeKLD1OlQovBT7/vmCM7tq0jVL91dM9hP3fc9WIEhMP25F4uD2j\nazQaoVQKj8PX3DabzS51FQqFS12LxfW6LCdOnMDKlSsxYcIElxCrKzc3110TPaqlX8+bfLGv+QW1\nK3Bypa5b6VyskuDd4yqgzqG6x8OteCzgBnJzbzT6vJ/d8K0rsy4pcC3LL8i/6WOCJDZ0DVThkqH6\nd6jFDmw4fBG/6nDzLYnExhfft7fDX/sZHR3d6H1uA0mpVLoET81tlUo4bTQgIMAlfMxms0u9o0eP\nYvbs2UhMTMTzzz9/2433tNzc3BZ9PW/y1b5G2mvPpUTXOxdkdzjwx6+KYHHUvl9DFQ6sGRmFdm52\nua77vL4ovyAfkRGRN63Tq5caz1aV451jtYfID1S0wZ+iG/9BKDa++r69Va2ln/W5DaSIiAjo9XpY\nLBbn6Een00GpVCI4OFhQNzw8HDqdTlCm0+kEh+m+++47vPrqqxg8eDDeeustXjSMPOZvuVWCi9sB\nwMs9zI2Gkb+tN2qKp3sECQLp4A0zrlRYm2UzWaJb5fYcUq9evaBQKKDVap1lJ06cQExMDORy4Zs4\nNjYWWq0WDkf1yWSHwwGtVot+/foBqJ708Oqrr2L48OGYP3++y+OJbleV1Y5Fx4TrjUZ2CsDI9r51\nOKq59QiW4972tYfVHQA+u+C5yQ11ZwW2xsCnO+M2kFQqFZKTk7F06VKcPHkSmZmZ2Lp1K8aOHQug\neiKD0Vg9fXT48OEwGAxYtmwZ8vLykJaWhqqqKowcORIOhwNvv/02unfvjpdeegklJSUoKioSPJ7o\ndq3PqRRsC6SSAct+0RYcgLt6uqdwtt22PM62I3Fo0sLYmjVIU6dOxeLFizF58mSMHDkSAJCcnIw9\ne/YAADQaDdLS0qDVapGSkgKtVou0tDSo1Wrk5eXhwoULOH36NEaNGoXk5GTn386dO5uvh+T3Skx2\npGUJlw4MigzAvmsmfHZDxl/s9fyqWyDqrgnOKrbgdKnrxCOiltakY2YqlQrz5s3DvHnzXO47fPiw\n4Hbfvn3x0UcfudTr2bOnS10iT1iu1QvWHAXKJHikc8PrjVpzKNXt+9COAdh7rfYSHJ+cq8Lc+0K8\n0SwiJ+72TT7tcoUV75+qEJQ90jkAQXK+tW/mN/XWJG05W4lKC9ckkXfxU0s+7Z1jepjqzFsIUUqQ\n2Ep3Y7gVT3YLRFtl7XG7EpMDH5/jhqvkXQwk8lk5JRb8o96u1Y9HqQRXUqWGqRVSTIoRruP668kK\n2HidJPIiBhL5rPlHy1H3+zMyUIr7I3gl1KZ6vo8GyjrfABf1NnzxE2e8kvcwkMgn5ZVbsbPebtWj\nuwZCxnneTdYhSIax9aaAr8rSO9cRErU0BhL5HLvDge0XhWtnureRIbYdF1rfqmmxGsHto0UWfHPD\n3EhtoubFQCKfc7jAjEsVwh0YRncN5DZUtyGmrQKP1Zsi//bRMo6SyCsYSORTKi12/PeS8FDd41Eq\n9Azm6Oh2vRzfRnD7h0ILvuLF+8gLGEjkU774yYhKq3AR7DsPcEHnnRgYEYCkKOGO/AuOlnPGHbU4\nBhL5jMMFJpfdvGfHt0G3Nhwd3ak3BwSj7gHPnFIr97ijFsdAIp9QarLj+QPCKxT3CpFjer2T8nR7\n7glVuFxR9p1j5bBylEQtiIFEoudwOPDStyUuExnefbAtF8F60Jz+wZDX+c95qcLmMpuRqDkxkEj0\n1uZUuizYTOygxAW9lbt4e1D3YDmejRauS1qZVcEZd9RiGEgkajt+MuDPP5QJyqLUMjzZLbCRR1BT\nNHZJjumxGsG5JG2xBZnXTSBqCQwkEq0dPxnwu33FqDOpDioZMKF3EORSHqprDtEhCiR3Ec64W5lV\n0UhtIs9iIJEobb9YHUb1r4jw7N1BCFPJvNOoVmJGP+FEkb3XTNDquHsDNT/OlyVRMdscmHe0DGtO\nup4XWvVQW3DSl+c1dA6uRxsZ8vS1k0hWn6zA+4NDW7JZ1ApxhESica7Mgsd3FDYaRim91A08iprD\n8E7Cw3afXzCg0GBrpDaRZzCQyOusdgdWZunx0PYCHC2yCO6TSxhG3tC3nRztVbVfD2Y7kH6WF/Cj\n5sVAIq86X2bFo18WYu6RcsGVXwGgs1qGHcntGUZeIJVI8FAH4bWlNp+p5EJZalYMJPIKh8OBj85W\nYvB/CvBjvVERUL1h6jdPRmBgBC9H7i0PRCgRWGfh8ZVKm8s1qIg8iYFELc5kc+DFb0ow/btSwUap\nANAuQIL1g9vh4xGhaBfAt6c3Bcml+E297YQ2nOIiZGo+/MRTiyoz2/F0hg6fnHfdkmZUFxX+96tI\njO0ZxGsbicTkPsLDpQeum3Cm1HVES+QJnPZNLeZGlQ1P7S7CyRKroDxILsEvu6rwYIQSO37eImhC\n79ovQm4N5D3Hiizo3kaGC3WngGdX4C8Pt/Niq8hfcYRELaLKasfYPTqXMOoZLMP+X4bjF5EBHBWJ\nVGJH4Xm8f5yvwtVKTgEnz2MgUbNzOByY8V0pTuiEh3ruC1dg96hw9Gqr8FLLqCniwxSCKeAWO7A6\nW+/FFpG/YiBRs/tLdoXLxd5GdArAf5LaN7oNUGObf1LLk0kkGH6XcJT04dkq6IwcJZFnMZCoWX13\nw4S5R8oFZTFt5dg8NBRBcr79fMXACCU6BNb+/6qyOrAuhz8WyLP4jUDNxmp34P8OlaLuxO4QpQQf\njwhDsJJvPV8il0owrd7VedefqkABtxMiD+IsO2o2H5yuRE6pcBLDuJ5ByLxu4jV2fNDve6vx3gk9\nSs3VPzHKzQ7M+V8ZNg7lpqvkGfyZSs2i0GDDomPCQ3W/vTsIfdpxAoOv0iikeDm+jaDsXxcM2MXd\nG8hDGEjULN7+sRzl5tqDdW0UEsy7N9iLLaI7teVMJYLkEnRWCyeivHyoFPr6F64iug0MJPK406UW\nfFRvZ+jhdwXgK/6S9nkyiQRjewaizhZ3uFJpw5++L4PDwY1X6c4wkMjj0rR6wUSGiECpy+JK8l1R\nGjmm3iOc4PD3c1VYdoJrk+jOMJDIoy7prfi03pqj5CgV5FLuwuBP5vRvg57BwkN3C4/pcaSQlzqn\n29ekQDKbzVi0aBFGjBiBpKQkpKenN1r37NmzmDhxIhITE5GSkoKcnJwG623atAl//vOfb6/VJFp/\nya6Arc7wKDpEjrgwTmTwN0FyKf7xSBjaKoU/NP5+rgo5Jdx8lW5PkwJp1apVyMrKwurVqzFnzhxs\n3rwZu3fvdqlnMBgwc+ZMxMbGIj09HQkJCUhNTUVlpXAB3a5du7BhwwbP9IBEo8Bgw9Zc4f/rmf00\nkHKPOr8UHaLA30aEoe6SMpsD2HS6Eqe5IzjdBreBZDAYsH37dqSmpqJPnz4YMmQIxo8fj23btrnU\nzcjIgFwux8yZM9G9e3ekpqZCo9EgIyMDAGC1WrF48WIsWLAAnTp18nxvqFk0dRuftScrUHc3mc5q\nGZ7uEdTMrSNvqHk/5JZZsSZRuPO31QFsPF3Jy1TQLXMbSLm5uTCbzYiPj3eWJSQkICcnBzabcJV2\ndnY24uLiIJVWP61EIkFcXByysrIAVIfbuXPnsGnTJvTr18+T/SAvKzXZ8cFpYWC9FKuBUsbRkb/7\nTY8gLHswRFBmsVeH0g8FPKdETec2kIqKihAcHIyAgNpZUqGhobBYLCgpKXGpGx4eLigLDQ1FQUEB\nAKBNmzb44IMPEB0d7Ym2k4hsPF0JvaX25JFaLoEUvJZRazG5jwZLHhCGktkOPJ1RhFM8p0RN5DaQ\njEYjlEqloKzmttlsdqmrUChc6losfEP6syqrHWtOVgjKhtwVwNFRK1Fz+C5AJsGYbirBfaVmB369\nuwiX9NZGHk1Uy+1edkql0iV4am6rVMI3X0BAgEv4mM1ml3q3Ijc397Yf6wuv501N7Wt+Qe303lyp\n62aan1yTQ2eq/dGiljnQW1aG/IKyO2+kB+QX5Hu7CS1CDP3sIwcK28nwXUntV8v1Kjue/PI6NsYb\nEeyB3TNby2fUX/t5syNkbt8eERER0Ov1sFgsztGPTqeDUqlEcLBwK5jw8HDodDpBmU6nQ1hY2O20\nG8DNG+9pubm5reZw4q30NdJee9gtOlotuM9sc+Afx/IB1AbV833boGsbcezbm1+Qj8iISG83o9mJ\nqZ+/CXdAcsGAb2/U/pC9aJDirUvt8OnIsDsaObeWz2hr6Wd9bg/Z9erVCwqFAlqt1ll24sQJxMTE\nQC4XfunExsZCq9U6txBxOBzQarWcwODHtuVV4Uqdy1nLJUBoANdbt2YSiQS/7h6IhHrrzzKvmzDr\nUCm3GKJGuf3mUKlUSE5OxtKlS3Hy5ElkZmZi69atGDt2LIDqiQxGY/UeZcOHD4fBYMCyZcuQl5eH\ntLQ0VFVVYeTIkc3bC/IKm92BFVnCc0cPRip5rSOCVCLBc9FBeCBCeP55a24V0uq9Z4hqNOmbo2YN\n0tSpU7F48WJMnjzZGTLJycnYs2cPAECj0SAtLQ1arRYpKSnQarVIS0uDWq2+2dOTj/riJyNyy2pP\nVksBDLuLe9ZRNYVUgr+NCEW3NsIthuYfLce/L1Q18ihqzSSlpaUcP/+sNR23vZW+1p26PaF39Y8L\nh8OBYf8txHFd7SSW+8IVGB8trh8fYjq30pzE3M/8KhtWZFXAUGdPqQAZ8N+k9hgYcWs/YFrLZ7S1\n9LM+Hluh27L/mkkQRgAwotPtz6Yk/xUZJMPEmCAo6nzbmGzAuD3FyC7mkhCqxUCi2/KeVnipgeQu\nKnQMkjVSm1q76BAFVg5qKygrNtnxxM4ihhI5MZDolv0v3ySY0gsAvULEMc2bxOvZaDVm17sEek0o\nndBxiyFiINFtWHxcODqKDpGjm0jWHZG4vd6/DV7sKzzPWGyyI3lHEf5z0dDIo6i1YCDRLfk+34R9\n10yCskc7c2YdNY1EIsE7A0NcQqnS6kDKvmIsOV4OO9cptVr8WUu35J1jwtHR3cFyRIfwAnzkXt3Z\nmu8MDIHd7igwAAAUfUlEQVRcIsHqensgvnNMjyMFZqwd3A7tVTwn2dpwhERNdr7cigPXhaOjpCjO\nrKNbJ5FIsGBgCNJ+0RbyejsJZVw1IXF7Ab67YWr4weS3GEjUZDsvGwW3o4PluJuTGeg21OwQLpEA\nnye1d9lu6npV9WSHD05xV4fWhIFETXK+zCrYlQHg6Ig84+EOATjwRLjLNkM2BzD7+zLMOlgKi53n\nlVoDBhI1icvoKESOnhwdkQdsOVOJr6+a8EyPQKT207jcv+lMJZ7do4PJxlDydwwkcutcmRW55cLR\n0eMcHZGHyaQSzL0vBH8bHgp1vRNLGVdN+N2+YljsXmoctQgGErlVf3TUK0SOHp640hpRPVvOVKLQ\naMe0ezToohHOstt52YjXzyh5+M6PMZDopr65bsK5cp47opZ1l1qGPaPDXXYA2aeT40/fl3qpVdTc\nGEh0U0uOlwtu9+boiFrIjp+MeC46COEq4dfU5jNV2Hja/ey7mpl8ddc/kbgxkKhR395w3bOOoyNq\nSSFKKabdo3GZFv7K92X45jrXKfkbBhI1askx19FRd46OqIW1DZDi+Rg1NHUmOlgdwO/2FeOi3nqT\nR5KvYSBRgw7eMOGbeqOjxzg6Ii/pqJZh7N1BgrJikx3Pfq1DBafe+Q0GEjVoSb0dvTmzjrytX6gC\ng0OFI6KcEitezCzhhqx+goFELr7PN7nsWcfREYnBoHY2JIQJN/P94icjFv2ob+QR5EsYSOSi/ugo\nOliOnhwdkQhIJMBv7w5CJ7VwjdIyrR5rTnLfO1/HQCKBwwWu1zvi6IjEJEAmweR6kxwA4LXDZUg/\nyynevoyBRAL1R0cPdVByR28SnXYBUkzqo4ay3jfYjO9KsS6nAg6eU/JJDCRyOlJoxtdXhaOjVxKC\nvdQaopvr3kaOSTFqyOoMlBwAXv1fGaZklnAzVh/EQCKn+uuOegTLcL7M4qXWELnXu60Cv++thrTe\nRf625RnwnlaPUyV8//oSBhIBAI4WmpFRb3SU1FkFiUTSyCOIxCE2VIGJvdUIqHfF8wKDHetPVWJd\nTgWyixlMvoCBRACApfX2rOveRoZonjsiHxEbqsDLcW3Qp63re/Z0qRUPby/A7/bpkMMRk6gxkAjH\niszYdaXe6CiKoyPyLRGBMkzorcYvIpVo6J27/aIRD31egN/vK8bpUgaTGDGQyGVm3cBwpcu2/0S+\nIEAmwdieQZgVp0HPYJnL/Q4A/75owC/+XYDJB4pxlsEkKgykVu54kdnlAnyv9G/D0RH5tCiNHC/d\no8HkGDU6qxsOpk/zDHjw8wJMySxGXjk3aRUDBlIrt6jezLquGhl+4g7K5AckEsnP55Y0mNg7CHcF\nuX7d2R3AP88bMPCzfPzf96UoNNi80FKqwUBqxfZdNWI3zx2Rn5NIJIgLU2J2fBv8vncQOjYQTFYH\nsOFUJfp/mo8lx8u5g7iXMJBaKZsDeP2HMkFZj2AZYhqYpUTkD6QSCeLDlPi/+DaY0CsIHQJdv/4q\nrA68c0yPAf/Kx6bTlbDYubi2JTGQWqkv8mXIKREemhvTLZCjI/J7UokECe2V+FNCG/y/6CCEBbh+\nDRYY7Jh1qBSDPi/AF5cM3IqohTCQWiG9xY61l5SCsmd6BKKLhqMjaj2kEgnuDVdiTv82+FW3QKjl\nrj/GcsusGL+3GMlfFeGHAnMDz0KexEBqZRwOB2Z8VwqdpfbDp5IBb97LPeuodZJLJRhyVwDeGBCM\nkZ0CoGjgW/FQvhkjvyzE7/bpuOtDM2IgtTLrcirx2QWDoGzqPRpEcXRErVygXIJRXQPxev9gPBih\ndNkfD6heXPvw9gI8tbsIX181wsZzTB7VpEAym81YtGgRRowYgaSkJKSnpzda9+zZs5g4cSISExOR\nkpKCnJwcwf0ZGRn49a9/jcTERMyePRvFxcV31gNqskP5JrxZbyJDxyApIgNl2HKG15EhAoC2AVKM\nuzsI/xfXBn0bmeTz9VUTntqtQ+y2G3jzhzL8UGBmOHlAkwJp1apVyMrKwurVqzFnzhxs3rwZu3fv\ndqlnMBgwc+ZMxMbGIj09HQkJCUhNTUVlZfWXXU5ODubPn4+JEydi06ZNqKysxLx58zzaIXLlcDiw\n+XQlntqtg7XOZyZYIfl5U0pOZCCqr6Nahil9NZh6T8OLawHgepUdf8muwMgvC9Hz79cxYV8x1pys\nwOECEwxWBtStcnucxmAwYPv27XjvvffQp08f9OnTB+PHj8e2bdvw6KOPCupmZGRALpdj5syZkEql\nSE1NxXfffYeMjAyMGTMGn3zyCYYNG4bRo0cDAObNm4df/vKXuHz5MqKiopqnh61YqcmO/ddM2Jpb\niT31dvIGgLWJ7VBo5HoLopvpFaLArDg5sost2HfNhAv6hhfPlpod+PyiAZ9frD4kLgHQrY0Mvdoq\n0EUtQ4cgGToESdExqPrf7QKkUMslUMslkDV0fLAVchtIubm5MJvNiI+Pd5YlJCRg06ZNsNlskMlq\nfzlkZ2cjLi4OUmn1wEsikSAuLg5ZWVkYM2YMsrOzMX78eGf9yMhIdOzYEVlZWR4PpFKTHS9kCg8H\nNvR7pe5szsqqAKgvFLnUc/e4m9a73cc1ULGp7Soz21FssiPfYEdjRxEmRVkwqmsgD9URNYH058W1\ncWFKXNBbcfCGCVqdBaab/J5zALigtzUaYHWpZIBaLkWgXAKZBLBbVQjMyodMAkglgEwigUwKyCTV\nQSf5eftYyc+3a/5do26ZpE5ZzbIOSZ36wvvr/LuB53g0SoVJMRq3/bldbgOpqKgIwcHBCAgIcJaF\nhobCYrGgpKQE7du3F9Tt2rWr4PGhoaE4e/YsAECn0wnq19xfUFBwR51oSNsAKT4Z2d59xVZuQm+1\nt5vQvHr38HYLWgb7SX7A7Tkko9EIpVK4ZqXmttlsdqmrUChc6loslkafS6FQuDwPERG1Pm4DSalU\nugRGzW2VSiUoDwgIcIZP3bo19Rp6LovF4vI8RETU+rgNpIiICOj1ekHQ6HQ6KJVKBAcLF1OGh4dD\np9MJynQ6HcLCwm56f/3DeERE1Pq4DaRevXpBoVBAq9U6y06cOIGYmBjI5cJTULGxsdBqtc4T8g6H\nA1qtFv369XPef+LECWf9/Px83LhxA7GxsR7pDBER+S63gaRSqZCcnIylS5fi5MmTyMzMxNatWzF2\n7FgA1RMZjMbqC7wNHz4cBoMBy5YtQ15eHtLS0lBVVYWRI0cCAJ566ins2rULn3/+Oc6dO4d58+Zh\n0KBB6NKlSzN20b1bWfjrS65cuYJZs2ZhxIgRGD16NFasWAGTqXr69/Xr1zF9+nQMHjwYzzzzDA4e\nPOjl1nrGwoUL8eKLLzpv+1M/rVYr0tLSMHLkSDzyyCNYvHix8xC4P/UTAMrLy/HnP/8ZjzzyCEaN\nGoXVq1fDZquereYPfTWbzRg3bhwOHz7sLHPXryNHjuDZZ59FYmIiXnzxRVy+fLmlm93smrQwNjU1\nFX369MHUqVOxePFiTJ482RkyycnJ2LNnDwBAo9EgLS0NWq0WKSkp0Gq1SEtLg1pdPZMrLi4Or732\nGjZt2oRJkyZBo9Fg7ty5zdS1pmvqwl9fYrFY8PLLL0OpVOKDDz7A/PnzceDAAaxduxYOhwOzZ89G\nSEgItmzZglGjRuGVV17B1atXvd3sO3L48GFs377dedvf+rlq1Srs378fy5Ytw/Lly3Ho0CFs3LjR\n7/oJAEuXLkVBQQHWr1+Pt956C19++SU+/vhjv+iryWTCG2+8gby8PGeZu37l5+dj9uzZSEpKwocf\nfoiwsDDMnj0bdrt/rSOUlJaWturlxAaDAY8++ijee+89DBw4EACwceNGfP/999iwYYOXW3f7jh8/\njqlTp2LPnj0ICgoCAOzcuRMrVqzA22+/jVmzZmHnzp3OHwvTpk1DbGws/vCHP3iz2bfNYDDg2Wef\nRfv27SGTybBu3Tr88MMPftNPvV6PpKQkLF++HA888AAA4IsvvkBGRgbGjx/vN/2sMWzYMMydOxdD\nhw4FAKxYsQIXLlzw+b7m5eXhzTffhMPhwLlz57B69WoMHDjQ7Xt1/fr1OHLkiPM7yWg04vHHH8eS\nJUuc31v+oNVvrtrYwt+cnBznIQJf1LVrV6xYscIZRkD1oriKigpkZ2ejV69ezjc+AMTHxyMrK8sb\nTfWItWvXYsCAARgwYICzzJ/6efz4cahUKsGXz+jRo7Fy5Uq/6meNkJAQ7Ny5E0ajEYWFhTh06BBi\nYmJ8vq8//vgj7r33XmzatElQ7q5f2dnZ6N+/v/M+lUqF3r17+0y/m6rVB5K7hb++ql27doIvL7vd\njm3btuH+++9HUVERwsPDBfWba4FyS9Bqtfj6668xY8YMQbk/9fPq1avo0KEDdu3ahbFjx+KJJ57A\nypUrYbFY/KqfNV555RX8+OOPGDp0KEaNGoWwsDA8//zzPt/X3/zmN5g1a5bLUhd3/SoqKmqxTQW8\nqdVfc+BWFv76shUrVuDMmTPYsmULPv7445suYPYlZrMZCxcuRGpqqssyBHcLtX1JZWUlrl27hn/+\n85+YM2cOqqqqsGTJEthsNr/qZ43Lly+jd+/emDx5MiorK/Huu+9i1apVftlXgJsK1Gj1I6RbWfjr\nixwOB9577z1s27YNCxYsQM+ePd0uYPYlH3zwAaKiovDII4+43OdP/ZTL5aisrMT8+fORkJCAQYMG\n4Y9//CP+/e9/Q6FQ+E0/gerZoWlpaXjjjTcQHx+PQYMG4fXXX8e2bdv8rq81uKlAtVY/Qqq78Lfm\nF0pjC399jd1ux4IFC7Bz504sWrQIQ4YMAVC9QDk3N1dQt7i42LmA2Zfs2rULOp3O2TeLxQK73Y4h\nQ4ZgwoQJftPPmskanTt3dpZ17doVJpMJYWFhOH/+vKC+r/YTAE6dOgWNRoPIyEhnWUxMDGw2G9q3\nb+9Xfa3h7jMZERHR4KYCPXv2bLE2toRWP0K6lYW/vmbFihXYtWsXlixZgmHDhjnLY2NjcfbsWRgM\ntVeOPX78uE8uUF63bh3+/ve/Y+vWrdi6dSvGjBmDPn36YOvWrX7Vz379+sFms+HcuXPOsgsXLkCt\nVqNfv35+00+g+stZr9ejqKjIWXbx4kUA1SHsT32t4e69Wn9TAaPRiLNnz/p8v+tr9YHkbuGvr8rK\nysI//vEPTJkyBX369EFRUZHzb8CAAejQoQPeeustnD9/Hh9++CGys7MxZswYbzf7lnXs2BFRUVHO\nvzZt2iAgIABRUVF+1c8uXbpg8ODBmD9/Pk6dOoVjx45h9erVePLJJ3H//ff7TT+B6i/fnj17Yu7c\nucjNzUVWVhYWLVqExx9/HMOHD/ervtZw91795S9/iZMnT2LTpk3Iy8vDggULEBkZifvvv9/LLfes\nVr8OCaj+tbF48WLs27cParUazz33HJ577jlvN+uOrFy5En/7298avO/gwYO4fv06FixYgJMnT6JT\np05ITU3Fgw8+2MKt9Ly1a9fixIkTWLduHYDqk+P+0s/KykosX74ce/fuhUwmw6hRo/DSSy9BoVD4\nVT8BoLCwEMuXL8eRI0egUCgwfPhwvPTSS1CpVH7T14EDBzrXIQHu36sHDx5EWlqac7u1119/XXAI\n1x8wkIiISBRa/SE7IiISBwYSERGJAgOJiIhEgYFERESiwEAiIiJRYCAREZEoMJCIiEgUGEhERCQK\nDCQiIhIF3949lEiEjEYjNm7ciL179+LGjRtQKpW45557MH36dPTu3RsA8O233+L999/HhQsXEBER\ngSlTpmD9+vVISkrClClTAADl5eVYs2YN9u/fj4qKCvTo0QMvvPACHnroIW92j6jZcOsgIg979dVX\ncezYMUybNg2dO3fG5cuXsX79egQGBuLTTz/F0aNHMX36dDz88MP41a9+hcuXL2PdunUwm8343e9+\nhylTpsBsNmPSpEkoKCjACy+8gA4dOmDHjh3Ys2cP3n33XSQmJnq7m0QexxESkQdZLBYYDAbMmjUL\njz32GIDqnZwrKiqwcuVKFBQU4P3330f37t2xdOlSSCQSANWXnH/jjTecz7Njxw6cOXMG69evR//+\n/QEAgwYNcj4PA4n8Ec8hEXmQQqHAypUr8dhjj6GgoABHjhzBZ599hm+//RZA9eG8rKwsDB061BlG\nADBixAjIZDLn7SNHjqBt27bo168frFar8y8xMRE//fQTrl+/3uJ9I2puHCERedihQ4eQlpaGixcv\nQq1W4+6770ZQUBCA6quA2mw2tGvXTvAYmUyGkJAQ5+3S0lKUlpZi0KBBDb5GYWEhOnbs2HydIPIC\nBhKRB125cgV/+tOf8PDDD2P58uXo1KkTJBIJPv30Uxw6dAihoaGQy+UoLi4WPM5ut6OsrMx5W6PR\noFOnTli4cGGDr9O1a9dm7QeRN/CQHZEHnTp1CiaTCSkpKejcubPzsNzBgweddeLj43HgwAE4HLXz\niTIzM2Gz2Zy3BwwYgIKCArRt2xZ9+/Z1/mm1WmzevBlSKT+65H9kr7766jxvN4LIXyiVSvzrX//C\ntWvXEBoaigsXLmDNmjX45ptvAABPPPEE4uLisHXrVpw7dw5qtRo//PADVqxYAaPRiPvuuw8DBgxA\njx49sGfPHuzYsQNqtRp6vR5ffPEF1q1bh9jYWIwYMcLLPSXyPE77JvKwr7/+Ghs2bMDVq1cRHByM\n2NhYjB07Fn/4wx8wa9YsjB07Fvv378f777+PS5cuoWPHjnjhhRfw+uuvY8aMGXjuuecAACUlJfjr\nX/+Kb7/9FhUVFYiMjMSoUaOQkpICuZxH28n/MJCIWlhmZiYiIiIQExPjLMvLy8O4ceOwbNkyDB48\n2IutI/Ie/swiamHff/89du/ejenTp6NLly4oLCzE5s2b0a1bNzzwwAPebh6R13CERNTCjEYj1q9f\nj71796KoqAjBwcEYNGgQpk6dirCwMG83j8hrGEhERCQKnDtKRESiwEAiIiJRYCAREZEoMJCIiEgU\nGEhERCQKDCQiIhKF/w9lIPFQ/JMlMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a238898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( data[\"age\"],bins=100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of Age in Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean         52.295207\n",
       "std          14.771866\n",
       "min           0.000000\n",
       "25%          41.000000\n",
       "50%          52.000000\n",
       "75%          63.000000\n",
       "max         109.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing Age. The minimum age is 0 and maximum is 109\"\"\"\n",
    "data.age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'age should be between a defined range, 0-109 makes less sense, should be between 22-91'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"age should be between a defined range, 0-109 makes less sense, should be between 22-91\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Median and Mean Ages\"\"\"\n",
    "np.median(data.age)\n",
    "#np.mean(data.age)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Removing Outlier \": Replacing age younger than 22 and  greater than 91 with mean of age\"\"\"\n",
    "median_age=np.median(data.age)\n",
    "ageNew=[]\n",
    "for val in data.age:\n",
    "    if val < 22 or val >91:\n",
    "        ageNew.append(median_age)\n",
    "    else:\n",
    "        ageNew.append(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'New Age after treatment'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"New Age after treatment\"\"\"\n",
    "#ageNew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Saving new age back to the original mage column\"\"\"\n",
    "data.age = ageNew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117a665f8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4xTh1qPnXhaJHSP2mfSKAj0+a4Wz89ETUAO9aAWhziRWv6oySMrkA3J8cjDsS\ng5hpgTqcVinDsqERkrJSixtbz7LrjprHSQ0d6Epmu31zzobcszZJmVoGTE8JQTIXuJIfua2HBjfF\nqiSb9+04a8PAKCUStbz1kDe2kALIgQo7vmi050ykWsCsa7UMRuSXJidpEKasb7G7AXxQYIalcYp5\nIjAgBYyiWif+ftIsKQtRCPh0bAwSQ/m0Sf4pWCHDvY2WHOhtbnx8ygxRZFAiKQakAFBjd2NtgUky\nm04pA9aPjsL1sarm30jkB9KjlPhVvPT39LC+LtcdUUMMSH7O6RaxtsDktaPrqmGRGNVd08y7iPzL\n5KQgyaw7APjrvhrs/JmTHKgeA5Kf+/dPFhQbpOmAxnZX4+4+zLxAgUMpE/DH5GCoG8QkhxuYtvMC\nDjSY9EBdGwOSH9tWYsXu89I/1gERCtyRyJYRBZ7YIDnu6yd9kDI7RdyTW4nzZu8cjK3VXrstU9tj\nQPJTFRYXZn1XJSmLVAvI6h/MdUYUsAZFq3BXUpCkrMom4vV8I4qZyaHLY0BqB5f7BCeKIh7/rgoV\nVrenTAbgj8khCFHyn4wC24gENW7voZaUmZ0i3jxihE7P7ruujHc3P7SmwIytjRa/3t5Tg96c3k2d\nxB09NXi40T5JDnfd7/72s1afTAlnV17gYUDyMxesbjyzv0ZSlhQqx9hGT5REgUwQBCwZEo55maGS\nchHApjNWrC0ww+riOqWuplUByW63Y9GiRRgzZgzGjx+PdevWNVv3xIkTePDBBzFs2DBkZWXh6NGj\nTdZbvXo1nnnmmSs76k5KFEX885QZxgar2NUy4A/9gyHnuBF1MoIgYN51YVjxqwivG9HhCw7k6Awo\nu4rJDhR4WhWQVqxYgby8PKxcuRLz58/HmjVrsG3bNq96FosFc+bMQXp6OtatW4fMzExkZ2fDZJI2\nmbdu3Yp3333XN2fQifxQbkdBjXRg987eQYjRyJt5B1Hgy0oOwSNpIQhWSB+6yixuvJZn4LhSF9Ji\nQLJYLNi4cSOys7ORmpqKESNGYNq0adiwYYNX3dzcXCgUCsyZMwdJSUnIzs6GVqtFbm4uAMDpdGLx\n4sV48cUX0b17d9+fTQCrsrmxsVGeumHdVLglnpkYqPMbEKHE/8vQonujxbM2F7C6wIz/nLFc1bgS\nx5MCQ4sBqbCwEHa7HYMGDfKUZWZm4ujRo3C5pM3p/Px8ZGRkQCar+1hBEJCRkYG8vDwAdcHt5MmT\nWL16NQaYNrCRAAAaRUlEQVQOHOjL8whoF7vqrA0uZ7BCwOu3RnKKN3UZ0Ro5ZqdrcUOsd6LgrWdt\neHh3FaxMytqptRiQKisrERYWBrW6flA9KioKDocDVVVVXnVjY2MlZVFRUSgvLwcAhIaG4r333kP/\n/v19ceydxr4KO45XS7vqFlwfxll11OWo5AL+0C8Yv00KgqzRs9inRRb8Zmsl9FaOK3VWLQYkq9UK\nlUrabXTxtd1u96qrVCq96jocjqs9zk6r2ubGF8XSrrqh8So8lBrSzDuIOjdBEDAsQY1Z12oR0mhc\n6f/K7Ri/uRIXGqzRuxrsyvMvLT6Cq1Qqr8Bz8bVGI01ho1arvYKP3W73qnc5CgsLr/i9/vJzysrr\n+8ULZfVPd6Vlcnx6XgGrq/77CkHEkz1qcOpktdd7G2r4Oc3VuZL6ral3uXUAOcrKy676+Pytjr8c\nXyBe39bU0wL4n+7AhnNK6B31z86FNU68drgGU69xIFYt7cK7muOTXtNG72mn+1BXcKkeshYDUlxc\nHAwGAxwOh6f1o9froVKpEBYWJqkbGxsLvV4vKdPr9YiOjr6S4wZw6YP3lcLCwjb9OfHu+qev/v3r\nWz7nyqtw0izd42hSryCMzujR5Hsbavg5zdW5kvqtqXe5dVBahPi4+Ks+Pn+r4y/HF4jXt7X14gH8\nv3g31hw3o7C2vlvb6BLw0Tk1Hk4NQZ+w+tvY1Ryf5Jo20Nb3B6rXYpddcnIylEoldDqdp+zw4cNI\nSUmBQiGNZ+np6dDpdJ7ZMKIoQqfTcQJDEy5YXfi8SNpV1ztUjuEJXABL1FCwQoZH0kIwubc0B57F\nJeKto0bkX+CQQGfRYkDSaDSYMGEClixZgiNHjmD37t1Yv349pkyZAqBuIoPVWrenyejRo2GxWLB0\n6VIUFRUhJycHZrMZY8eObduzCEBP7a+VLICVC8DUvkycStQUhUzA+yMim0w39P5xE34oszXzztbj\neFLHa9XC2ItrkGbOnInFixfjoYce8gSZCRMmYPv27QAArVaLnJwc6HQ6ZGVlQafTIScnByEhXW+A\n/lK/3F//bMU/Gm1HfnsPDboFcwEsUXPksrp0Q09d551u6ONTFuT6KAcedZxWzSvWaDRYsGABFixY\n4PW9ffv2SV6npaXhww8/bPEzn3322dYdYSdjcrgxe2+1pCwhWIYx3dlVR9QSQRDwv5lhOFnrxCen\nLGgYfr46Y8X8fTVYdFM4exoCFJOrtrNFBw04Y6yfzSMAmNI3GIrGiy6IqFm3xKvxwIBgNJoVjreP\nmjBjdxXsTMwakBiQ2tEZgxNvHTVKyoYlqLgAlugKZESr8FiaFo1TPX5aZMGU7XpmCw9ADEjtxOUW\n8Y9TZrgb/I1EqgVMTAxq/k1EdEl9wxX4U3oowpTSptLX52x4I9/okwW0n5fKOdmhnTAgtZOd52w4\nb5b+cdzbJxhqObvqiK7GNSFyzB6oRaxGejsrMbnwymFmCw8kDEjtoMziwtYSq6Ts3r5BSI30TiJJ\nRJcvWiPHn9K1yIyW/k1ZXCJWF5ix4RQ3/AsEDEhXqaW1C+5fMnk3TFIcrZbhpZvC2+kIibqGUJUM\nX94Rg/E9vVOV7Smz4+VDtSio5iJaf8aA1Ma+L7OjqFaaI2vxzeGI5qZ7RD4XqpThH2Oi8OKNYV7Z\nwqtsIt46asKf9lShxu6b5KzkWwxIbaja5sa/f5KmB7q9hxq/68OJDERtRRAEPJ4eitnpWsRovG9x\n606YccsXZdhSYmni3dSRGJDaiCiK+LTIDFuDxpFKBrx6SwQELtojanO9QhX486BQjExQo/Ff3Dmz\nG1O3X8D6QhNMjta3lpheqG0xILWRw3oH8qukm+5N6hWEnlquOSJqLyq5gMlJQZg9UIv4IO/b3YEK\nBxYf4kw8f8GA1AaqbG58WuydyfvWbqpm3kFEbal3qAJPDgrF2O5qNF5pYXDUzcRbW2CC8TJaS+R7\nDEht4G/7a2B0SDN5T2Emb6IOpZQJmNgrCDsmxeLaSO+eikN6B146yNZSR2JA8rFd56z4qFCayXts\nDw0SmMmbyC9kxqjw9a/jMC8z1GsmnslZ11rK3lsFs5OtpfbGgORDZqcbcxpl8u4WJMNtzORN5FdU\ncgHzrgvDkxmh6BHi/bC4psCMUf+u4LqldsaA5EMvHTTgtKFRJu9+zORN5K+uCZEjO0OLiYkar7Gl\nghonxnxZgQJj07dJzrjzPU75ugJN/QKWGJ1444g0k/et3VRIYiZvIr8mFwSM7aFBSoQC606YUdEg\nIavRKeLzUiWMCgvu6Knhko02xhaSD7hFERuKLJJM3hGqugFUIgoMPbV1M/FujPXOMbntrA0fnTTD\n6WY+vLbEgOQDP5TZJZvuAcC9fYOhYSZvooCilgu4r18wlg4J99r870CFA+8eMzFJaxtiQLpKRocb\nm85IM3n/upcGaczkTRSQBEHAQ6labLojxiv1UEGNEyvzjahlLrw2wYB0lb78yQpzg1TewQqBmbyJ\nOoEh8WrkToxFhFLaIjprcmFZnhFlFlcz76QrxYB0FYprnfi/cukiur9khqIH0wMRdQpJYQpkdbcj\nUSudGn7B5sbyPCOKDc5m3klXggHpCrlEERuKpAtgB4Qr8FiatoOOiIjaQogCmHWtFmkR0gdNs1PE\nm0eMyL/AtUq+woB0hb4rteNcoy3Jl94SARUnMhB1Omq5gOmpIbg5TpqP0uEG3j9uwprj9UtBuD7p\nyrFv6QrU2N34zxlp8tTrY5QYlsCMDESdlVwQMLVvECJUAraetXnKRQDZ31fjWLUDCxuNHzcMSvcP\nCGmxvKtjC+kKfPmTBdYG45kaOXBnb645IursBEHAHYlBuLdPkNceS+8cM+HurZXMGH4VGJAu048V\ndhyokPYZ35EYhHAVLyVRVzG0mxrTU0KgbPRn/22pHUsOGXCsiuNKV4J30VZaW2DCmuNGPPTNBUl5\nQrCM+xwRdUHpUUrMTtciUiVtK9U6RKw6ZsKGU2ZYnFxEezk4hnQZDusdKDZI1x5M7h0EOfNbEXVJ\nPbQKzM0IxX9KrNhbJl0CsqfMjsN6B+5I1GBIfPMPrRxPqscWUis53SK+/EmakeHaSAUGRDAjA1FX\nFqqSYeP4GDw5yHt/JaOzLs/l4oMGrDthgo1phy6JAamVdp+3QW+rH6yUAbiTyVOJCHW70f5tcBi2\nTPBONwQAFVY3/rSnGhkbSvHSwVpU2zjxoSnssmuFSqsL285KW0e/6qZCPHeBJaIGbopT438HheLr\nczbs/NmKxinvyixuvHzIAAF1PSxDu6mREqGArJlu/67WnceA1AovHzR4TfMe11PTcQdERH5LLRcw\nvqcGQ+JU2FxixYEKOxrvWiECyK9yIr/KiUiVgFvi1bj5EuNMXQUDUgsKqh1Y3WjF9bieGmgbz/ck\nImogQi3Dff2CcUdPDXads+FAhR2mJmbdVdlFbC6xYkuJFfvL7XggJQSjrlF7tZq6QmuJAakFz+yv\nQcNxyBiNDMO6MSMDEbVOpFqGu5KC8OHoKGwoMmNNgbnJ/HduAJvOWLHpjBW9Q+X4Y3IIFELdpImu\nggHpEnLPWiUpQoC6vY4UjafSEBG1IEwlw/QULR4cEILnfqzF3lI7DurtaCqxw2mDC8/9WAu5AAyM\nUmJovAr9wpsea+pMLScGpGbYXCL+8kO1pKxPmBwZUZzmTURXThAE9A5VoHeoApOTNDhQ4cDeUhtK\nLd6RySUCh/QOHNI7EKORYWi8yivBa2fCgNSMN44YUdRgEawA4K7eQRC4CJaIfCRYIcPwBDWGdVOh\n2OBCqcWFjactsDWx91+l1Y1//1Q31nTG6MKsdC0SO9nea12nc/IylBidWHrYICkbGq9Cz072j09E\n/kEQBPQJU+Cd4VE4dm83LLwpHP3Dm77f2N3AqmMmXPdpGWZ8cwE/mzrPzrW8wzYiiiLm/V+NZFvy\nKLUMExI5zZuI2l6URo5Z12oxMy0ET++vxd4yGw7rHWic5MElAp8U1W2DkxqhwMROcI9iQGrko5Nm\nfHVGugj2mevDOuhoiKirEgQB/cIV6BeugNHhxg9ldnxbakON3Xvq+LFqJ45VG/H1ORsmJGoQrZEH\n5AQHdtk1UFTrxF9+qJGUDY5R4n/6B3fQERERAVqlDLf10ODpwWH4fd8gDGimO+/HSgcWHTTg82Iz\n9NbA68pjQPqFwy1ixu4LkoVrwQoBq4ZHQs5p3kTkBxQyATfHq/H9XXH4x5goDGlixp1LBHaft+Pa\nT0oxbYce7x4zdsCRXhkGJABuEZizt9pr472FN4ajfzineRORf5H9snPtfybEYEZqCBKCvW/lVlfd\nQttFB2vxWZEZouj/mca7fEASRREvn1Lio0KzpPyOnhrcP4BddUTkvwRBQFqkEv87KBR/6BfstVkg\nAFTZREz/pgq3f1WBfeW2Jj7Ff7QqINntdixatAhjxozB+PHjsW7dumbrnjhxAg8++CCGDRuGrKws\nHD16VPL93Nxc3H333Rg2bBiefPJJXLhwoZlPansWp4jsvdX4vFTaCuoRIsfrt0ZwzRERBQSZIODG\nOBX+OjgMv+mtQbDC+961v8KB27+qxLQdevxYYW/iUzpeqwLSihUrkJeXh5UrV2L+/PlYs2YNtm3b\n5lXPYrFgzpw5SE9Px7p165CZmYns7GyYTHWpLY4ePYrnn38eDz74IFavXg2TyYQFCxb49IRa65tz\nNgz9VxnWnpC2jLoFybBxXAxiNNxagogCi1ImYNQ1Gjx1XShGJKghb+KZetMZK8ZsqsCk/1Tgk1Nm\nGJrKXdRBWpz2bbFYsHHjRrz66qtITU1Famoqpk2bhg0bNuD222+X1M3NzYVCocCcOXMgk8mQnZ2N\nPXv2IDc3F5MnT8Y///lPjBo1CpMmTQIALFiwAL/+9a9RUlKCnj17ts0Z/qLW7kZRrRNfn7PhX6ct\nOKz3Tm4Yq6nb+bFvMzNYiIgCQYiyLqHrrd1U+PInK3RNJHP9rtSO70rt0MiBEddocFOsCjfEKtEv\nXIluQbIOmczV4p23sLAQdrsdgwYN8pRlZmZi9erVcLlckMvrWxL5+fnIyMiATFbX8BIEARkZGcjL\ny8PkyZORn5+PadOmeerHx8cjISEBeXl5Pg9IxbVOPLDrAowOEdV2Nyqtl34K6BEixz9vi+aW5ETU\nacQGyfFgSgj6hSvw9P4aHKz0DkxWF7C1xIqtJfXrL+VC3c4GQQoBGrkAtbzuv+N6ajA3I7TNjleo\nrq6+5NSLnTt34qWXXkJubq6nrLi4GFOmTMHmzZsRExPjKZ87dy569eqF2bNne8pef/11nDhxAq+/\n/jpGjhyJF154AcOGDfN8/4EHHsCIESNw//33+/C0iIgo0LQ4hmS1WqFSSee6X3xtt9u96iqVSq+6\nDoej2c9SKpVen0NERF1PiwFJpVJ5BYyLrzUaae4ktVrtCT4N616s19RnORwOr88hIqKup8WAFBcX\nB4PBIAk0er0eKpUKYWHSHG+xsbHQ6/WSMr1ej+jo6Et+v2G3HxERdU0tBqTk5GQolUrodDpP2eHD\nh5GSkgKFQjonIj09HTqdzrMiWBRF6HQ6DBw40PP9w4cPe+qXlZWhtLQU6enpPjkZIiIKXC0GJI1G\ngwkTJmDJkiU4cuQIdu/ejfXr12PKlCkAgMrKSlitdbMzRo8eDYvFgqVLl6KoqAg5OTkwm80YO3Ys\nAOC3v/0ttm7din/96184efIkFixYgKFDhyIxMbENT7He2bNnMXfuXIwZMwaTJk3CsmXLYLPVrVw+\nf/48nnjiCQwfPhz33nsv9u7d2y7H1BktXLgQjz76qOc1r61vOJ1O5OTkYOzYsbjtttuwePFiTxc4\nr/HVq62txTPPPIPbbrsNEydOxMqVK+Fy1SUo5fVtH/J58+YtaKnSjTfeiIKCArzxxhvYt28f7r//\nfkyePBkAMHLkSPTs2RPJyclQqVS4/vrr8dlnn2HNmjVwOBx44YUXcM011wCom+YdHx+P9957D598\n8gmSkpLw9NNPIygoqE1PEqgbq3r00UcRExOD559/HrfccgvWrVuHiooK3HzzzXj00UfRrVs3PPfc\nc1Cr1ViyZAnGjRvn1S1Jl7Zv3z7k5OQgISEBkyZNgiiKvLY+smzZMnzzzTd46aWXPBlTDAYDbrjh\nBl5jH3jhhRdQUVGBl156CUOGDMGqVasAABkZGby+7aTFad+dxaFDhzBz5kxs374dwcF1Oeq2bNmC\nZcuW4YUXXsDcuXOxZcsWhITU7SEya9YspKen47HHHuvIww4oFosF9913H2JiYiCXy/H2229j//79\nvLY+YDAYMH78eLz22mu4+eabAQCbNm1Cbm4upk2bxmvsA6NGjcKzzz6LkSNHAqh7ACguLub1bUdd\nJrlqr169sGzZMk8wAuoW7hqNRuTn5yM5OdnzywYAgwYNQl5eXkccasB66623MHjwYAwePNhTxmvr\nG4cOHYJGo8FNN93kKZs0aRKWL1/Oa+wj4eHh2LJlC6xWKyoqKvD9998jJSWF17cddZmAFBkZKflj\ndrvd2LBhA2688UZUVlYiNjZWUj8qKgrl5eXtfZgBS6fTYceOHZJF0QB4bX3k559/Rrdu3bB161ZM\nmTIFd955J5YvXw6Hw8Fr7CN/+ctf8N///hcjR47ExIkTER0djYcffpjXtx112aRty5YtQ0FBAdau\nXYu///3vl1zQS5dmt9uxcOFCZGdne/Wpt7RYmlrHZDLh3Llz+OSTTzB//nyYzWa8/PLLcLlcvMY+\nUlJSggEDBuChhx6CyWTCK6+8ghUrVvD6tqMu00K6SBRFvPrqq9iwYQNefPFF9O3bt8UFvXRp7733\nHnr27InbbrvN63u8tr6hUChgMpnw/PPPIzMzE0OHDsWf/vQnfPHFF1AqlbzGV+ns2bPIycnB3/72\nNwwaNAhDhw7FU089hQ0bNvD6tqMu1UJyu9148cUXsWXLFixatAgjRowAULdgt7CwUFL3woULngW9\ndGlbt26FXq/3XE+HwwG32+3JUchre/UuThTp0aOHp6xXr16w2WyIjo7GqVOnJPV5jS/PsWPHoNVq\nER8f7ylLSUmBy+VCTEwMr2876VItpGXLlmHr1q14+eWXMWrUKE95eno6Tpw4AYvF4ik7dOgQF+y2\n0ttvv41//OMfWL9+PdavX4/JkycjNTUV69ev57X1kYEDB8LlcuHkyZOesuLiYoSEhGDgwIG8xlcp\nNjYWBoMBlZWVnrLTp08DqAv8vL7to8sEpLy8PHz88ceYMWMGUlNTUVlZ6fkaPHiwZ43BqVOn8MEH\nHyA/P9+z1oouLSEhAT179vR8hYaGQq1Wo2fPnry2PpKYmIjhw4fj+eefx7Fjx3Dw4EGsXLkSv/nN\nb3DjjTfyGl+l9PR09O3bF88++ywKCwuRl5eHRYsW4Y477sDo0aN5fdtJl1mHtHz5cnz00UdNfm/v\n3r04f/48XnzxRRw5cgTdu3dHdnY2hgwZ0s5H2Tm89dZbOHz4MN5++20AdYPFvLZXz2Qy4bXXXsPO\nnTshl8sxceJEPP7441AqlbzGPlBRUYHXXnsNBw4cgFKpxOjRo/H4449Do9Hw+raTLhOQiIjIv3WZ\nLjsiIvJvDEhEROQXGJCIiMgvMCAREZFfYEAiIiK/wIBERER+gQGJiIj8AgMSERH5BQYkIiLyC10q\n2zdRe7BarXj//fexc+dOlJaWQqVS4dprr8UTTzyBAQMGAAC+++47vPPOOyguLkZcXBxmzJiBVatW\nYfz48ZgxYwYAoLa2Fm+++SZ27doFo9GIPn364JFHHsGvfvWrjjw9ojbD1EFEPjZv3jwcPHgQs2bN\nQo8ePVBSUoJVq1YhKCgIn376KX788Uc88cQTuPXWW3HXXXehpKQEb7/9Nux2O/74xz9ixowZsNvt\nmD59OsrLy/HII4+gW7du2Lx5M7Zv345XXnkFw4YN6+jTJPI5tpCIfMjhcMBisWDu3LkYN24cAGDw\n4MEwGo1Yvnw5ysvL8c477yApKQlLliyBIAgAgMjISPztb3/zfM7mzZtRUFCAVatW4brrrgMADB06\n1PM5DEjUGXEMiciHlEolli9fjnHjxqG8vBwHDhzA559/ju+++w5AXXdeXl4eRo4c6QlGADBmzBjI\n5XLP6wMHDiAiIgIDBw6E0+n0fA0bNgxnzpzB+fPn2/3ciNoaW0hEPvb9998jJycHp0+fRkhICPr1\n64fg4GAAdTuNulwuREZGSt4jl8sRHh7ueV1dXY3q6moMHTq0yZ9RUVGBhISEtjsJog7AgETkQ2fP\nnsWf//xn3HrrrXjttdfQvXt3CIKATz/9FN9//z2ioqKgUChw4cIFyfvcbjdqamo8r7VaLbp3746F\nCxc2+XN69erVpudB1BHYZUfkQ8eOHYPNZkNWVhZ69Ojh6Zbbu3evp86gQYPwzTffQBTr5xPt3r0b\nLpfL83rw4MEoLy9HREQE0tLSPF86nQ5r1qyBTMY/Xep85PPmzVvQ0QdB1FmoVCp89tlnOHfuHKKi\nolBcXIw333wT3377LQDgzjvvREZGBtavX4+TJ08iJCQE+/fvx7Jly2C1WnHDDTdg8ODB6NOnD7Zv\n347NmzcjJCQEBoMBmzZtwttvv4309HSMGTOmg8+UyPc47ZvIx3bs2IF3330XP//8M8LCwpCeno4p\nU6bgsccew9y5czFlyhTs2rUL77zzDn766SckJCTgkUcewVNPPYXZs2fjD3/4AwCgqqoKb7zxBr77\n7jsYjUbEx8dj4sSJyMrKgkLB3nbqfBiQiNrZ7t27ERcXh5SUFE9ZUVERpk6diqVLl2L48OEdeHRE\nHYePWUTt7IcffsC2bdvwxBNPIDExERUVFVizZg169+6Nm2++uaMPj6jDsIVE1M6sVitWrVqFnTt3\norKyEmFhYRg6dChmzpyJ6Ojojj48og7DgERERH6Bc0eJiMgvMCAREZFfYEAiIiK/wIBERER+gQGJ\niIj8AgMSERH5hf8PXb2BO7IEczUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a1d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Histogram of Age after removing Outliers\"\"\"\n",
    "sns.distplot( data[\"age\"],bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RevolvingUtilizationOfUnsecuredLines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'RevolvingUtilizationOfUnsecuredLines = Total balance on credit cards and personal lines of credit \\nexcept real estate and no installment debt like car loans divided by the sum of credit limits'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"RevolvingUtilizationOfUnsecuredLines = Total balance on credit cards and personal lines of credit \n",
    "except real estate and no installment debt like car loans divided by the sum of credit limits\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          6.048438\n",
       "std         249.755371\n",
       "min           0.000000\n",
       "25%           0.029867\n",
       "50%           0.154181\n",
       "75%           0.559046\n",
       "max       50708.000000\n",
       "Name: RevolvingUtilizationOfUnsecuredLines, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing the RevolvingUtilizationOfUnsecuredLines\"\"\"\n",
    "data.RevolvingUtilizationOfUnsecuredLines.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3321"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" # of RevolvingUtilizationOfUnsecuredLines greater than 1\"\"\"\n",
    "len(data[data.RevolvingUtilizationOfUnsecuredLines >1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'the value here should be between 0-1 [implies 0 to 100%], but few values are more than 1 [implying more than 100%], \\nso all those values must be a data entry error and should be changed to the value/100'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"the value here should be between 0-1 [implies 0 to 100%], but few values are more than 1 [implying more than 100%], \n",
    "so all those values must be a data entry error and should be changed to the value/100\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Capping RevolvingUtilizationOfUnsecuredLines greater than 1 with 1\"\"\"\n",
    "for val in data.RevolvingUtilizationOfUnsecuredLines:\n",
    "    if val >1:\n",
    "        data['RUUL_indicator']=1\n",
    "    else:\n",
    "        data['RUUL_indicator']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RUULNew=[]\n",
    "\n",
    "for val in data.RevolvingUtilizationOfUnsecuredLines:\n",
    "    if val <=10:\n",
    "        RUULNew.append(val/10)\n",
    "    elif val >10 and val <=100:\n",
    "        RUULNew.append(val/100)\n",
    "    elif val >100 and val <=1000:\n",
    "        RUULNew.append(val/1000)\n",
    "    elif val >1000 and val <=10000:\n",
    "        RUULNew.append(val/10000)\n",
    "    elif val >10000 and val <=100000:   \n",
    "        RUULNew.append(val/100000)\n",
    "    else:\n",
    "        RUULNew.append(val)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.RevolvingUtilizationOfUnsecuredLines = RUULNew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11916cac8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cplZVF4GIqNqxeUSjqKgICoVhc6vktlKptPXliYjIidkcMgqFwiRMSm67ubnZ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\nxw3EuPtKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11898e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Histogram of RevolvingUtilizationOfUnsecuredLines after removing Outliers\"\"\"\n",
    "\n",
    "sns.distplot( data[\"RevolvingUtilizationOfUnsecuredLines\"],bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NumberOfTime3059DaysPastDueNotWorse'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"NumberOfTime3059DaysPastDueNotWorse\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          0.421033\n",
       "std           4.192781\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           0.000000\n",
       "max          98.000000\n",
       "Name: NumberOfTime3059DaysPastDueNotWorse, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing NumberOfTime3059DaysPastDueNotWorse\"\"\"\n",
    "data.NumberOfTime3059DaysPastDueNotWorse.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1183699e8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IVAweIiISVbWCRyaTISgoCIMHD4arqyt27typ8TG//fYb3N3dX3qARERUt+hWp1NERASS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\nomLwEBGRqBg8REQkqv8Ds6h/1MzF6zgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1186509e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Histogram of NumberOfTime3059DaysPastDueNotWorse after removing Outliers\"\"\"\n",
    "\n",
    "\n",
    "sns.distplot( data[\"NumberOfTime3059DaysPastDueNotWorse\"],bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks 96 and 98 are outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Removing outliers : Replacing value at 96 and 98 with mean\"\"\"\n",
    "New = []\n",
    "medianNOTT = data.NumberOfTime3059DaysPastDueNotWorse.median()\n",
    "for val in data.NumberOfTime3059DaysPastDueNotWorse:\n",
    "    if ((val == 98) | (val == 96)):\n",
    "        New.append(medianNOTT)\n",
    "    else:\n",
    "        New.append(val)\n",
    "\n",
    "data.NumberOfTime3059DaysPastDueNotWorse = New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'DebtRatio'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"DebtRatio\"\"\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean        353.005076\n",
       "std        2037.818523\n",
       "min           0.000000\n",
       "25%           0.175074\n",
       "50%           0.366508\n",
       "75%           0.868254\n",
       "max      329664.000000\n",
       "Name: DebtRatio, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing Debt Ratio\"\"\"\n",
    "data.DebtRatio.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35137"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Debt ratio shoule be between 0 and 1\"\"\"\n",
    "len(data[data.DebtRatio > 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145887"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[data.DebtRatio >0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Median of Debt ratio : Replacing value greater than 1 nwith median\"\"\"\n",
    "New = []\n",
    "medianNOTT = data.DebtRatio.median()\n",
    "for val in data.NumberRealEstateLoansOrLines:\n",
    "    if val>1:\n",
    "        New.append(medianNOTT)\n",
    "    else:\n",
    "        New.append(val)\n",
    "\n",
    "data.DebtRatio = New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NumberOfOpenCreditLinesAndLoans'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"NumberOfOpenCreditLinesAndLoans\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          8.452760\n",
       "std           5.145951\n",
       "min           0.000000\n",
       "25%           5.000000\n",
       "50%           8.000000\n",
       "75%          11.000000\n",
       "max          58.000000\n",
       "Name: NumberOfOpenCreditLinesAndLoans, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing NumberOfOpenCreditLinesAndLoans\"\"\"\n",
    "data.NumberOfOpenCreditLinesAndLoans.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bKSoqIiTkWn7Onz+f1tZWCgoKcLvdpKens3r1at/8OTk5bNmyhZUrVxIREcHy5cuZN29e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\naAoyERExNAWZiIgY2v8HM06j0nPNkPUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1183b1ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Histogram of NumberOfOpenCreditLinesAndLoans : Checking Distribution\"\"\"\n",
    "data['NumberOfOpenCreditLinesAndLoans'].hist(bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NumberOfTimes90DaysLate'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"NumberOfTimes90DaysLate\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          0.265973\n",
       "std           4.169304\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           0.000000\n",
       "max          98.000000\n",
       "Name: NumberOfTimes90DaysLate, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing NumberOfTimes90DaysLate\"\"\"\n",
    "data.NumberOfTimes90DaysLate.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Removing Outlier : Replacing value at 98 and 96 with mean\"\"\"\n",
    "New = []\n",
    "medianNOTT = data.NumberOfTimes90DaysLate.median()\n",
    "for val in data.NumberOfTimes90DaysLate:\n",
    "    if ((val == 98) | (val == 96)):\n",
    "        New.append(medianNOTT)\n",
    "    else:\n",
    "        New.append(val)\n",
    "\n",
    "data.NumberOfTimes90DaysLate = New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NumberRealEstateLoansOrLines'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"NumberRealEstateLoansOrLines\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          1.018240\n",
       "std           1.129771\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           1.000000\n",
       "75%           2.000000\n",
       "max          54.000000\n",
       "Name: NumberRealEstateLoansOrLines, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing NumberRealEstateLoansOrLines\"\"\"\n",
    "data.NumberRealEstateLoansOrLines.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Removing Outlier : Replacing value greater than with 50 with mean\"\"\"\n",
    "New = []\n",
    "medianNOTT = data.NumberRealEstateLoansOrLines.median()\n",
    "for val in data.NumberRealEstateLoansOrLines:\n",
    "    if val>50:\n",
    "        New.append(medianNOTT)\n",
    "    else:\n",
    "        New.append(val)\n",
    "\n",
    "data.NumberRealEstateLoansOrLines = New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          1.017887\n",
       "std           1.121458\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           1.000000\n",
       "75%           2.000000\n",
       "max          32.000000\n",
       "Name: NumberRealEstateLoansOrLines, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing NumberRealEstateLoansOrLines\"\"\"\n",
    "data.NumberRealEstateLoansOrLines.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NumberOfTime6089DaysPastDueNotWorse'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"NumberOfTime6089DaysPastDueNotWorse\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          0.240387\n",
       "std           4.155179\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           0.000000\n",
       "max          98.000000\n",
       "Name: NumberOfTime6089DaysPastDueNotWorse, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing NumberOfTime6089DaysPastDueNotWorse\"\"\"\n",
    "data.NumberOfTime6089DaysPastDueNotWorse.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Removing Outlier : Replacing value at 98 and 96 with mean\"\"\"\n",
    "New = []\n",
    "medianNOTT = data.NumberOfTime6089DaysPastDueNotWorse.median()\n",
    "for val in data.NumberOfTime6089DaysPastDueNotWorse:\n",
    "    if ((val == 98) | (val == 96)):\n",
    "        New.append(medianNOTT)\n",
    "    else:\n",
    "        New.append(val)\n",
    "\n",
    "data.NumberOfTime6089DaysPastDueNotWorse = New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          0.064707\n",
       "std           0.329788\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           0.000000\n",
       "max          11.000000\n",
       "Name: NumberOfTime6089DaysPastDueNotWorse, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.NumberOfTime6089DaysPastDueNotWorse.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NumberOfDependents'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"NumberOfDependents\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    146076.000000\n",
       "mean          0.757222\n",
       "std           1.115086\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           1.000000\n",
       "max          20.000000\n",
       "Name: NumberOfDependents, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing NumberOfDependents\"\"\"\n",
    "data.NumberOfDependents.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'having more than 10 dependents looks weird'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"having more than 10 dependents looks weird\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Removing more than 10 dependents with median\"\"\"\n",
    "New = []\n",
    "medianNOTT = data.NumberOfDependents.median()\n",
    "for val in data.NumberOfDependents:\n",
    "    if val>10:\n",
    "        New.append(medianNOTT)\n",
    "    else:\n",
    "        New.append(val)\n",
    "\n",
    "data.NumberOfDependents = New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3924"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Checking null values in NumberOfDependents\"\"\"\n",
    "data.NumberOfDependents.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Replacing null with 0 for NumberOfDependents\"\"\"\n",
    "data['NumberOfDependents'] = data['NumberOfDependents'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          0.737193\n",
       "std           1.105454\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           1.000000\n",
       "max          10.000000\n",
       "Name: NumberOfDependents, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.NumberOfDependents.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MonthlyIncome'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"MonthlyIncome\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.202690e+05\n",
       "mean     6.670221e+03\n",
       "std      1.438467e+04\n",
       "min      0.000000e+00\n",
       "25%      3.400000e+03\n",
       "50%      5.400000e+03\n",
       "75%      8.249000e+03\n",
       "max      3.008750e+06\n",
       "Name: MonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Describing Monthly Income\"\"\"\n",
    "data.MonthlyIncome.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Using Linear Regression to predict the missing values in Monthly Income'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Using Linear Regression to predict the missing values in Monthly Income\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Splitting into train and test where monthly income is not null and null\"\"\"\n",
    "train = data[data.MonthlyIncome.isnull() == False]\n",
    "test = data[data.MonthlyIncome.isnull() == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((120269, 13), (29731, 13))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Dimension of train and test\"\"\"\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Making final X_train, y_train and X_test to predict. y_train is the dependent variable \"\"\"\n",
    "X_train = train.drop(['MonthlyIncome', 'SeriousDlqin2yrs'], axis=1)\n",
    "y_train = train.MonthlyIncome\n",
    "X_test = test.drop(['MonthlyIncome', 'SeriousDlqin2yrs'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Making Model\"\"\"\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hswamy/anaconda3/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  warnings.warn(mesg, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Fitting the Model\"\"\"\n",
    "lmMod = LinearRegression(fit_intercept=True, normalize=True).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3961.33740175,    32.03621524,  -102.68003447,   -72.09238452,\n",
       "         108.74675887,  -144.60250509,  1261.76661631,  -151.79776075,\n",
       "         720.53795628,     0.        ,     0.        ])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Model coefficients\"\"\"\n",
    "lmMod.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"predicting on test set which are the missing values\"\"\"\n",
    "pred = lmMod.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predNoZero = []\n",
    "for val in pred:\n",
    "    if val >= 0:\n",
    "        predNoZero.append(val)\n",
    "    else:\n",
    "        predNoZero.append(0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testFull = data[data.MonthlyIncome.isnull() == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hswamy/anaconda3/lib/python3.6/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": [
    "testFull['MonthlyIncome'] = predNoZero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "monNew = []\n",
    "for index in data.index:\n",
    "    if data.MonthlyIncome[index].is_integer() == True:\n",
    "        monNew.append(data.MonthlyIncome[index])\n",
    "    else:\n",
    "        monNew.append(testFull.MonthlyIncome[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.MonthlyIncome = monNew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Checking null values in Monthly Income\"\"\"\n",
    "testFull.MonthlyIncome.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.500000e+05\n",
       "mean     6.546697e+03\n",
       "std      1.290913e+04\n",
       "min      0.000000e+00\n",
       "25%      3.733000e+03\n",
       "50%      5.523671e+03\n",
       "75%      7.869197e+03\n",
       "max      3.008750e+06\n",
       "Name: MonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.MonthlyIncome.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x11889e710>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x11863fda0>,\n",
       "  <matplotlib.lines.Line2D at 0x118e65748>],\n",
       " 'fliers': [],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x118379358>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x11889e3c8>,\n",
       "  <matplotlib.lines.Line2D at 0x11889e208>]}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nnqG1tZX58+fHz82bN49f//rX9Pb2snTpUtLT0+Oz4uJivF4vAL29vaxcuTI+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\nqB7+cB8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11baadf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WITHOUT OUTLIERS\n",
    "\n",
    "plt.boxplot(data.MonthlyIncome, sym='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x11901b518>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x11902e6a0>,\n",
       "  <matplotlib.lines.Line2D at 0x11902eb00>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1190372e8>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x119037358>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x1190252b0>,\n",
       "  <matplotlib.lines.Line2D at 0x119025e48>]}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qyMrKIiUlhcTERAoLCzl9+jR3796lv7+fkydPkpubS0JCAqmpqaxevZpjx44BjLm3iIiY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e8a6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WITH OUTLIER...\n",
    "\n",
    "plt.boxplot(data.MonthlyIncome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_subset_by_IQR(df,column):\n",
    "   # q1 = df[column].quantile(0.01)\n",
    "    q3 = df[column].quantile(0.99)\n",
    "    iqr = (df[column] < q3)\n",
    "    return df.loc[iqr]\n",
    "\n",
    "data = get_subset_by_IQR(data,'MonthlyIncome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x1192d8e48>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1192e1828>,\n",
       "  <matplotlib.lines.Line2D at 0x1192ebef0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1192f2eb8>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1192f21d0>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x1192d8390>,\n",
       "  <matplotlib.lines.Line2D at 0x1192e1b70>]}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZliW32y3pfnAejk0oFJLH47Fj9CB2/z13uVyKi4sbceyD7QfX/7BAIPD0JwMYhPs6XgQZ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\nRMAAAEYiYAAAI/0fuBGkKEiy6zkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118eaa1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AFTER REMOVING OUTLIERS....\n",
    "\n",
    "plt.boxplot(data.MonthlyIncome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    148499.000000\n",
       "mean       6101.379304\n",
       "std        3543.210474\n",
       "min           0.000000\n",
       "25%        3700.000000\n",
       "50%        5500.000000\n",
       "75%        7754.000000\n",
       "max       23033.000000\n",
       "Name: MonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.MonthlyIncome.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148499, 13)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "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>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime3059DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime6089DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>NoIncome_MI_indicator</th>\n",
       "      <th>RUUL_indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.076613</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.095715</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.065818</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.023381</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.021318</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines   age  \\\n",
       "0                 1                              0.076613  45.0   \n",
       "1                 0                              0.095715  40.0   \n",
       "2                 0                              0.065818  38.0   \n",
       "3                 0                              0.023381  30.0   \n",
       "5                 0                              0.021318  74.0   \n",
       "\n",
       "   NumberOfTime3059DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                  2.0   0.366508         9120.0   \n",
       "1                                  0.0   0.000000         2600.0   \n",
       "2                                  1.0   0.000000         3042.0   \n",
       "3                                  0.0   0.000000         3300.0   \n",
       "5                                  0.0   1.000000         3500.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                      0.0   \n",
       "1                                4                      0.0   \n",
       "2                                2                      1.0   \n",
       "3                                5                      0.0   \n",
       "5                                3                      0.0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime6089DaysPastDueNotWorse  \\\n",
       "0                           6.0                                  0.0   \n",
       "1                           0.0                                  0.0   \n",
       "2                           0.0                                  0.0   \n",
       "3                           0.0                                  0.0   \n",
       "5                           1.0                                  0.0   \n",
       "\n",
       "   NumberOfDependents  NoIncome_MI_indicator  RUUL_indicator  \n",
       "0                 2.0                      0               0  \n",
       "1                 1.0                      0               0  \n",
       "2                 0.0                      0               0  \n",
       "3                 0.0                      0               0  \n",
       "5                 1.0                      0               0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    138555\n",
       "1      9944\n",
       "Name: SeriousDlqin2yrs, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SeriousDlqin2yrs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SeriousDlqin2yrs', 'RevolvingUtilizationOfUnsecuredLines', 'age',\n",
       "       'NumberOfTime3059DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTime6089DaysPastDueNotWorse',\n",
       "       'NumberOfDependents', 'NoIncome_MI_indicator', 'RUUL_indicator'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_variables = ['SeriousDlqin2yrs', 'RevolvingUtilizationOfUnsecuredLines', 'age',\n",
    "       'NumberOfTime3059DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
    "       'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
    "       'NumberRealEstateLoansOrLines', 'NumberOfTime6089DaysPastDueNotWorse',\n",
    "       'NumberOfDependents', 'RUUL_indicator']\n",
    "\n",
    "loans_data_relevent = data[model_variables]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X: (148499, 11)\n",
      "Shape of y: (148499, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hswamy/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X = np.array(loans_data_relevent.ix[:, loans_data_relevent.columns != 'SeriousDlqin2yrs'])\n",
    "y = np.array(loans_data_relevent.ix[:, loans_data_relevent.columns == 'SeriousDlqin2yrs'])\n",
    "print('Shape of X: {}'.format(X.shape))\n",
    "print('Shape of y: {}'.format(y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '1': [9944]\n",
      "Before OverSampling, counts of label '0': [138555] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hswamy/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After OverSampling, the shape of train_X: (277110, 11)\n",
      "After OverSampling, the shape of train_y: (277110,) \n",
      "\n",
      "After OverSampling, counts of label '1': 138555\n",
      "After OverSampling, counts of label '0': 138555\n"
     ]
    }
   ],
   "source": [
    "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y==1)))\n",
    "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y==0)))\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "sm = SMOTE(random_state=2,ratio=1)\n",
    "X_train_res, y_train_res = sm.fit_sample(X, y.ravel())\n",
    "\n",
    "print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))\n",
    "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_res.shape))\n",
    "\n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res==1)))\n",
    "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res==0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train_feature = pd.DataFrame(X_train_res)\n",
    "y_train_target = pd.DataFrame(y_train_res)\n",
    "Somte_data = pd.concat([x_train_feature, y_train_target], axis=1, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "         0     1    2         3       4     5    6    7    8    9    10  11\n",
       "0  0.076613  45.0  2.0  0.366508  9120.0  13.0  0.0  6.0  0.0  2.0  0.0   1\n",
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     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "Somte_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oldNames = [0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11] \n",
    "newNames = ['RevolvingUtilizationOfUnsecuredLines', 'age',\n",
    "       'NumberOfTime3059DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
    "       'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
    "       'NumberRealEstateLoansOrLines', 'NumberOfTime6089DaysPastDueNotWorse',\n",
    "       'NumberOfDependents', 'RUUL_indicator','SeriousDlqin2yrs']\n",
    "Somte_data.rename(columns=dict(zip(oldNames,newNames)), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>NumberOfTime3059DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
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      ],
      "text/plain": [
       "   RevolvingUtilizationOfUnsecuredLines   age  \\\n",
       "0                              0.076613  45.0   \n",
       "1                              0.095715  40.0   \n",
       "2                              0.065818  38.0   \n",
       "3                              0.023381  30.0   \n",
       "4                              0.021318  74.0   \n",
       "\n",
       "   NumberOfTime3059DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                  2.0   0.366508         9120.0   \n",
       "1                                  0.0   0.000000         2600.0   \n",
       "2                                  1.0   0.000000         3042.0   \n",
       "3                                  0.0   0.000000         3300.0   \n",
       "4                                  0.0   1.000000         3500.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                             13.0                      0.0   \n",
       "1                              4.0                      0.0   \n",
       "2                              2.0                      1.0   \n",
       "3                              5.0                      0.0   \n",
       "4                              3.0                      0.0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime6089DaysPastDueNotWorse  \\\n",
       "0                           6.0                                  0.0   \n",
       "1                           0.0                                  0.0   \n",
       "2                           0.0                                  0.0   \n",
       "3                           0.0                                  0.0   \n",
       "4                           1.0                                  0.0   \n",
       "\n",
       "   NumberOfDependents  RUUL_indicator  SeriousDlqin2yrs  \n",
       "0                 2.0             0.0                 1  \n",
       "1                 1.0             0.0                 0  \n",
       "2                 0.0             0.0                 0  \n",
       "3                 0.0             0.0                 0  \n",
       "4                 1.0             0.0                 0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Somte_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data =  Somte_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base_data = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(277110, 12)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(277110, 12)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def feature_eng(df):\n",
    "    for val in df.MonthlyIncome:\n",
    "        if val <=0:\n",
    "            df['NoIncome_MI_indicator']=1\n",
    "        else:\n",
    "            df['NoIncome_MI_indicator']=0\n",
    "    \n",
    "            \n",
    "    for val in df.DebtRatio:\n",
    "        if val <=0:\n",
    "            df['No_DebtRatio_indicator']=1\n",
    "        else:\n",
    "            df['No_DebtRatio_indicator']=0\n",
    "            \n",
    "    \n",
    "\n",
    "    df['MIz_DRnz']=list(map(lambda x,y: 1 if (x==0 and y>0) else 0, df['MonthlyIncome'], df['DebtRatio']))\n",
    "    df['MIz_DRz']=list(map(lambda x,y: 1 if (x==0 and y==0) else 0, df['MonthlyIncome'], df['DebtRatio']))\n",
    "    df['MInz_DRz']=list(map(lambda x,y: 1 if (x>0 and y==0) else 0, df['MonthlyIncome'], df['DebtRatio']))\n",
    "    \n",
    "    \n",
    "    \n",
    "    for val in df.RevolvingUtilizationOfUnsecuredLines:\n",
    "        if val <=0:\n",
    "            df['ZeroRevolvingUtilization']=1\n",
    "        else:\n",
    "            df['ZeroRevolvingUtilization']=0\n",
    "            \n",
    "   \n",
    "                      \n",
    "    for val in df.MonthlyIncome:\n",
    "        if val ==0:\n",
    "            MIZ=1\n",
    "        else:\n",
    "            MIZ=val\n",
    "            \n",
    "    \n",
    "            \n",
    "    df['DR_MI']=MIZ*df['DebtRatio']\n",
    "    \n",
    "    df['Log_DR_MI']=np.log(df.DR_MI)\n",
    "    \n",
    "    df['Log_DR_MI']=df.Log_DR_MI.replace([np.inf, -np.inf], 0)\n",
    "    df['RevolvingLines']=df['NumberOfOpenCreditLinesAndLoans']-df['NumberRealEstateLoansOrLines']\n",
    "    \n",
    "   \n",
    "    \n",
    "    HRSL=[]\n",
    "    for val in df.NumberRealEstateLoansOrLines:\n",
    "        if val >0:\n",
    "            HRSL.append(1)\n",
    "        else:\n",
    "            HRSL.append(0)\n",
    "            \n",
    "    \n",
    "        \n",
    "    df['HasRealEstateLoans']=HRSL\n",
    "    \n",
    "    MHRSL=[]\n",
    "    for val in df.NumberRealEstateLoansOrLines:\n",
    "        if val >2:\n",
    "            MHRSL.append(1)\n",
    "        else:\n",
    "            MHRSL.append(0)\n",
    "            \n",
    "    \n",
    "        \n",
    "    df['HasMultipleRealEstateLoans ']=MHRSL\n",
    "    df['DisposableIncome']=(1-df['DebtRatio'])*df['MonthlyIncome']\n",
    "    df['RevolvingToRealEstate']=df['RevolvingLines'] / (1+df['NumberRealEstateLoansOrLines'])\n",
    "    \n",
    "    \n",
    "    \n",
    "    FU=[]\n",
    "    for val in df.RevolvingUtilizationOfUnsecuredLines:\n",
    "        if val ==1:\n",
    "            FU.append(1)\n",
    "        else:\n",
    "            FU.append(0)\n",
    "        \n",
    "        \n",
    "    df['FullUtilization']=FU\n",
    "    \n",
    "   \n",
    "    \n",
    "    EU=[]\n",
    "    for val in df.RevolvingUtilizationOfUnsecuredLines:\n",
    "        if val >1:\n",
    "            EU.append(1)\n",
    "        else:\n",
    "            EU.append(0)\n",
    "        \n",
    "    df['ExcessUtilization']=EU\n",
    "    \n",
    "    \n",
    "    \n",
    "    df['RevolvingLinesPerPerson'] = df['RevolvingLines'] / (1+df['NumberOfDependents'])\n",
    "    df['RealEstateLoanPerPerson'] = df['NumberRealEstateLoansOrLines'] / (1+df['NumberOfDependents'])\n",
    "    \n",
    "    df['IncomePerDependent']=(1+df['MonthlyIncome']) / (1+df['NumberOfDependents'])\n",
    "    \n",
    "    df['NumberOfTimePastDue']=df['NumberOfTime3059DaysPastDueNotWorse']+df['NumberOfTime6089DaysPastDueNotWorse']+df['NumberOfTimes90DaysLate']\n",
    "    df['DelinquenciesPerLine']=df['NumberOfTimePastDue'] /df['NumberOfOpenCreditLinesAndLoans']\n",
    "    \n",
    "    \n",
    "    df['DelinquenciesPerLine']=df.DelinquenciesPerLine.replace([np.inf, -np.inf], np.NaN)\n",
    "    df.DelinquenciesPerLine[np.isnan(df.DelinquenciesPerLine)] = 0\n",
    "    df.DelinquenciesPerLine[np.isinf(df.DelinquenciesPerLine)] = 0\n",
    "    df['DelinquenciesPerLine']=df['DelinquenciesPerLine'].fillna(0)\n",
    "    \n",
    "    df['DelinquenciesPerLine']=df.DelinquenciesPerLine.replace([np.inf, -np.inf], 0)\n",
    "    df['DelinquenciesPerRevolvingLine'] = df['NumberOfTimePastDue'] / df['RevolvingLines']\n",
    "    \n",
    "    df['DelinquenciesPerRevolvingLine']=df.DelinquenciesPerRevolvingLine.replace([np.inf, -np.inf], np.NaN)\n",
    "    df.DelinquenciesPerRevolvingLine[np.isnan(df.DelinquenciesPerRevolvingLine)] = 0\n",
    "    df.DelinquenciesPerRevolvingLine[np.isinf(df.DelinquenciesPerRevolvingLine)] = 0\n",
    "    df['DelinquenciesPerRevolvingLine']=df['DelinquenciesPerRevolvingLine'].fillna(0)\n",
    "    \n",
    "    \n",
    "    \n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hswamy/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:41: RuntimeWarning: divide by zero encountered in log\n",
      "/Users/hswamy/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:107: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/hswamy/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:108: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time was 313.223 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hswamy/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:115: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/hswamy/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:116: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import time\n",
    "import timeit\n",
    "start_time = time.time()\n",
    "\n",
    "feature_data= feature_eng(data)\n",
    "\n",
    "end_time = time.time()\n",
    "print(\"Elapsed time was %g seconds\" % (end_time - start_time))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_data = feature_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime3059DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime6089DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>RUUL_indicator</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>NoIncome_MI_indicator</th>\n",
       "      <th>No_DebtRatio_indicator</th>\n",
       "      <th>MIz_DRnz</th>\n",
       "      <th>MIz_DRz</th>\n",
       "      <th>MInz_DRz</th>\n",
       "      <th>ZeroRevolvingUtilization</th>\n",
       "      <th>DR_MI</th>\n",
       "      <th>Log_DR_MI</th>\n",
       "      <th>RevolvingLines</th>\n",
       "      <th>HasRealEstateLoans</th>\n",
       "      <th>HasMultipleRealEstateLoans</th>\n",
       "      <th>DisposableIncome</th>\n",
       "      <th>RevolvingToRealEstate</th>\n",
       "      <th>FullUtilization</th>\n",
       "      <th>ExcessUtilization</th>\n",
       "      <th>RevolvingLinesPerPerson</th>\n",
       "      <th>RealEstateLoanPerPerson</th>\n",
       "      <th>IncomePerDependent</th>\n",
       "      <th>NumberOfTimePastDue</th>\n",
       "      <th>DelinquenciesPerLine</th>\n",
       "      <th>DelinquenciesPerRevolvingLine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.076613</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1136.787317</td>\n",
       "      <td>7.035961</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5777.44849</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.095715</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2600.00000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1300.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.065818</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "   RevolvingUtilizationOfUnsecuredLines   age  \\\n",
       "0                              0.076613  45.0   \n",
       "1                              0.095715  40.0   \n",
       "2                              0.065818  38.0   \n",
       "3                              0.023381  30.0   \n",
       "4                              0.021318  74.0   \n",
       "\n",
       "   NumberOfTime3059DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                  2.0   0.366508         9120.0   \n",
       "1                                  0.0   0.000000         2600.0   \n",
       "2                                  1.0   0.000000         3042.0   \n",
       "3                                  0.0   0.000000         3300.0   \n",
       "4                                  0.0   1.000000         3500.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                             13.0                      0.0   \n",
       "1                              4.0                      0.0   \n",
       "2                              2.0                      1.0   \n",
       "3                              5.0                      0.0   \n",
       "4                              3.0                      0.0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime6089DaysPastDueNotWorse  \\\n",
       "0                           6.0                                  0.0   \n",
       "1                           0.0                                  0.0   \n",
       "2                           0.0                                  0.0   \n",
       "3                           0.0                                  0.0   \n",
       "4                           1.0                                  0.0   \n",
       "\n",
       "   NumberOfDependents  RUUL_indicator  SeriousDlqin2yrs  \\\n",
       "0                 2.0             0.0                 1   \n",
       "1                 1.0             0.0                 0   \n",
       "2                 0.0             0.0                 0   \n",
       "3                 0.0             0.0                 0   \n",
       "4                 1.0             0.0                 0   \n",
       "\n",
       "   NoIncome_MI_indicator  No_DebtRatio_indicator  MIz_DRnz  MIz_DRz  MInz_DRz  \\\n",
       "0                      0                       1         0        0         0   \n",
       "1                      0                       1         0        0         1   \n",
       "2                      0                       1         0        0         1   \n",
       "3                      0                       1         0        0         1   \n",
       "4                      0                       1         0        0         0   \n",
       "\n",
       "   ZeroRevolvingUtilization        DR_MI  Log_DR_MI  RevolvingLines  \\\n",
       "0                         0  1136.787317   7.035961             7.0   \n",
       "1                         0     0.000000   0.000000             4.0   \n",
       "2                         0     0.000000   0.000000             2.0   \n",
       "3                         0     0.000000   0.000000             5.0   \n",
       "4                         0  3101.672570   8.039697             2.0   \n",
       "\n",
       "   HasRealEstateLoans  HasMultipleRealEstateLoans   DisposableIncome  \\\n",
       "0                   1                            1        5777.44849   \n",
       "1                   0                            0        2600.00000   \n",
       "2                   0                            0        3042.00000   \n",
       "3                   0                            0        3300.00000   \n",
       "4                   1                            0           0.00000   \n",
       "\n",
       "   RevolvingToRealEstate  FullUtilization  ExcessUtilization  \\\n",
       "0                    1.0                0                  0   \n",
       "1                    4.0                0                  0   \n",
       "2                    2.0                0                  0   \n",
       "3                    5.0                0                  0   \n",
       "4                    1.0                0                  0   \n",
       "\n",
       "   RevolvingLinesPerPerson  RealEstateLoanPerPerson  IncomePerDependent  \\\n",
       "0                 2.333333                      2.0         3040.333333   \n",
       "1                 2.000000                      0.0         1300.500000   \n",
       "2                 2.000000                      0.0         3043.000000   \n",
       "3                 5.000000                      0.0         3301.000000   \n",
       "4                 1.000000                      0.5         1750.500000   \n",
       "\n",
       "   NumberOfTimePastDue  DelinquenciesPerLine  DelinquenciesPerRevolvingLine  \n",
       "0                  2.0              0.153846                       0.285714  \n",
       "1                  0.0              0.000000                       0.000000  \n",
       "2                  2.0              1.000000                       1.000000  \n",
       "3                  0.0              0.000000                       0.000000  \n",
       "4                  0.0              0.000000                       0.000000  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RevolvingUtilizationOfUnsecuredLines', 'age',\n",
       "       'NumberOfTime3059DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTime6089DaysPastDueNotWorse',\n",
       "       'NumberOfDependents', 'RUUL_indicator', 'SeriousDlqin2yrs',\n",
       "       'NoIncome_MI_indicator', 'No_DebtRatio_indicator', 'MIz_DRnz',\n",
       "       'MIz_DRz', 'MInz_DRz', 'ZeroRevolvingUtilization', 'DR_MI', 'Log_DR_MI',\n",
       "       'RevolvingLines', 'HasRealEstateLoans', 'HasMultipleRealEstateLoans ',\n",
       "       'DisposableIncome', 'RevolvingToRealEstate', 'FullUtilization',\n",
       "       'ExcessUtilization', 'RevolvingLinesPerPerson',\n",
       "       'RealEstateLoanPerPerson', 'IncomePerDependent', 'NumberOfTimePastDue',\n",
       "       'DelinquenciesPerLine', 'DelinquenciesPerRevolvingLine'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RevolvingUtilizationOfUnsecuredLines    float64\n",
       "age                                     float64\n",
       "NumberOfTime3059DaysPastDueNotWorse     float64\n",
       "DebtRatio                               float64\n",
       "MonthlyIncome                           float64\n",
       "NumberOfOpenCreditLinesAndLoans         float64\n",
       "NumberOfTimes90DaysLate                 float64\n",
       "NumberRealEstateLoansOrLines            float64\n",
       "NumberOfTime6089DaysPastDueNotWorse     float64\n",
       "NumberOfDependents                      float64\n",
       "RUUL_indicator                          float64\n",
       "SeriousDlqin2yrs                          int64\n",
       "NoIncome_MI_indicator                     int64\n",
       "No_DebtRatio_indicator                    int64\n",
       "MIz_DRnz                                  int64\n",
       "MIz_DRz                                   int64\n",
       "MInz_DRz                                  int64\n",
       "ZeroRevolvingUtilization                  int64\n",
       "DR_MI                                   float64\n",
       "Log_DR_MI                               float64\n",
       "RevolvingLines                          float64\n",
       "HasRealEstateLoans                        int64\n",
       "HasMultipleRealEstateLoans                int64\n",
       "DisposableIncome                        float64\n",
       "RevolvingToRealEstate                   float64\n",
       "FullUtilization                           int64\n",
       "ExcessUtilization                         int64\n",
       "RevolvingLinesPerPerson                 float64\n",
       "RealEstateLoanPerPerson                 float64\n",
       "IncomePerDependent                      float64\n",
       "NumberOfTimePastDue                     float64\n",
       "DelinquenciesPerLine                    float64\n",
       "DelinquenciesPerRevolvingLine           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RANDOM FOREST ALGO-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "feature_data.insert(0, 'New_ID_1', range(110, 110 + len(feature_data)))\n",
    "\n",
    "# calculate the time to run a GridSearchCV for multiple numbers of parameter permutations.  \n",
    "grid_times = {}\n",
    "clf = RandomForestClassifier(random_state = 84)\n",
    "\n",
    "X_total = feature_data.drop(['New_ID_1','SeriousDlqin2yrs'], axis=1)\n",
    "Y_total = feature_data['SeriousDlqin2yrs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime3059DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime6089DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>RUUL_indicator</th>\n",
       "      <th>NoIncome_MI_indicator</th>\n",
       "      <th>No_DebtRatio_indicator</th>\n",
       "      <th>MIz_DRnz</th>\n",
       "      <th>MIz_DRz</th>\n",
       "      <th>MInz_DRz</th>\n",
       "      <th>ZeroRevolvingUtilization</th>\n",
       "      <th>DR_MI</th>\n",
       "      <th>Log_DR_MI</th>\n",
       "      <th>RevolvingLines</th>\n",
       "      <th>HasRealEstateLoans</th>\n",
       "      <th>HasMultipleRealEstateLoans</th>\n",
       "      <th>DisposableIncome</th>\n",
       "      <th>RevolvingToRealEstate</th>\n",
       "      <th>FullUtilization</th>\n",
       "      <th>ExcessUtilization</th>\n",
       "      <th>RevolvingLinesPerPerson</th>\n",
       "      <th>RealEstateLoanPerPerson</th>\n",
       "      <th>IncomePerDependent</th>\n",
       "      <th>NumberOfTimePastDue</th>\n",
       "      <th>DelinquenciesPerLine</th>\n",
       "      <th>DelinquenciesPerRevolvingLine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.076613</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1136.787317</td>\n",
       "      <td>7.035961</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5777.44849</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3040.333333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.095715</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2600.00000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1300.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.065818</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3042.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3043.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.023381</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3300.00000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3301.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.021318</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3101.672570</td>\n",
       "      <td>8.039697</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1750.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RevolvingUtilizationOfUnsecuredLines   age  \\\n",
       "0                              0.076613  45.0   \n",
       "1                              0.095715  40.0   \n",
       "2                              0.065818  38.0   \n",
       "3                              0.023381  30.0   \n",
       "4                              0.021318  74.0   \n",
       "\n",
       "   NumberOfTime3059DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                  2.0   0.366508         9120.0   \n",
       "1                                  0.0   0.000000         2600.0   \n",
       "2                                  1.0   0.000000         3042.0   \n",
       "3                                  0.0   0.000000         3300.0   \n",
       "4                                  0.0   1.000000         3500.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                             13.0                      0.0   \n",
       "1                              4.0                      0.0   \n",
       "2                              2.0                      1.0   \n",
       "3                              5.0                      0.0   \n",
       "4                              3.0                      0.0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime6089DaysPastDueNotWorse  \\\n",
       "0                           6.0                                  0.0   \n",
       "1                           0.0                                  0.0   \n",
       "2                           0.0                                  0.0   \n",
       "3                           0.0                                  0.0   \n",
       "4                           1.0                                  0.0   \n",
       "\n",
       "   NumberOfDependents  RUUL_indicator  NoIncome_MI_indicator  \\\n",
       "0                 2.0             0.0                      0   \n",
       "1                 1.0             0.0                      0   \n",
       "2                 0.0             0.0                      0   \n",
       "3                 0.0             0.0                      0   \n",
       "4                 1.0             0.0                      0   \n",
       "\n",
       "   No_DebtRatio_indicator  MIz_DRnz  MIz_DRz  MInz_DRz  \\\n",
       "0                       1         0        0         0   \n",
       "1                       1         0        0         1   \n",
       "2                       1         0        0         1   \n",
       "3                       1         0        0         1   \n",
       "4                       1         0        0         0   \n",
       "\n",
       "   ZeroRevolvingUtilization        DR_MI  Log_DR_MI  RevolvingLines  \\\n",
       "0                         0  1136.787317   7.035961             7.0   \n",
       "1                         0     0.000000   0.000000             4.0   \n",
       "2                         0     0.000000   0.000000             2.0   \n",
       "3                         0     0.000000   0.000000             5.0   \n",
       "4                         0  3101.672570   8.039697             2.0   \n",
       "\n",
       "   HasRealEstateLoans  HasMultipleRealEstateLoans   DisposableIncome  \\\n",
       "0                   1                            1        5777.44849   \n",
       "1                   0                            0        2600.00000   \n",
       "2                   0                            0        3042.00000   \n",
       "3                   0                            0        3300.00000   \n",
       "4                   1                            0           0.00000   \n",
       "\n",
       "   RevolvingToRealEstate  FullUtilization  ExcessUtilization  \\\n",
       "0                    1.0                0                  0   \n",
       "1                    4.0                0                  0   \n",
       "2                    2.0                0                  0   \n",
       "3                    5.0                0                  0   \n",
       "4                    1.0                0                  0   \n",
       "\n",
       "   RevolvingLinesPerPerson  RealEstateLoanPerPerson  IncomePerDependent  \\\n",
       "0                 2.333333                      2.0         3040.333333   \n",
       "1                 2.000000                      0.0         1300.500000   \n",
       "2                 2.000000                      0.0         3043.000000   \n",
       "3                 5.000000                      0.0         3301.000000   \n",
       "4                 1.000000                      0.5         1750.500000   \n",
       "\n",
       "   NumberOfTimePastDue  DelinquenciesPerLine  DelinquenciesPerRevolvingLine  \n",
       "0                  2.0              0.153846                       0.285714  \n",
       "1                  0.0              0.000000                       0.000000  \n",
       "2                  2.0              1.000000                       1.000000  \n",
       "3                  0.0              0.000000                       0.000000  \n",
       "4                  0.0              0.000000                       0.000000  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_total.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HYPERPARAMETER TUNNING....."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kGyEqhc7GBhQKuP9v0TKFAl3ROsX81dWubdZYFenp6briVqSmpjJ06FAaNmzI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\niOQdhZuIiOSdXxA3gvxablq/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fcb3dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X_total, Y_total, test_size=0.25)\n",
    "\n",
    "n_estimators = [1, 2, 4, 8, 16, 32, 64, 100, 200]\n",
    "train_results = []\n",
    "test_results = []\n",
    "for estimator in n_estimators:\n",
    "    rf = RandomForestClassifier(n_estimators=estimator, n_jobs=-1)\n",
    "    rf.fit(x_train, y_train)\n",
    "    train_pred = rf.predict(x_train)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    train_results.append(roc_auc)\n",
    "    y_pred = rf.predict(x_test)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    test_results.append(roc_auc)\n",
    "from matplotlib.legend_handler import HandlerLine2D\n",
    "line1, = plt.plot(n_estimators, train_results, 'b', label='Train_AUC')\n",
    "line2, = plt.plot(n_estimators, test_results, 'r', label='Test_AUC')\n",
    "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
    "plt.ylabel(\"AUC_score\")\n",
    "plt.xlabel(\"n_estimators\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uesp5+rSZESNakJFROPD16ZPOuHGnqFPHzu+//5WoKIQtX07dxYsxlDBNweHj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\nkp8QQohW538B33L+1Xxk0O4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1212c2eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_depths = np.linspace(1, 60, 60, endpoint=True)\n",
    "train_results = []\n",
    "test_results = []\n",
    "for max_depth in max_depths:\n",
    "    rf = RandomForestClassifier(max_depth=max_depth, n_jobs=-1)\n",
    "    rf.fit(x_train, y_train)\n",
    "    train_pred = rf.predict(x_train)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    train_results.append(roc_auc)\n",
    "    y_pred = rf.predict(x_test)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    test_results.append(roc_auc)\n",
    "from matplotlib.legend_handler import HandlerLine2D\n",
    "line1, = plt.plot(max_depths, train_results, 'b', label='Train_AUC')\n",
    "line2, = plt.plot(max_depths, test_results, 'r', label='Test_AUC')\n",
    "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
    "plt.ylabel(\"AUC_score\")\n",
    "plt.xlabel(\"Tree_depth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y7PrabkFXjjWZ2m7iziLJTQhhMu/raru5V+Vy4fDNa4YJYW2S3IQQJrNT2JPs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fb90f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#---- min_samples_split-------#\n",
    "\n",
    "min_samples_splits = np.linspace(0.1, 1.0, 10, endpoint=True)\n",
    "train_results = []\n",
    "test_results = []\n",
    "for min_samples_split in min_samples_splits:\n",
    "    rf = RandomForestClassifier(min_samples_split=min_samples_split)\n",
    "    rf.fit(x_train, y_train)\n",
    "    train_pred = rf.predict(x_train)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    train_results.append(roc_auc)\n",
    "    y_pred = rf.predict(x_test)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    test_results.append(roc_auc)\n",
    "from matplotlib.legend_handler import HandlerLine2D\n",
    "line1, = plt.plot(min_samples_splits, train_results, 'b', label='Train_AUC')\n",
    "line2, = plt.plot(min_samples_splits, test_results, 'r', label='Test_AUC')\n",
    "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
    "plt.ylabel(\"AUC score\")\n",
    "plt.xlabel(\"min samples split\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A7lNZW66Te4UT8381YkRCKOmU3GxsbLh582ahy65duyYzXwtRwVg7VuOkx2BF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1193045f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_samples_leafs = np.linspace(0.1, 0.5, 5, endpoint=True)\n",
    "train_results = []\n",
    "test_results = []\n",
    "for min_samples_leaf in min_samples_leafs:\n",
    "    rf = RandomForestClassifier(min_samples_leaf=min_samples_leaf)\n",
    "    rf.fit(x_train, y_train)\n",
    "    train_pred = rf.predict(x_train)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    train_results.append(roc_auc)\n",
    "    y_pred = rf.predict(x_test)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    test_results.append(roc_auc)\n",
    "from matplotlib.legend_handler import HandlerLine2D\n",
    "line1, = plt.plot(min_samples_leafs, train_results, 'b', label='Train_AUC')\n",
    "line2, = plt.plot(min_samples_leafs, test_results, 'r', label='Test_AUC')\n",
    "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
    "plt.ylabel(\"AUC score\")\n",
    "plt.xlabel(\"min samples leaf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fzz//fKEvR5OQkEDTpk0dZ7W6VkBAQKFeqzgK3HLbtWsXM2fOJDw8nL59+zpN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\nIiJiOQo3ERGxHIWbiIhYjsJNREQs5z+ZAgONV4yd2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fce7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_features = list(range(1,X_total.shape[1]))\n",
    "train_results = []\n",
    "test_results = []\n",
    "for max_feature in max_features:\n",
    "    rf = RandomForestClassifier(max_features=max_feature)\n",
    "    rf.fit(x_train, y_train)\n",
    "    train_pred = rf.predict(x_train)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    train_results.append(roc_auc)\n",
    "    y_pred = rf.predict(x_test)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    test_results.append(roc_auc)\n",
    "from matplotlib.legend_handler import HandlerLine2D\n",
    "line1, = plt.plot(max_features, train_results, 'b', label='Train_AUC')\n",
    "line2, = plt.plot(max_features, test_results, 'r', label='Test_AUC')\n",
    "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
    "plt.ylabel(\"AUC score\")\n",
    "plt.xlabel(\"max features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model with rank: 1\n",
      "Mean validation score: 0.949 (std: 0.033)\n",
      "Parameters: {'bootstrap': False, 'criterion': 'gini', 'max_depth': None, 'max_features': 2, 'min_samples_leaf': 4, 'min_samples_split': 22}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.947 (std: 0.036)\n",
      "Parameters: {'bootstrap': False, 'criterion': 'gini', 'max_depth': None, 'max_features': 7, 'min_samples_leaf': 15, 'min_samples_split': 6}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.946 (std: 0.040)\n",
      "Parameters: {'bootstrap': True, 'criterion': 'gini', 'max_depth': None, 'max_features': 22, 'min_samples_leaf': 5, 'min_samples_split': 27}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean validation score: 0.946 (std: 0.039)\n",
      "Parameters: {'bootstrap': False, 'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_leaf': 18, 'min_samples_split': 15}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from scipy.stats import randint as sp_randint\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Utility function to report best scores\n",
    "def report(results, n_top=4):\n",
    "    for i in range(1, n_top + 1):\n",
    "        candidates = np.flatnonzero(results['rank_test_score'] == i)\n",
    "        for candidate in candidates:\n",
    "            print(\"Model with rank: {0}\".format(i))\n",
    "            print(\"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n",
    "                  results['mean_test_score'][candidate],\n",
    "                  results['std_test_score'][candidate]))\n",
    "            print(\"Parameters: {0}\".format(results['params'][candidate]))\n",
    "            print(\"\")\n",
    "\n",
    "\n",
    "# specify parameters and distributions to sample from\n",
    "param_dist = {\"max_depth\": [5, None],\n",
    "              \"max_features\": sp_randint(1, 32),\n",
    "              \"min_samples_split\": sp_randint(2, 32),\n",
    "              \"min_samples_leaf\": sp_randint(1, 32),\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "\n",
    "# run randomized search\n",
    "n_iter_search = 20\n",
    "random_search = RandomizedSearchCV(clf, param_distributions=param_dist,\n",
    "                                   n_iter=n_iter_search)\n",
    "\n",
    "#start_time = time.time()\n",
    "random_search.fit(X_total, Y_total)\n",
    "#print(\"RandomizedSearchCV took %.2f seconds for %d candidates\"\n",
    "      #\" parameter settings.\" % ((time() - start_time), n_iter_search))\n",
    "report(random_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use a full grid over all parameters\n",
    "param_grid = {\"n_estimators\": [10, 30, 60],\n",
    "              \"max_depth\": [3, None],\n",
    "              \"max_features\": ['auto', 'sqrt', 'log2'],\n",
    "              \"min_samples_split\": [19, 30],\n",
    "              \"min_samples_leaf\": [5, 10, 30],\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=84, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_estimators': [10, 30, 60], 'max_depth': [3, None], 'max_features': ['auto', 'sqrt', 'log2'], 'min_samples_split': [19, 30], 'min_samples_leaf': [5, 10, 30], 'bootstrap': [True, False], 'criterion': ['gini', 'entropy']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "grid_search = GridSearchCV(clf, param_grid=param_grid)\n",
    "start = time.time()\n",
    "grid_search.fit(X_total, Y_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False,\n",
       " 'criterion': 'entropy',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'min_samples_leaf': 5,\n",
       " 'min_samples_split': 19,\n",
       " 'n_estimators': 60}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def report_grd(grid_scores, n_top):\n",
    "    top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top]\n",
    "    for i, score in enumerate(top_scores):\n",
    "        print(\"Model with rank: {0}\".format(i + 1))\n",
    "        print(\"Mean validation score: {0:.4f})\".format(\n",
    "              score.mean_validation_score,\n",
    "              np.std(score.cv_validation_scores)))\n",
    "        print(\"Parameters: {0}\".format(score.parameters))\n",
    "        print(\"\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model with rank: 1\n",
      "Mean validation score: 0.9509)\n",
      "Parameters: {'bootstrap': False, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 5, 'min_samples_split': 19, 'n_estimators': 60}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.9509)\n",
      "Parameters: {'bootstrap': False, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 5, 'min_samples_split': 19, 'n_estimators': 60}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.9509)\n",
      "Parameters: {'bootstrap': False, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'min_samples_leaf': 5, 'min_samples_split': 19, 'n_estimators': 60}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean validation score: 0.9509)\n",
      "Parameters: {'bootstrap': False, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 5, 'min_samples_split': 19, 'n_estimators': 60}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hswamy/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "report_grd(grid_search.grid_scores_,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(bootstrap = False,\n",
    "                             criterion = 'entropy',\n",
    "                             max_features = 'auto',\n",
    "                             min_samples_split = 19, \n",
    "                             #max_leaf_nodes = 15, \n",
    "                             n_estimators = 60, \n",
    "                             max_depth = None,\n",
    "                             min_samples_leaf = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf.fit(X_total, Y_total);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_prob_total = clf.predict_proba(X_total)\n",
    "\n",
    "dtype = [('Col1','int32'), ('Col2','float32')]\n",
    "values = Y_prob_total\n",
    "index = ['Row'+str(i) for i in range(1, len(values)+1)]\n",
    "\n",
    "prob_df1 = pd.DataFrame(values, index=index)\n",
    "prob_df1.reset_index(inplace=True,drop = True)\n",
    "\n",
    "feature_data['Global_prob_nondefault']=prob_df1[0]\n",
    "feature_data['Global_prob_default']=prob_df1[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>New_ID_1</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime3059DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime6089DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>RUUL_indicator</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>NoIncome_MI_indicator</th>\n",
       "      <th>No_DebtRatio_indicator</th>\n",
       "      <th>MIz_DRnz</th>\n",
       "      <th>MIz_DRz</th>\n",
       "      <th>MInz_DRz</th>\n",
       "      <th>ZeroRevolvingUtilization</th>\n",
       "      <th>DR_MI</th>\n",
       "      <th>Log_DR_MI</th>\n",
       "      <th>RevolvingLines</th>\n",
       "      <th>HasRealEstateLoans</th>\n",
       "      <th>HasMultipleRealEstateLoans</th>\n",
       "      <th>DisposableIncome</th>\n",
       "      <th>RevolvingToRealEstate</th>\n",
       "      <th>FullUtilization</th>\n",
       "      <th>ExcessUtilization</th>\n",
       "      <th>RevolvingLinesPerPerson</th>\n",
       "      <th>RealEstateLoanPerPerson</th>\n",
       "      <th>IncomePerDependent</th>\n",
       "      <th>NumberOfTimePastDue</th>\n",
       "      <th>DelinquenciesPerLine</th>\n",
       "      <th>DelinquenciesPerRevolvingLine</th>\n",
       "      <th>Global_prob_nondefault</th>\n",
       "      <th>Global_prob_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110</td>\n",
       "      <td>0.076613</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>9120.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1136.787317</td>\n",
       "      <td>7.035961</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5777.448490</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3040.333333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.308923</td>\n",
       "      <td>0.691077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111</td>\n",
       "      <td>0.095715</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2600.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1300.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.941428</td>\n",
       "      <td>0.058572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>112</td>\n",
       "      <td>0.065818</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3042.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3042.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3043.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.786003</td>\n",
       "      <td>0.213997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>113</td>\n",
       "      <td>0.023381</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3301.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.994345</td>\n",
       "      <td>0.005655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>114</td>\n",
       "      <td>0.021318</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3101.672570</td>\n",
       "      <td>8.039697</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1750.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.989529</td>\n",
       "      <td>0.010471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>115</td>\n",
       "      <td>0.030568</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>8682.959225</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1136.787317</td>\n",
       "      <td>7.035961</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5500.586586</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8683.959225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.968158</td>\n",
       "      <td>0.031842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>116</td>\n",
       "      <td>0.075446</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3501.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.988576</td>\n",
       "      <td>0.011424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>117</td>\n",
       "      <td>0.011695</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3385.277832</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3385.277832</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3386.277832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.998198</td>\n",
       "      <td>0.001802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>118</td>\n",
       "      <td>0.064423</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.954853</td>\n",
       "      <td>0.045147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>119</td>\n",
       "      <td>0.001880</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>6501.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1136.787317</td>\n",
       "      <td>7.035961</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4118.332526</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2167.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.997507</td>\n",
       "      <td>0.002493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   New_ID_1  RevolvingUtilizationOfUnsecuredLines   age  \\\n",
       "0       110                              0.076613  45.0   \n",
       "1       111                              0.095715  40.0   \n",
       "2       112                              0.065818  38.0   \n",
       "3       113                              0.023381  30.0   \n",
       "4       114                              0.021318  74.0   \n",
       "5       115                              0.030568  57.0   \n",
       "6       116                              0.075446  39.0   \n",
       "7       117                              0.011695  27.0   \n",
       "8       118                              0.064423  30.0   \n",
       "9       119                              0.001880  51.0   \n",
       "\n",
       "   NumberOfTime3059DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                  2.0   0.366508    9120.000000   \n",
       "1                                  0.0   0.000000    2600.000000   \n",
       "2                                  1.0   0.000000    3042.000000   \n",
       "3                                  0.0   0.000000    3300.000000   \n",
       "4                                  0.0   1.000000    3500.000000   \n",
       "5                                  0.0   0.366508    8682.959225   \n",
       "6                                  0.0   0.000000    3500.000000   \n",
       "7                                  0.0   0.000000    3385.277832   \n",
       "8                                  0.0   0.000000    2500.000000   \n",
       "9                                  0.0   0.366508    6501.000000   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                             13.0                      0.0   \n",
       "1                              4.0                      0.0   \n",
       "2                              2.0                      1.0   \n",
       "3                              5.0                      0.0   \n",
       "4                              3.0                      0.0   \n",
       "5                              8.0                      0.0   \n",
       "6                              8.0                      0.0   \n",
       "7                              2.0                      0.0   \n",
       "8                              5.0                      0.0   \n",
       "9                              7.0                      0.0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime6089DaysPastDueNotWorse  \\\n",
       "0                           6.0                                  0.0   \n",
       "1                           0.0                                  0.0   \n",
       "2                           0.0                                  0.0   \n",
       "3                           0.0                                  0.0   \n",
       "4                           1.0                                  0.0   \n",
       "5                           3.0                                  0.0   \n",
       "6                           0.0                                  0.0   \n",
       "7                           0.0                                  0.0   \n",
       "8                           0.0                                  0.0   \n",
       "9                           2.0                                  0.0   \n",
       "\n",
       "   NumberOfDependents  RUUL_indicator  SeriousDlqin2yrs  \\\n",
       "0                 2.0             0.0                 1   \n",
       "1                 1.0             0.0                 0   \n",
       "2                 0.0             0.0                 0   \n",
       "3                 0.0             0.0                 0   \n",
       "4                 1.0             0.0                 0   \n",
       "5                 0.0             0.0                 0   \n",
       "6                 0.0             0.0                 0   \n",
       "7                 0.0             0.0                 0   \n",
       "8                 0.0             0.0                 0   \n",
       "9                 2.0             0.0                 0   \n",
       "\n",
       "   NoIncome_MI_indicator  No_DebtRatio_indicator  MIz_DRnz  MIz_DRz  MInz_DRz  \\\n",
       "0                      0                       1         0        0         0   \n",
       "1                      0                       1         0        0         1   \n",
       "2                      0                       1         0        0         1   \n",
       "3                      0                       1         0        0         1   \n",
       "4                      0                       1         0        0         0   \n",
       "5                      0                       1         0        0         0   \n",
       "6                      0                       1         0        0         1   \n",
       "7                      0                       1         0        0         1   \n",
       "8                      0                       1         0        0         1   \n",
       "9                      0                       1         0        0         0   \n",
       "\n",
       "   ZeroRevolvingUtilization        DR_MI  Log_DR_MI  RevolvingLines  \\\n",
       "0                         0  1136.787317   7.035961             7.0   \n",
       "1                         0     0.000000   0.000000             4.0   \n",
       "2                         0     0.000000   0.000000             2.0   \n",
       "3                         0     0.000000   0.000000             5.0   \n",
       "4                         0  3101.672570   8.039697             2.0   \n",
       "5                         0  1136.787317   7.035961             5.0   \n",
       "6                         0     0.000000   0.000000             8.0   \n",
       "7                         0     0.000000   0.000000             2.0   \n",
       "8                         0     0.000000   0.000000             5.0   \n",
       "9                         0  1136.787317   7.035961             5.0   \n",
       "\n",
       "   HasRealEstateLoans  HasMultipleRealEstateLoans   DisposableIncome  \\\n",
       "0                   1                            1       5777.448490   \n",
       "1                   0                            0       2600.000000   \n",
       "2                   0                            0       3042.000000   \n",
       "3                   0                            0       3300.000000   \n",
       "4                   1                            0          0.000000   \n",
       "5                   1                            1       5500.586586   \n",
       "6                   0                            0       3500.000000   \n",
       "7                   0                            0       3385.277832   \n",
       "8                   0                            0       2500.000000   \n",
       "9                   1                            0       4118.332526   \n",
       "\n",
       "   RevolvingToRealEstate  FullUtilization  ExcessUtilization  \\\n",
       "0               1.000000                0                  0   \n",
       "1               4.000000                0                  0   \n",
       "2               2.000000                0                  0   \n",
       "3               5.000000                0                  0   \n",
       "4               1.000000                0                  0   \n",
       "5               1.250000                0                  0   \n",
       "6               8.000000                0                  0   \n",
       "7               2.000000                0                  0   \n",
       "8               5.000000                0                  0   \n",
       "9               1.666667                0                  0   \n",
       "\n",
       "   RevolvingLinesPerPerson  RealEstateLoanPerPerson  IncomePerDependent  \\\n",
       "0                 2.333333                 2.000000         3040.333333   \n",
       "1                 2.000000                 0.000000         1300.500000   \n",
       "2                 2.000000                 0.000000         3043.000000   \n",
       "3                 5.000000                 0.000000         3301.000000   \n",
       "4                 1.000000                 0.500000         1750.500000   \n",
       "5                 5.000000                 3.000000         8683.959225   \n",
       "6                 8.000000                 0.000000         3501.000000   \n",
       "7                 2.000000                 0.000000         3386.277832   \n",
       "8                 5.000000                 0.000000         2501.000000   \n",
       "9                 1.666667                 0.666667         2167.333333   \n",
       "\n",
       "   NumberOfTimePastDue  DelinquenciesPerLine  DelinquenciesPerRevolvingLine  \\\n",
       "0                  2.0              0.153846                       0.285714   \n",
       "1                  0.0              0.000000                       0.000000   \n",
       "2                  2.0              1.000000                       1.000000   \n",
       "3                  0.0              0.000000                       0.000000   \n",
       "4                  0.0              0.000000                       0.000000   \n",
       "5                  0.0              0.000000                       0.000000   \n",
       "6                  0.0              0.000000                       0.000000   \n",
       "7                  0.0              0.000000                       0.000000   \n",
       "8                  0.0              0.000000                       0.000000   \n",
       "9                  0.0              0.000000                       0.000000   \n",
       "\n",
       "   Global_prob_nondefault  Global_prob_default  \n",
       "0                0.308923             0.691077  \n",
       "1                0.941428             0.058572  \n",
       "2                0.786003             0.213997  \n",
       "3                0.994345             0.005655  \n",
       "4                0.989529             0.010471  \n",
       "5                0.968158             0.031842  \n",
       "6                0.988576             0.011424  \n",
       "7                0.998198             0.001802  \n",
       "8                0.954853             0.045147  \n",
       "9                0.997507             0.002493  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_to_drop = ['SeriousDlqin2yrs', 'New_ID_1','Global_prob_nondefault','Global_prob_default']\n",
    "\n",
    "feature_data_desc = feature_data.drop(features_to_drop, axis=1)\n",
    "\n",
    "data_stats = feature_data_desc.describe(include='all')  # take the describtion of dataset\n",
    "data_stats1 = data_stats.drop(['count','std'])   # drop the column which u don't require\n",
    "clf.rfm = data_stats1                            # embedded the model with the above data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(clf, 'random_frst.pkl') \n",
    "\n",
    "load_clf = joblib.load('random_frst.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=False, class_weight=None,\n",
       "            criterion='entropy', max_depth=None, max_features='auto',\n",
       "            max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=5,\n",
       "            min_samples_split=19, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=60, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime3059DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime6089DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>RUUL_indicator</th>\n",
       "      <th>NoIncome_MI_indicator</th>\n",
       "      <th>No_DebtRatio_indicator</th>\n",
       "      <th>MIz_DRnz</th>\n",
       "      <th>MIz_DRz</th>\n",
       "      <th>MInz_DRz</th>\n",
       "      <th>ZeroRevolvingUtilization</th>\n",
       "      <th>DR_MI</th>\n",
       "      <th>Log_DR_MI</th>\n",
       "      <th>RevolvingLines</th>\n",
       "      <th>HasRealEstateLoans</th>\n",
       "      <th>HasMultipleRealEstateLoans</th>\n",
       "      <th>DisposableIncome</th>\n",
       "      <th>RevolvingToRealEstate</th>\n",
       "      <th>FullUtilization</th>\n",
       "      <th>ExcessUtilization</th>\n",
       "      <th>RevolvingLinesPerPerson</th>\n",
       "      <th>RealEstateLoanPerPerson</th>\n",
       "      <th>IncomePerDependent</th>\n",
       "      <th>NumberOfTimePastDue</th>\n",
       "      <th>DelinquenciesPerLine</th>\n",
       "      <th>DelinquenciesPerRevolvingLine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.050769</td>\n",
       "      <td>49.172756</td>\n",
       "      <td>0.562139</td>\n",
       "      <td>0.413276</td>\n",
       "      <td>5686.623202</td>\n",
       "      <td>8.054529</td>\n",
       "      <td>0.333957</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>0.210115</td>\n",
       "      <td>0.818066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004601</td>\n",
       "      <td>0.003786</td>\n",
       "      <td>0.337317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1281.846085</td>\n",
       "      <td>4.852228</td>\n",
       "      <td>7.076163</td>\n",
       "      <td>0.658897</td>\n",
       "      <td>0.081441</td>\n",
       "      <td>3067.401970</td>\n",
       "      <td>4.103791</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.965396</td>\n",
       "      <td>0.653183</td>\n",
       "      <td>3869.911654</td>\n",
       "      <td>1.106211</td>\n",
       "      <td>0.320104</td>\n",
       "      <td>0.400268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.002444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</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>0.009923</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3306.464082</td>\n",
       "      <td>4.261800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>199.878776</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1890.321894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.047133</td>\n",
       "      <td>48.508165</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.214147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1136.787317</td>\n",
       "      <td>7.035961</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2803.107183</td>\n",
       "      <td>3.395755</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.410472</td>\n",
       "      <td>3182.458023</td>\n",
       "      <td>0.107722</td>\n",
       "      <td>0.008553</td>\n",
       "      <td>0.008310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.088593</td>\n",
       "      <td>58.889688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910742</td>\n",
       "      <td>7248.889622</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.162332</td>\n",
       "      <td>1.608499</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.357962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2824.824230</td>\n",
       "      <td>7.946201</td>\n",
       "      <td>9.577402</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4699.000000</td>\n",
       "      <td>5.291712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5176.133687</td>\n",
       "      <td>1.713722</td>\n",
       "      <td>0.257142</td>\n",
       "      <td>0.295240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.999000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23033.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3101.672570</td>\n",
       "      <td>8.039697</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23034.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3422.289048</td>\n",
       "      <td>6844.578097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RevolvingUtilizationOfUnsecuredLines        age  \\\n",
       "mean                              0.050769  49.172756   \n",
       "min                               0.000000  22.000000   \n",
       "25%                               0.009923  39.000000   \n",
       "50%                               0.047133  48.508165   \n",
       "75%                               0.088593  58.889688   \n",
       "max                               0.999000  91.000000   \n",
       "\n",
       "      NumberOfTime3059DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "mean                             0.562139   0.413276    5686.623202   \n",
       "min                              0.000000   0.000000       0.000000   \n",
       "25%                              0.000000   0.000000    3306.464082   \n",
       "50%                              0.000000   0.366508    5000.000000   \n",
       "75%                              1.000000   0.910742    7248.889622   \n",
       "max                             13.000000   1.000000   23033.000000   \n",
       "\n",
       "      NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "mean                         8.054529                 0.333957   \n",
       "min                          0.000000                 0.000000   \n",
       "25%                          4.261800                 0.000000   \n",
       "50%                          7.000000                 0.000000   \n",
       "75%                         11.000000                 0.162332   \n",
       "max                         58.000000                17.000000   \n",
       "\n",
       "      NumberRealEstateLoansOrLines  NumberOfTime6089DaysPastDueNotWorse  \\\n",
       "mean                      0.978366                             0.210115   \n",
       "min                       0.000000                             0.000000   \n",
       "25%                       0.000000                             0.000000   \n",
       "50%                       1.000000                             0.000000   \n",
       "75%                       1.608499                             0.000000   \n",
       "max                      25.000000                            11.000000   \n",
       "\n",
       "      NumberOfDependents  RUUL_indicator  NoIncome_MI_indicator  \\\n",
       "mean            0.818066             0.0                    0.0   \n",
       "min             0.000000             0.0                    0.0   \n",
       "25%             0.000000             0.0                    0.0   \n",
       "50%             0.214147             0.0                    0.0   \n",
       "75%             1.357962             0.0                    0.0   \n",
       "max            10.000000             0.0                    0.0   \n",
       "\n",
       "      No_DebtRatio_indicator  MIz_DRnz   MIz_DRz  MInz_DRz  \\\n",
       "mean                     1.0  0.004601  0.003786  0.337317   \n",
       "min                      1.0  0.000000  0.000000  0.000000   \n",
       "25%                      1.0  0.000000  0.000000  0.000000   \n",
       "50%                      1.0  0.000000  0.000000  0.000000   \n",
       "75%                      1.0  0.000000  0.000000  1.000000   \n",
       "max                      1.0  1.000000  1.000000  1.000000   \n",
       "\n",
       "      ZeroRevolvingUtilization        DR_MI  Log_DR_MI  RevolvingLines  \\\n",
       "mean                       0.0  1281.846085   4.852228        7.076163   \n",
       "min                        0.0     0.000000  -3.002444        0.000000   \n",
       "25%                        0.0     0.000000   0.000000        4.000000   \n",
       "50%                        0.0  1136.787317   7.035961        6.000000   \n",
       "75%                        0.0  2824.824230   7.946201        9.577402   \n",
       "max                        0.0  3101.672570   8.039697       57.000000   \n",
       "\n",
       "      HasRealEstateLoans  HasMultipleRealEstateLoans   DisposableIncome  \\\n",
       "mean            0.658897                     0.081441       3067.401970   \n",
       "min             0.000000                     0.000000          0.000000   \n",
       "25%             0.000000                     0.000000        199.878776   \n",
       "50%             1.000000                     0.000000       2803.107183   \n",
       "75%             1.000000                     0.000000       4699.000000   \n",
       "max             1.000000                     1.000000      23000.000000   \n",
       "\n",
       "      RevolvingToRealEstate  FullUtilization  ExcessUtilization  \\\n",
       "mean               4.103791              0.0                0.0   \n",
       "min                0.000000              0.0                0.0   \n",
       "25%                2.000000              0.0                0.0   \n",
       "50%                3.395755              0.0                0.0   \n",
       "75%                5.291712              0.0                0.0   \n",
       "max               57.000000              0.0                0.0   \n",
       "\n",
       "      RevolvingLinesPerPerson  RealEstateLoanPerPerson  IncomePerDependent  \\\n",
       "mean                 4.965396                 0.653183         3869.911654   \n",
       "min                  0.000000                 0.000000            0.090909   \n",
       "25%                  2.000000                 0.000000         1890.321894   \n",
       "50%                  4.000000                 0.410472         3182.458023   \n",
       "75%                  7.000000                 1.000000         5176.133687   \n",
       "max                 57.000000                23.000000        23034.000000   \n",
       "\n",
       "      NumberOfTimePastDue  DelinquenciesPerLine  DelinquenciesPerRevolvingLine  \n",
       "mean             1.106211              0.320104                       0.400268  \n",
       "min              0.000000              0.000000                       0.000000  \n",
       "25%              0.000000              0.000000                       0.000000  \n",
       "50%              0.107722              0.008553                       0.008310  \n",
       "75%              1.713722              0.257142                       0.295240  \n",
       "max             19.000000           3422.289048                    6844.578097  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_clf.rfm"
   ]
  }
 ],
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