** Group Project: Predicting Delinquent Customer **
Problem Statement
Thousands of credit card applications are received each year by CredX, a leading provider of credit cards. In recent years, however, it has seen a rise in the number of credit defaults. The CEO believes that acquiring the right customers is the best strategy for reducing credit risk. We are tasked with identifying the right customers for CredX’s business using predictive models. Use historical data on bank applicants to identify factors that affect credit risk, develop strategies to reduce acquisition risk, and assess the project’s financial benefit.
Background & Objective
- Background
- There are thousands of people who apply for a credit card with CredX every year. In recent years, however, it has seen a rise in the number of credit defaults.
- The CEO believes that acquiring “the right customers” is the best strategy for mitigating credit risk.
- Objective
- Using predictive modelling, CredX hopes to find the right customers. Credit risk factors must be identified, strategies must be developed to mitigate acquisition risk, and the financial benefits of your project must be assessed.
- Our project must be evaluated and explained to bank management in terms of its potential financial benefit. We must also identify the metrics we are trying to optimise, explain how the analysis and model work, and present the model’s results.
- Create a scorecard for applicants and determine the minimum score below which you will not issue credit cards to them.
Problem Solving Methodology – Analysis Flow
- Data Preparation
Load library
library(ggplot2)
library(gridExtra)
library(grid)
library(MASS)
library(car)## Loading required package: carDatalibrary(e1071)
library(caret) ## Loading required package: latticelibrary(caTools)
library(randomForest)## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:gridExtra':
##
## combine## The following object is masked from 'package:ggplot2':
##
## marginlibrary(ROCR)- Load datasets- Demographic and Credit Bureau Data
demographic_df<- read.csv(file = 'demogs.csv',header = T,stringsAsFactors = T, na.strings = c("NA"))
credit_df<- read.csv(file = 'Credit_Bureau.csv',header = T,stringsAsFactors = T, na.strings =c("NA"))nrow(demographic_df)## [1] 71295nrow(credit_df)## [1] 71295- Remove Duplicate Rows
Observing duplication in unique ID column before joining.
sum(duplicated(demographic_df$Application.ID))## [1] 3sum(duplicated(credit_df$Application.ID))## [1] 3There are 3 rows in which application id is duplicated.
length(unique(tolower(demographic_df$Application.ID)))## [1] 71292length(unique(tolower(credit_df$Application.ID)))## [1] 71292demographic_df[duplicated(demographic_df$Application.ID),]## Application.ID Age Gender Marital.Status..at.the.time.of.application.
## 27587 765011468 38 M Married
## 42638 653287861 40 M Married
## 59023 671989187 57 M Married
## No.of.dependents Income Education Profession Type.of.residence
## 27587 4 4.5 Professional SAL Rented
## 42638 5 32.0 Phd SE Rented
## 59023 4 7.0 Professional SE Rented
## No.of.months.in.current.residence No.of.months.in.current.company
## 27587 6 72
## 42638 45 46
## 59023 42 3
## Performance.Tag
## 27587 0
## 42638 1
## 59023 0credit_df[duplicated(credit_df$Application.ID),]## Application.ID No.of.times.90.DPD.or.worse.in.last.6.months
## 27587 765011468 0
## 42638 653287861 1
## 59023 671989187 0
## No.of.times.60.DPD.or.worse.in.last.6.months
## 27587 0
## 42638 1
## 59023 1
## No.of.times.30.DPD.or.worse.in.last.6.months
## 27587 0
## 42638 1
## 59023 2
## No.of.times.90.DPD.or.worse.in.last.12.months
## 27587 0
## 42638 2
## 59023 0
## No.of.times.60.DPD.or.worse.in.last.12.months
## 27587 0
## 42638 2
## 59023 2
## No.of.times.30.DPD.or.worse.in.last.12.months
## 27587 0
## 42638 2
## 59023 3
## Avgas.CC.Utilization.in.last.12.months
## 27587 11
## 42638 113
## 59023 76
## No.of.trades.opened.in.last.6.months
## 27587 1
## 42638 2
## 59023 3
## No.of.trades.opened.in.last.12.months
## 27587 3
## 42638 5
## 59023 7
## No.of.PL.trades.opened.in.last.6.months
## 27587 0
## 42638 1
## 59023 1
## No.of.PL.trades.opened.in.last.12.months
## 27587 0
## 42638 3
## 59023 4
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans.
## 27587 1
## 42638 1
## 59023 2
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.
## 27587 3
## 42638 3
## 59023 6
## Presence.of.open.home.loan Outstanding.Balance Total.No.of.Trades
## 27587 0 29817 6
## 42638 0 628075 6
## 59023 0 822298 7
## Presence.of.open.auto.loan Performance.Tag
## 27587 0 0
## 42638 0 1
## 59023 0 0765011468,653287861,671989187 are duplicate application id in both the datasets.
#Removing the duplicate entry for these application id
demographic_df <- demographic_df[-which(duplicated(demographic_df$Application.ID) == T), ]
credit_df <- credit_df[-which(duplicated(credit_df$Application.ID) == T), ]nrow(credit_df)## [1] 71292nrow(demographic_df)## [1] 71292- Merge Demographic and Credit Bureau Data on Applicant ID
Merging by common attributes and by unique rows
merged_df<- merge(x = unique(demographic_df)
, y = unique(credit_df)
, by = c("Application.ID", "Performance.Tag"))
nrow(merged_df)## [1] 71292master_data_backup<-merged_dfDropping ID column as it is of no use.
merged_df<-merged_df[,-1]check Duplicate rows in data
No duplicate rows present.
sum(duplicated(merged_df))## [1] 0#Finding rows where dependant variable-“Performance.Tag” is not populated.
rejected_applicants<-merged_df[which( is.na(merged_df$Performance.Tag)),]
nrow(rejected_applicants)/nrow(merged_df) ## [1] 0.01998822The dependent variable ‘performance.tag’ has NA values in only 1.9 percent of the rows.
Assumption 1 - So model should be built on data where credit card was approved(0/1)
performance.tag is not available for applicants for whom a credit decision was not made in the first place (NA).
As a result, these rows will be removed from the table. Score cards would be verified using rejected applicants.
data_for_eda <- merged_df[!is.na(merged_df$Performance.Tag) == TRUE,]
str(data_for_eda)## 'data.frame': 69867 obs. of 28 variables:
## $ Performance.Tag : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Age : int 47 53 41 44 53 53 59 31 39 58 ...
## $ Gender : Factor w/ 3 levels "","F","M": 3 3 3 3 3 3 3 2 3 3 ...
## $ Marital.Status..at.the.time.of.application. : Factor w/ 3 levels "","Married","Single": 2 2 2 2 2 2 2 2 2 2 ...
## $ No.of.dependents : int 5 4 1 2 4 4 3 3 4 2 ...
## $ Income : num 25 43 12 43 33 33 44 31 35 31 ...
## $ Education : Factor w/ 6 levels "","Bachelor",..: 3 6 3 2 6 2 3 2 3 6 ...
## $ Profession : Factor w/ 4 levels "","SAL","SE",..: 3 2 3 2 3 2 3 2 2 3 ...
## $ Type.of.residence : Factor w/ 6 levels "","Company provided",..: 6 6 6 5 5 6 6 6 6 5 ...
## $ No.of.months.in.current.residence : int 6 6 6 6 100 78 100 6 6 6 ...
## $ No.of.months.in.current.company : int 23 44 16 15 13 28 10 52 28 24 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : int 1 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : int 2 0 0 0 1 0 1 0 0 0 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ Avgas.CC.Utilization.in.last.12.months : int 47 3 6 12 66 8 11 6 16 8 ...
## $ No.of.trades.opened.in.last.6.months : int 3 1 0 1 2 1 6 1 2 0 ...
## $ No.of.trades.opened.in.last.12.months : int 7 2 0 1 6 2 14 3 2 1 ...
## $ No.of.PL.trades.opened.in.last.6.months : int 2 0 0 0 2 0 1 0 0 0 ...
## $ No.of.PL.trades.opened.in.last.12.months : int 4 0 0 0 3 0 2 1 0 0 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : int 2 0 0 0 2 0 4 1 2 0 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: int 7 0 0 0 4 0 10 1 4 0 ...
## $ Presence.of.open.home.loan : int 0 1 1 0 1 0 0 0 1 0 ...
## $ Outstanding.Balance : int 749114 2955120 2935260 3366 3419174 25859 406850 223213 2934243 0 ...
## $ Total.No.of.Trades : int 8 3 3 2 8 5 21 5 4 3 ...
## $ Presence.of.open.auto.loan : int 0 0 0 0 0 0 0 0 0 0 ...Summary of master data
summary(data_for_eda)## Performance.Tag Age Gender
## Min. :0.00000 Min. :-3 : 2
## 1st Qu.:0.00000 1st Qu.:37 F:16506
## Median :0.00000 Median :45 M:53359
## Mean :0.04218 Mean :45
## 3rd Qu.:0.00000 3rd Qu.:53
## Max. :1.00000 Max. :65
##
## Marital.Status..at.the.time.of.application. No.of.dependents Income
## : 6 Min. :1.000 Min. :-0.50
## Married:59544 1st Qu.:2.000 1st Qu.:14.00
## Single :10317 Median :3.000 Median :27.00
## Mean :2.859 Mean :27.41
## 3rd Qu.:4.000 3rd Qu.:40.00
## Max. :5.000 Max. :60.00
## NA's :3
## Education Profession Type.of.residence
## : 118 : 13 : 8
## Bachelor :17302 SAL :39673 Company provided : 1603
## Masters :23481 SE :13925 Living with Parents: 1778
## Others : 119 SE_PROF:16256 Others : 198
## Phd : 4463 Owned :14003
## Professional:24384 Rented :52277
##
## No.of.months.in.current.residence No.of.months.in.current.company
## Min. : 6.00 Min. : 3.0
## 1st Qu.: 6.00 1st Qu.: 17.0
## Median : 10.00 Median : 34.0
## Mean : 34.61 Mean : 34.2
## 3rd Qu.: 61.00 3rd Qu.: 51.0
## Max. :126.00 Max. :133.0
##
## No.of.times.90.DPD.or.worse.in.last.6.months
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.249
## 3rd Qu.:0.000
## Max. :3.000
##
## No.of.times.60.DPD.or.worse.in.last.6.months
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.3917
## 3rd Qu.:1.0000
## Max. :5.0000
##
## No.of.times.30.DPD.or.worse.in.last.6.months
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.5235
## 3rd Qu.:1.0000
## Max. :7.0000
##
## No.of.times.90.DPD.or.worse.in.last.12.months
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.4148
## 3rd Qu.:1.0000
## Max. :5.0000
##
## No.of.times.60.DPD.or.worse.in.last.12.months
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.6034
## 3rd Qu.:1.0000
## Max. :7.0000
##
## No.of.times.30.DPD.or.worse.in.last.12.months
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.7339
## 3rd Qu.:1.0000
## Max. :9.0000
##
## Avgas.CC.Utilization.in.last.12.months No.of.trades.opened.in.last.6.months
## Min. : 0.00 Min. : 0.000
## 1st Qu.: 8.00 1st Qu.: 1.000
## Median : 15.00 Median : 2.000
## Mean : 29.26 Mean : 2.285
## 3rd Qu.: 45.00 3rd Qu.: 3.000
## Max. :113.00 Max. :12.000
## NA's :1023 NA's :1
## No.of.trades.opened.in.last.12.months No.of.PL.trades.opened.in.last.6.months
## Min. : 0.000 Min. :0.00
## 1st Qu.: 2.000 1st Qu.:0.00
## Median : 4.000 Median :1.00
## Mean : 5.785 Mean :1.19
## 3rd Qu.: 9.000 3rd Qu.:2.00
## Max. :28.000 Max. :6.00
##
## No.of.PL.trades.opened.in.last.12.months
## Min. : 0.000
## 1st Qu.: 0.000
## Median : 2.000
## Mean : 2.363
## 3rd Qu.: 4.000
## Max. :12.000
##
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans.
## Min. : 0.000
## 1st Qu.: 0.000
## Median : 1.000
## Mean : 1.758
## 3rd Qu.: 3.000
## Max. :10.000
##
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.
## Min. : 0.000
## 1st Qu.: 0.000
## Median : 3.000
## Mean : 3.525
## 3rd Qu.: 5.000
## Max. :20.000
##
## Presence.of.open.home.loan Outstanding.Balance Total.No.of.Trades
## Min. :0.0000 Min. : 0 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.: 208396 1st Qu.: 3.000
## Median :0.0000 Median : 774241 Median : 6.000
## Mean :0.2597 Mean :1253370 Mean : 8.175
## 3rd Qu.:1.0000 3rd Qu.:2926238 3rd Qu.:10.000
## Max. :1.0000 Max. :5218801 Max. :44.000
## NA's :272 NA's :272
## Presence.of.open.auto.loan
## Min. :0.00000
## 1st Qu.:0.00000
## Median :0.00000
## Mean :0.08488
## 3rd Qu.:0.00000
## Max. :1.00000
## str(data_for_eda)## 'data.frame': 69867 obs. of 28 variables:
## $ Performance.Tag : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Age : int 47 53 41 44 53 53 59 31 39 58 ...
## $ Gender : Factor w/ 3 levels "","F","M": 3 3 3 3 3 3 3 2 3 3 ...
## $ Marital.Status..at.the.time.of.application. : Factor w/ 3 levels "","Married","Single": 2 2 2 2 2 2 2 2 2 2 ...
## $ No.of.dependents : int 5 4 1 2 4 4 3 3 4 2 ...
## $ Income : num 25 43 12 43 33 33 44 31 35 31 ...
## $ Education : Factor w/ 6 levels "","Bachelor",..: 3 6 3 2 6 2 3 2 3 6 ...
## $ Profession : Factor w/ 4 levels "","SAL","SE",..: 3 2 3 2 3 2 3 2 2 3 ...
## $ Type.of.residence : Factor w/ 6 levels "","Company provided",..: 6 6 6 5 5 6 6 6 6 5 ...
## $ No.of.months.in.current.residence : int 6 6 6 6 100 78 100 6 6 6 ...
## $ No.of.months.in.current.company : int 23 44 16 15 13 28 10 52 28 24 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : int 1 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : int 2 0 0 0 1 0 1 0 0 0 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ Avgas.CC.Utilization.in.last.12.months : int 47 3 6 12 66 8 11 6 16 8 ...
## $ No.of.trades.opened.in.last.6.months : int 3 1 0 1 2 1 6 1 2 0 ...
## $ No.of.trades.opened.in.last.12.months : int 7 2 0 1 6 2 14 3 2 1 ...
## $ No.of.PL.trades.opened.in.last.6.months : int 2 0 0 0 2 0 1 0 0 0 ...
## $ No.of.PL.trades.opened.in.last.12.months : int 4 0 0 0 3 0 2 1 0 0 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : int 2 0 0 0 2 0 4 1 2 0 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: int 7 0 0 0 4 0 10 1 4 0 ...
## $ Presence.of.open.home.loan : int 0 1 1 0 1 0 0 0 1 0 ...
## $ Outstanding.Balance : int 749114 2955120 2935260 3366 3419174 25859 406850 223213 2934243 0 ...
## $ Total.No.of.Trades : int 8 3 3 2 8 5 21 5 4 3 ...
## $ Presence.of.open.auto.loan : int 0 0 0 0 0 0 0 0 0 0 ...- Check NAs and NANs
Missing and empty value detection and treatment
missing_val_counts<- sapply(data_for_eda, function(x) sum(is.na(x)))
missing_val_counts## Performance.Tag
## 0
## Age
## 0
## Gender
## 0
## Marital.Status..at.the.time.of.application.
## 0
## No.of.dependents
## 3
## Income
## 0
## Education
## 0
## Profession
## 0
## Type.of.residence
## 0
## No.of.months.in.current.residence
## 0
## No.of.months.in.current.company
## 0
## No.of.times.90.DPD.or.worse.in.last.6.months
## 0
## No.of.times.60.DPD.or.worse.in.last.6.months
## 0
## No.of.times.30.DPD.or.worse.in.last.6.months
## 0
## No.of.times.90.DPD.or.worse.in.last.12.months
## 0
## No.of.times.60.DPD.or.worse.in.last.12.months
## 0
## No.of.times.30.DPD.or.worse.in.last.12.months
## 0
## Avgas.CC.Utilization.in.last.12.months
## 1023
## No.of.trades.opened.in.last.6.months
## 1
## No.of.trades.opened.in.last.12.months
## 0
## No.of.PL.trades.opened.in.last.6.months
## 0
## No.of.PL.trades.opened.in.last.12.months
## 0
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans.
## 0
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.
## 0
## Presence.of.open.home.loan
## 272
## Outstanding.Balance
## 272
## Total.No.of.Trades
## 0
## Presence.of.open.auto.loan
## 03 NAs found in column - “No.of.dependents” 1023 NAs in Avgas.CC.Utilization.in.last.12.months 1 NA value detected in “No.of.trades.opened.in.last.6.months” column 272 NAs in Presence.of.open.home.loan 272 NAs in Outstanding.Balance
Handle missing values by asssigning median value to respective NA records.
data_for_eda$No.of.dependents[which(is.na(data_for_eda$No.of.dependents)==1)]<-median(data_for_eda$No.of.dependents, na.rm = T)
data_for_eda$No.of.trades.opened.in.last.6.months[which(is.na(data_for_eda$No.of.trades.opened.in.last.6.months)==1)]=median(data_for_eda$No.of.trades.opened.in.last.6.months, na.rm = T)
data_for_eda$Presence.of.open.home.loan[which(is.na(data_for_eda$Presence.of.open.home.loan)==1)] = median(data_for_eda$Presence.of.open.home.loan,na.rm = T)
data_for_eda$Outstanding.Balance[which(is.na(data_for_eda$Outstanding.Balance)==1)] = median(data_for_eda$Outstanding.Balance,na.rm = T)Assumption - 2 : NA value in Avgas.CC.Utilization.in.last.12.months is indicating no usage of CC by user. So lets assign value 0 to these avg-cc-utilization values.
data_for_eda$Avgas.CC.Utilization.in.last.12.months[which(is.na(data_for_eda$Avgas.CC.Utilization.in.last.12.months)==1)] = 0checking for empty values
empty_val_counts<- sapply(data_for_eda, function(x) sum(x==" " | x==""))
empty_val_counts## Performance.Tag
## 0
## Age
## 0
## Gender
## 2
## Marital.Status..at.the.time.of.application.
## 6
## No.of.dependents
## 0
## Income
## 0
## Education
## 118
## Profession
## 13
## Type.of.residence
## 8
## No.of.months.in.current.residence
## 0
## No.of.months.in.current.company
## 0
## No.of.times.90.DPD.or.worse.in.last.6.months
## 0
## No.of.times.60.DPD.or.worse.in.last.6.months
## 0
## No.of.times.30.DPD.or.worse.in.last.6.months
## 0
## No.of.times.90.DPD.or.worse.in.last.12.months
## 0
## No.of.times.60.DPD.or.worse.in.last.12.months
## 0
## No.of.times.30.DPD.or.worse.in.last.12.months
## 0
## Avgas.CC.Utilization.in.last.12.months
## 0
## No.of.trades.opened.in.last.6.months
## 0
## No.of.trades.opened.in.last.12.months
## 0
## No.of.PL.trades.opened.in.last.6.months
## 0
## No.of.PL.trades.opened.in.last.12.months
## 0
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans.
## 0
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.
## 0
## Presence.of.open.home.loan
## 0
## Outstanding.Balance
## 0
## Total.No.of.Trades
## 0
## Presence.of.open.auto.loan
## 0Gender - 2 Marital.Status..at.the.time.of.application. - 6 Education - 118 Profession - 13 Type.of.residence - 8
Repalce empty strings with values with maximum freq
data_for_eda$Gender[which(data_for_eda$Gender==" " | data_for_eda$Gender==" ")]<- 'M'
data_for_eda$Marital.Status..at.the.time.of.application.[which(data_for_eda$Marital.Status..at.the.time.of.application.==" " | data_for_eda$Marital.Status..at.the.time.of.application.==" ")]<- 'Married'
data_for_eda$Education[which(data_for_eda$Education==" " | data_for_eda$Education==" ")]<- 'Professional'
data_for_eda$Profession[which(data_for_eda$Profession==" " | data_for_eda$Profession==" ")]<- 'SAL'
data_for_eda$Type.of.residence[which(data_for_eda$Type.of.residence==" " | data_for_eda$Type.of.residence==" ")]<- 'Rented'- Outlier Treatment
Method to find outliers
FindOutliers <- function(data) {
lowerq = quantile(data,probs = seq(0,1,0.10))[3] #20%
upperq = quantile(data,probs = seq(0,1,0.10))[9] #80%
iqr = upperq - lowerq #Or use IQR(data)
extreme.threshold.upper = (iqr * 1.5) + upperq
extreme.threshold.lower = lowerq - (iqr * 1.5)
# we identify extreme outlier indeces
result <- which(data > extreme.threshold.upper | data < extreme.threshold.lower)
}
company_recency_outliers <- data_for_eda[FindOutliers(data_for_eda$No.of.months.in.current.company),]
avg_cc_utilization_outliers <- data_for_eda[FindOutliers(data_for_eda$Avgas.CC.Utilization.in.last.12.months),]
last_6mon_trades_outliers <- data_for_eda[FindOutliers(data_for_eda$No.of.trades.opened.in.last.6.months),]
last_12mon_trades_outliers <- data_for_eda[FindOutliers(data_for_eda$No.of.trades.opened.in.last.12.months),]
last_6mon_pl_outliers <- data_for_eda[FindOutliers(data_for_eda$No.of.PL.trades.opened.in.last.6.months),]
last_12mon_pl_outliers <- data_for_eda[FindOutliers(data_for_eda$No.of.PL.trades.opened.in.last.12.months),]
last_6mon_inqr_outliers <- data_for_eda[FindOutliers(data_for_eda$No.of.Inquiries.in.last.6.months..excluding.home...auto.loans.),]
last_12mon_inqr_outliers <- data_for_eda[FindOutliers(data_for_eda$No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.),]
total_trd_outliers <- data_for_eda[FindOutliers(data_for_eda$Total.No.of.Trades),]We are not ceiling outliers to ensure no info loss.
Some outlier samples
company_recency_outliers$No.of.months.in.current.company## [1] 123 121 133 128 126last_6mon_pl_outliers$No.of.PL.trades.opened.in.last.6.months## [1] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [38] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [75] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [112] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [149] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [186] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [223] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [260] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6total_trd_outliers$Total.No.of.Trades## [1] 26 32 26 29 28 28 27 26 26 25 29 25 32 31 27 26 31 26 26 24 24 29 26 27
## [25] 27 26 27 30 30 25 28 31 24 31 29 26 28 27 28 26 33 25 26 36 26 31 28 39
## [49] 28 33 34 27 27 25 33 25 32 27 26 24 32 27 27 28 29 27 24 25 25 26 28 25
## [73] 35 28 24 24 24 29 24 31 24 25 38 28 26 30 33 27 31 24 26 29 26 29 24 27
## [97] 33 25 32 26 24 26 33 27 30 27 31 25 26 27 27 26 26 32 30 26 29 28 24 36
## [121] 29 25 31 29 30 28 28 31 26 27 34 30 25 25 26 26 25 27 28 28 27 25 30 27
## [145] 29 28 25 24 30 27 27 29 26 32 24 27 35 25 26 28 25 28 27 25 26 24 26 27
## [169] 31 27 28 26 26 32 24 24 28 25 27 26 24 24 24 30 27 27 25 30 31 33 27 28
## [193] 24 25 32 24 27 27 32 36 31 28 28 30 37 30 26 27 25 24 30 26 27 28 28 25
## [217] 29 24 28 26 28 29 27 29 26 25 25 26 33 27 25 26 24 24 24 30 24 25 25 28
## [241] 29 27 30 31 26 33 30 26 28 26 29 31 32 34 24 24 28 29 27 30 25 36 26 26
## [265] 31 28 24 26 29 26 30 43 27 26 26 30 24 32 26 28 34 32 24 27 26 26 30 28
## [289] 27 24 31 24 27 27 30 26 24 30 27 28 26 28 27 29 28 24 25 27 25 32 26 25
## [313] 25 26 29 31 27 26 24 28 27 36 27 29 29 26 25 28 24 29 27 32 28 35 33 36
## [337] 28 24 35 29 34 29 30 27 28 24 25 25 29 27 28 25 29 26 30 29 26 24 26 25
## [361] 24 25 30 31 29 29 28 27 24 30 25 28 28 25 32 27 35 32 25 28 26 34 25 25
## [385] 32 28 26 25 24 26 29 31 25 27 32 27 25 26 26 24 29 32 30 32 24 35 27 26
## [409] 26 25 24 37 24 25 27 31 29 33 32 29 34 24 26 27 27 30 33 31 28 27 25 30
## [433] 26 26 27 26 24 29 29 38 26 27 26 27 31 27 32 31 30 30 35 34 32 29 26 28
## [457] 25 36 27 31 24 27 25 26 31 30 26 31 24 24 29 26 24 24 41 34 26 29 24 24
## [481] 33 25 26 26 30 26 27 25 28 27 33 26 25 24 26 25 31 26 31 27 29 24 27 26
## [505] 30 24 25 29 25 27 27 26 25 29 28 32 34 25 26 29 28 26 28 28 29 28 37 27
## [529] 24 27 29 24 24 24 28 33 39 30 27 26 26 27 25 40 28 27 29 27 25 30 26 27
## [553] 28 25 34 26 34 27 25 33 28 28 29 24 27 24 36 34 26 24 28 25 28 28 34 24
## [577] 29 32 30 24 26 27 33 29 26 25 25 28 32 26 24 26 26 33 28 25 35 25 24 25
## [601] 25 29 28 25 29 25 27 24 31 34 24 33 26 32 25 25 30 24 29 35 25 31 31 31
## [625] 26 27 29 30 28 25 32 25 25 31 29 28 31 30 32 28 28 32 24 26 28 27 25 28
## [649] 29 24 32 30 26 30 26 27 25 38 29 29 33 24 24 26 26 24 31 34 35 29 25 38
## [673] 24 24 25 26 31 26 26 34 32 28 25 25 30 24 29 25 27 26 29 29 30 26 28 30
## [697] 27 24 30 26 28 27 27 25 29 25 26 24 24 26 25 24 28 28 25 24 24 29 25 27
## [721] 27 27 30 27 35 24 25 28 29 24 33 29 31 28 31 27 24 34 26 32 27 27 24 24
## [745] 30 26 25 32 29 25 25 29 26 26 27 25 26 27 27 25 27 26 27 33 27 27 30 31
## [769] 31 27 28 24 27 26 25 31 24 33 28 27 27 29 32 32 24 25 28 28 26 32 32 24
## [793] 24 24 26 32 28 26 28 31 30 27 24 31 25 29 29 25 29 30 29 28 26 29 33 26
## [817] 25 25 24 24 28 26 26 25 24 25 27 25 25 26 29 28 36 31 35 29 25 25 26 29
## [841] 30 29 26 25 30 25 33 31 28 26 29 29 27 25 25 25 25 28 28 28 25 30 28 25
## [865] 25 24 29 27 26 31 26 24 27 25 24 24 28 25 31 24 24 26 31 24 24 31 27 32
## [889] 34 26 28 29 28 25 26 28 26 31 26 39 25 33 31 28 30 28 31 34 27 26 40 26
## [913] 30 28 24 28 25 25 27 28 30 24 30 27 25 25 29 24 27 28 29 32 24 31 27 32
## [937] 30 27 24 26 29 26 32 26 27 31 28 32 32 24 24 25 25 30 24 30 28 29 25 27
## [961] 31 28 24 32 30 25 24 30 25 30 27 24 27 24 37 32 24 30 25 25 25 24 35 24
## [985] 27 30 34 30 27 34 37 25 25 28 25 27 28 34 26 29 28 30 26 31 35 27 30 25
## [1009] 26 26 25 26 26 29 25 28 28 26 29 31 28 26 28 28 28 31 28 25 27 24 29 35
## [1033] 24 26 26 25 30 26 30 25 29 28 32 24 25 25 26 28 24 28 25 31 27 29 25 28
## [1057] 37 27 28 29 24 31 25 29 26 24 32 29 31 24 30 28 26 31 27 27 29 29 27 33
## [1081] 25 26 24 24 27 33 31 28 29 33 26 24 26 30 28 24 24 36 34 27 25 25 28 30
## [1105] 29 26 29 25 25 31 27 24 31 25 25 33 25 27 27 33 30 26 29 28 24 33 27 30
## [1129] 26 24 27 25 30 27 39 27 24 25 33 30 28 30 37 25 26 27 24 27 25 26 25 29
## [1153] 27 25 34 25 29 28 33 29 25 25 24 24 29 24 30 24 28 26 26 24 29 29 29 25
## [1177] 28 32 32 27 30 27 25 24 26 27 26 28 26 25 32 30 26 24 29 27 27 27 31 28
## [1201] 27 25 26 25 24 26 25 33 30 24 24 33 26 30 24 30 24 24 40 32 27 30 28 25
## [1225] 24 28 34 33 34 28 29 26 27 24 33 26 34 29 31 26 26 35 26 26 31 30 24 31
## [1249] 29 33 25 30 28 27 26 24 25 27 32 27 27 26 29 31 34 31 25 27 27 25 24 32
## [1273] 26 33 28 26 25 26 25 25 25 29 27 29 24 31 30 27 28 30 28 27 29 24 24 24
## [1297] 25 24 30 27 29 27 26 26 30 35 27 29 24 29 30 24 27 27 29 27 28 27 30 29
## [1321] 31 27 30 25 28 25 24 34 24 28 34 27 27 27 28 29 30 25 28 33 32 26 26 34
## [1345] 29 32 27 26 24 31 29 34 24 27 25 26 25 26 26 24 29 32 28 33 32 28 27 26
## [1369] 27 24 24 28 29 25 26 26 26 27 24 31 27 31 27 24 25 29 26 27 26 24 26 27
## [1393] 29 27 30 29 30 26 28 25 25 28 24 25 25 29 27 25 24 27 26 26 26 27 25 27
## [1417] 30 34 28 30 25 27 27 26 24 30 25 29 26 31 28 26 26 24 25 35 34 25 34 24
## [1441] 26 27 24 31 34 24 29 29 27 31 29 25 24 26 32 28 24 24 32 24 24 30 24 29
## [1465] 26 26 35 32 25 25 28 37 24 25 26 25 25 27 36 29 32 26 28 24 26 33 24 31
## [1489] 25 25 26 25 36 28 31 26 25 27 31 35 33 24 24 26 32 31 28 26 25 29 33 29
## [1513] 27 29 26 25 27 25 24 27 24 25 25 28 25 37 24 28 30 24 24 26 29 28 31 29
## [1537] 25 24 28 29 25 31 28 33 40 30 27 27 24 31 26 25 29 25 26 35 30 30 27 29
## [1561] 28 25 25 24 28 25 24 27 24 29 28 26 26 24 26 26 26 25 24 24 27 26 37 26
## [1585] 25 27 28 29 31 26 25 36 30 25 24 27 31 24 29 26 27 28 26 25 31 25 28 29
## [1609] 32 25 25 26 27 27 28 34 26 27 27 37 27 27 25 33 24 26 35 29 28 27 36 25
## [1633] 24 25 24 24 31 26 25 26 31 28 24 28 26 28 24 24 26 25 26 24 26 30 32 31
## [1657] 28 33 24 27 34 28 29 34 30 30 25 24 26 27 30 25 27 26 30 26 28 27 27 25
## [1681] 28 25 29 24 25 25 27 25 30 24 27 26 28 24 26 30 25 25 25 25 31 29 29 26
## [1705] 24 29 28 24 25 30 27 28 27 30 28 27 32 28 25 24 25 30 24 27 31 25 25 27
## [1729] 28 32 26 36 27 29 25 29 31 31 25 28 25 32 28 37 31 28 28 24 32 32 25 29
## [1753] 30 34 34 31 27 27 31 25 24 24 25 25 26 37 29 27 27 24 24 26 28 28 30 24
## [1777] 30 25 26 28 26 28 32 29 28 32 26 26 31 25 28 35 27 29 30 32 26 26 28 35
## [1801] 26 24 24 30 24 29 26 33 27 25 30 25 32 27 31 34 25 30 25 25 28 30 30 28
## [1825] 31 25 36 24 28 24 34 26 33 29 31 25 30 25 27 26 24 24 30 25 28 26 26 27
## [1849] 27 24 28 25 26 28 24 26 24 24 33 28 25 24 24 28 26 24 35 25 27 32 24 24
## [1873] 24 24 36 24 29 32 26 27 32 35 28 30 24 25 24 29 24 25 24 29 29 24 30 34
## [1897] 32 25 26 36 32 24 33 27 25 24 24 24 40 24 28 27 25 28 31 26 27 25 24 30
## [1921] 26 28 26 30 28 30 26 32 29 25 26 33 25 26 24 27 25 31 25 24 31 25 30 27
## [1945] 26 24 25 31 26 24 32 26 28 26 25 25 25 33 27 31 33 29 36 31 28 32 28 35
## [1969] 30 28 24 25 25 31 30 29 25 25 26 26 26 27 26 29 29 28 27 32 26 28 32 35
## [1993] 29 28 33 25 38 33 25 25 27 36 28 26 26 42 29 25 27 26 27 25 25 26 29 31
## [2017] 31 25 25 31 27 25 26 33 31 24 37 24 28 26 24 28 29 33 29 31 25 28 26 36
## [2041] 28 29 24 25 28 27 25 35 24 29 24 25 27 26 29 30 33 35 24 31 37 26 24 29
## [2065] 25 25 26 31 24 26 27 30 27 25 24 29 26 29 29 25 28 33 28 24 24 29 32 25
## [2089] 25 31 32 25 25 29 33 29 32 30 29 25 24 28 30 44 27 28 25 31 29 29 24 29
## [2113] 28 32 27 29 35 29 25 25 25 25 25 25 34 35 27 25 25 30 31 35 25 28 33 26
## [2137] 27 30 24 28 25 25 27 28 31 24 25 29 25 30 28 27 24 25 28 26 27 25 33 31
## [2161] 27 24 29 24 25 29 32 24 29 32 25 35 24 34 24 31 24 25 29 25 24 32 28 25
## [2185] 26 33 29 29 27 29 30 25 25 28 34 26 32 28 36 25 33 25 25 24 26 27 24 25
## [2209] 25 27 25 36 28 25 27 24 32 38 27 27 26 30 30 25 24 28 24 30 31 29 24 26
## [2233] 31 29 29 24 30 26 28 27 24 28 25 26 25 29 26 35 33 25 33 30 35 26 30 30
## [2257] 30 31 27 37 29 27 24 36 31 27 30 28 24 24 24 26 27 24 35 27 31 28 30 24
## [2281] 34 27 30 28 32 34 29 28 24 29 26 28 25 26 29 27 33 27 25 26 30 33 25 30
## [2305] 27 31 36 24 29 25 28 29 24 27 28 26 36 24 29 32 32 24 26 31 26 27 26 34
## [2329] 32 33 34 27 36 31 26 27 33 25 26 28 24 27 28 32 31 27 25 25 24 32 29 24
## [2353] 27 36 26 25 28 25 33 32 25 40 24 29 25 24 31 30 31 26 24 29 29 32 29 34
## [2377] 40 29 25 26 26 25 25 31 28 25 25 25 26 28 26 29 27 28 34 30 26 29 25 24
## [2401] 27 29 35 29 27 27 28 24 28 28 24 25 30 28 28 28 24 24 31 28 30 29 25 29
## [2425] 24 24 26 30 28 24 25 25 36 24 25 24 30 33 27 31 24 25 33 25 30 32 24 24
## [2449] 29 27 30 33 30 29 32 25 37 31 32 26 26 26 28 26 24 27 24 31 27 28 30 26
## [2473] 25 27 26 25 25 28 26 31 27 24 24 26 24 31 25 34 33 35 27 35 33 25 28 27
## [2497] 24 27 24 25 31 26 29 28 25 28 28 27 24 29 26 25 26 33 25 25 34 25 27 28
## [2521] 26 26 26 27 24 26 25 24 26 27 32 24 30 27 31 36 27 26 30 24 25 25 24 24
## [2545] 25 27 28 28 34 24 25 32 26 25 26 27 27 30 24 25 24 25 27 30 24 27 30 28
## [2569] 25 26 35 27 28 32 31 24 27 25 27 34 32 29 25 27 26 24 29 27 24 27 29 31
## [2593] 26 26 30 24 24 31 27 31 26 31 29 27 24 34 24 26 30 29 32 25 26 25 29 24
## [2617] 24 31 24 24 24 25 29 25 31 30 28 26 30 24 24 27 25 26 28 31 31 26 25 33
## [2641] 31 25 28 25 28 24 31 25 26 25 34 25 29 25 32 24 28 31 25 24 27 32 26 25
## [2665] 28 27 25 27 27 33 24 27 24 27 30 27 24 25 24 24 25 27 24 35 27 28 28 24
## [2689] 27 26 34 29 24 26 25 26 29 33 27 26 24 28 26 34 26 26 28 27 24 29 26 28
## [2713] 27 24 25 27 24 24 30 31 29 30 29 26 26 26 26 29 26 27 34 26 28 27 37 25
## [2737] 28 31 33 27 24 33 25 24 24 26 28 25 27 24 29 27 27 27 25 27 31 27 25 25
## [2761] 31 26 29 33 25 26 26 32 24 24 30 24 28 29 24 29 24 32 25 24 25 29 24 25
## [2785] 26 28 35 25 31 26 27 25 28 25 30 26 25 30 24 25 29 32 30 28 26 29 27 26
## [2809] 27 24 30 24 26 33 30 25 32 31 33 27 26 28 26 30 27 27 30 32 26 30 29 29
## [2833] 26 30 30 26 27 25 24 24 27 34 31 30 24 30 26 26 25 34 35 30 28 27 33 30
## [2857] 28 24 27 24 34 37 26 24 26 26 25 26 28 27 24 25 26 25 30 26 24 26 28 25
## [2881] 26 25 34 30 35 24 27 24 25 24 24 24 34 31 26 28 24 35 25 30 30 31 26 29
## [2905] 25 31 25 27 24 28 29 27 30 26 31 26 26 25 39 34 25 25 28 36 28 25 28 30
## [2929] 28 29 24 25 27 30 25 28 28 25 24 31 29 25 34 26 31 31 32 33 26 27 24 24
## [2953] 29 25 24 27 27 28 24 34 25 28 24 25 28 28 24 28 26 25 30 30 39 24 25 24
## [2977] 27 27 29 25 33 24 36 33 34 26 29 25 27 26 28 28 25 27 25 24 33 26 31 25
## [3001] 25 29 26 24 26 27 26 30 30 27 24 24 25 24 29 29 33 26 27 30 27 30 28 29
## [3025] 30 26 25 28 31 24 25 29 28 28 31 29 28 29 27 24 24 29 24 26 28 26 30 24
## [3049] 27 29 25 27 32 25 24 28 30 30 29 27 27 32 24 28 26 30 28 34 36 26 25 29
## [3073] 27 26 34 25 33 30 24 31 25 29 25 27 33 24 31 33 35 27 26 28 30 25 30 27
## [3097] 31 29 30 28 26 28 29 25 24 25 28 26 24 35 26 29 26 32 25 32 28 27 36 29
## [3121] 29 29 28 26 26 31 27 28 26 24 27 27 32 34 35 27 26 26 25 35 24 29 25 24
## [3145] 33 27 25 24 28 25 27 27 29 32 24 24 28 27 25 30 28 35 26 31 30 24 27 25
## [3169] 25 26 26 30 32 28 25 32 25 32 30 27 24 27 27 27 31 25 31 28 27 29 28 25
## [3193] 31 24 30 28 24 27 25 26 25 24 26 25 31 30 28 28 35 25 37 25 26 28 24 26
## [3217] 28 26 32 31 29 29 37 29 24 25 26 28 24 32 29 27 30 24 28 30 36 24 27 33
## [3241] 30 24 28 28 34 30 27 24 28 24 27 31 28 32 27 27 27 28 30 25 30 25 29 35
## [3265] 25 31 24 24 27 28 28 24 25 36 26 29 31 27 26 24 29 25 33 29 28 25 31 25
## [3289] 29 30 29 31 25 27 26 28 27 26 28 29 27 27 24 25 29 24 31 26 25 25 24 24
## [3313] 36 29 30 27 24 30 31 35 26 31 25 24 34 24 26 27 27 27 25 33 25 26 35 24
## [3337] 31 29 26 29 25 29 27 26 26 26 30 29 39 29 24 24 24 30 24 27 29 34 32 25
## [3361] 24 29 37 25 25 25 32 30 29 25 25 25 27 27 29 27 27 29 24 30 27 27 25 30
## [3385] 25 33 26 24 25 28 29 33 32 31 24 26 28 29 29 24 25 24 24 26 24 27 28 30
## [3409] 25 29 26 30 37 32 32 27 29 27 24 26 29 27 25 34 26 26 24 26 25 26 31 28
## [3433] 24 27 33 29 26 26 24 25 32 25 32 24 29 27 29 29 30 24 32 29 29 32 28 24
## [3457] 27 29 25 32 24 25 25 24 27 24 27 28 31 35 32 32 32 29 29 34 29 27 29 25
## [3481] 24 28 24 25 25 24 26 24 27 26 33 32 27 31 29 35 31 28 30 32 27 24 30 27
## [3505] 25 30 28 25 26 27 28 27 25 25 29 24 28 30 32 25 26 28 30 24 31 30 28 25
## [3529] 25 33 24 28 30 37 31 25 28 30 26 27 26 25 27 27 30 26 25 28 26 25 26 24
## [3553] 25 27 26 24 28 28 24 27 24 25 31 26 26 31 27 33 29 25 29 31 29 26 26 30
## [3577] 29 27 25 33 30 30 32 24 31 26 24 25 25 24 28 33 25 24 30 24 34 30 26 25
## [3601] 29 26 24 26 32 26 31 24 30 24 28 33 26 25 25 36 25 29 24 28 25 32 26 26
## [3625] 26 31 24 27 30 30 29 24 29 24 26 24 28 27 24 26 30 31 33 34 31 28 27 24
## [3649] 32 28 31 27 35 29 31 24 32 31 30 28 28 26 25 28 29 24 27 27 29 25 24 25
## [3673] 25 26 32 29 27 27 26 33 32 25 29 29 26 35 31 29 33 28 25 25 28 33 27 25
## [3697] 31 26 31 29 25 25 32 27 27 24 27 32 24 28 28 25 28 27 24 39 25 30 30 34
## [3721] 26 25 29 26 28 26 25 27 27 31 24 26 30 25 24 27 28 25 33 29 24 27 27 28
## [3745] 25 24 33 31 29 26 36 29 25 30 28 27 30 24 24 28 27 29 28 26 26 25 26 26
## [3769] 26 30 36 29 25 28 25 26 26 25 26 24 25 28 28 25 25 26 29 26 32 26 32 24
## [3793] 32 29 34 27 27 24 24 34 27 35 29 27 26 27 35 25 29 31 31 31 25 32 24 29
## [3817] 24 26 28 25 28 25 24 34 25 37 25 25 29 30 26 26 25 28 25 25 25 29 26 26
## [3841] 29 28 24 29 27 25 26 27 26 26 32 24 24 26 39 26 32 25 32 26 30 27 26 26
## [3865] 26 27 34 26 33 31 28 25 28 28 27 25 27 28 24 28 24 25 32 29 24 35 25 30
## [3889] 28 28 25 25 31 27 26 26 32 26 28 32 26 26 30 29 30 25 28 28 25 33 29 25
## [3913] 30 34 28 25 28 26 24 28 25 26 28 26 31 34 24 26 29 29 27 25 28 37 26 30
## [3937] 30 29 24 24 32 26 24 28 28 33 31 27 24 25 31 24 26 35 24 30 27 25 27 25
## [3961] 25 26 25 31 28 29 29 28 27 25 26 27 25 26 28 29 27 26 24 31 34 33 24 30
## [3985] 24 24 26 24 36 25 28 30 25 31 24 28 29 24 30 28 29 26 28 25 25 28 33 35
## [4009] 28 25 28 25 27 30 28 33 26 24 29 29 26 29 29 30 25 25 29 26 24 25 27 30
## [4033] 28 28 24 28 37 24 25 29 30 27 34 24 27 28 29 31 26 28 33 26 30 31 34 24
## [4057] 29 25 26 25 25 30 32 25 25 27 32 24 25 25 43 26 31 30 25 27 24 26 27 26
## [4081] 27 30 25 33 24 26 26 25 25 24 24 28 31 31 31 27 25 25 32 26 25 30 26 30
## [4105] 28 31 24 28 25 35 30 30 25 24 27 28 25 32 24 24 24 38 26 30 26 24 27 31
## [4129] 28 30 35 25 34 29 25 33 36 25 28 25 24 31 27 29 29 28 30 28 32 30 30 31
## [4153] 27 24 24 33 30 25 36 28 25 30 26 30 33 27 25 24 24 26 24 31 33 31 25 26
## [4177] 30 27 27 27 24 33 27 30 28 29 25 29 36 26 25 27 28 34 27 31 29 30 27 27
## [4201] 29 25 24 32 30 26 25 26 29 28 25 26 24 27 29 34 32 25 27 26 27 26 27 32
## [4225] 26 24 27 28 29 26 29 24 28 24 25 27 32 28 25 31 25 28 25 26 26 29 32 25
## [4249] 26 25 24 25 31 25 29 32 24 27 27 24 26 30 27 27 25 29 30 25 24 24 27 25
## [4273] 27 28 25 28 30 24 25 38 33 26 24 26 24 29 25 28 28 25 30 27 25 29 27 27
## [4297] 28 25 32 30 25 30 26 26 26 31 24 26 30 26 32 26 25 29 24 24 33 24 30 24
## [4321] 27 37 24 26 29 25 24 25 33 29 28 27 26 24 36 27 27 28 31 24 30 25 30 25
## [4345] 25 26 31 27 26 28 27 26 24 27 27 31 28 29 27 29 33 34 32 27 26 33 26 24
## [4369] 25 29 27 38 32 33 28 27 28 25 24 29 24 24 33 26 36 36 25 26 25 31 24 24
## [4393] 24 27 32 26 27 34 26 27 28 24 33 26 30 31 26 35 31 29 24 25 31 30 27 29
## [4417] 27 28 27 34 27 24 27 26 30 30 26 30 32 32 33 25 24 27 26 27 31 30 31 30
## [4441] 34 25 35 27 31 26 28 35 26 25 25 27 32 27 25 25 38 31 34 29 24 30 24 26
## [4465] 28 28 31 27 33 31 26 25 25 27 24 25 27 29 32 30 25 28 29 25 27 30 24 25
## [4489] 28 28 29 31 32 35 26 25 32 25 29 24 28 24 34 31 36 26 24 30 25 24 30 24
## [4513] 27 31 28 24 25 25 27 24 32 25 27 33 25 27 29 26 26 30 26 33 30 27 29 26
## [4537] 25 24 25 28 33 28 26 26 26 27 24 25 29 26 24 28 24 25 27 26 25 26 24 26
## [4561] 30 24 29 24 29 25 29 31 31 24 32 32 29 31 25 30 30 28 24 24 25 25 32 25
## [4585] 24 24 29 33 31 25 24 33 38 28 31 25 24 31 27 25 24 26 32 26 25 31 24 29
## [4609] 27 26 27 26 27 26 24 24 26 31 29 27 31 26 28 32 29 30 26 25 25 25 33 26
## [4633] 25 30 24 27 25 24 27 27 24 27 27 28 24 28 27 29 32 27 34 26 25 24 27 26
## [4657] 26 28 26 26 26 28 33 28 24 29 27 24 33 26 31 30 29 27 24 24 30 26 29 26
## [4681] 26 30 26 24 24 29 32 25 27 31 28 35 24 26 25 28 27 32 30 32 26 33 30 28
## [4705] 25 26 26 24 31 25 24 31 25 27 28 30 24 25 28 27 24 28 24 27 25 26 25 34
## [4729] 27 26 26 29 37 30 27 26 31 25 24 25 25 24 26 24 29 24 33 29 33 26 27 25
## [4753] 30 30 24 24Handling Invalid value
#Invalid negative/zero value for age column populated for some row i.e. 0, -3
invalid_age_index <-which(data_for_eda$Age < 10)#populating median values for all these rows
Assume age value substituted with median values where invalid.
data_for_eda$Age[invalid_age_index] <-median(data_for_eda$Age,na.rm = T)#Invalid negative/zero value for Income column populated for some row
invalid_income_index <-which(data_for_eda$Income < 0)#populating median values for all these rows
Assume Income value substituted with 0 values where invalid.
data_for_eda$Income[invalid_income_index] <-0Assume card has not been used by these 1023 persons,so substituting NA by 0.
data_for_eda$Avgas.CC.Utilization.in.last.12.months[is.na(data_for_eda$Avgas.CC.Utilization.in.last.12.months)] <- 0Assume No.of.dependents wherever NA,is substituting by 0.
data_for_eda$No.of.dependents[is.na(data_for_eda$No.of.dependents)] <- 0Assume Presence.of.open.home.loan wherever NA,is substituting by 0.
data_for_eda$Presence.of.open.home.loan[is.na(data_for_eda$Presence.of.open.home.loan)] <- 0Assume Outstanding.Balance wherever NA,is substituting by 0 value.
data_for_eda$Outstanding.Balance[is.na(data_for_eda$Outstanding.Balance)] <- 0str(data_for_eda) ## 'data.frame': 69867 obs. of 28 variables:
## $ Performance.Tag : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Age : int 47 53 41 44 53 53 59 31 39 58 ...
## $ Gender : Factor w/ 3 levels "","F","M": 3 3 3 3 3 3 3 2 3 3 ...
## $ Marital.Status..at.the.time.of.application. : Factor w/ 3 levels "","Married","Single": 2 2 2 2 2 2 2 2 2 2 ...
## $ No.of.dependents : num 5 4 1 2 4 4 3 3 4 2 ...
## $ Income : num 25 43 12 43 33 33 44 31 35 31 ...
## $ Education : Factor w/ 6 levels "","Bachelor",..: 3 6 3 2 6 2 3 2 3 6 ...
## $ Profession : Factor w/ 4 levels "","SAL","SE",..: 3 2 3 2 3 2 3 2 2 3 ...
## $ Type.of.residence : Factor w/ 6 levels "","Company provided",..: 6 6 6 5 5 6 6 6 6 5 ...
## $ No.of.months.in.current.residence : int 6 6 6 6 100 78 100 6 6 6 ...
## $ No.of.months.in.current.company : int 23 44 16 15 13 28 10 52 28 24 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : int 1 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : int 2 0 0 0 1 0 1 0 0 0 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ Avgas.CC.Utilization.in.last.12.months : num 47 3 6 12 66 8 11 6 16 8 ...
## $ No.of.trades.opened.in.last.6.months : num 3 1 0 1 2 1 6 1 2 0 ...
## $ No.of.trades.opened.in.last.12.months : int 7 2 0 1 6 2 14 3 2 1 ...
## $ No.of.PL.trades.opened.in.last.6.months : int 2 0 0 0 2 0 1 0 0 0 ...
## $ No.of.PL.trades.opened.in.last.12.months : int 4 0 0 0 3 0 2 1 0 0 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : int 2 0 0 0 2 0 4 1 2 0 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: int 7 0 0 0 4 0 10 1 4 0 ...
## $ Presence.of.open.home.loan : num 0 1 1 0 1 0 0 0 1 0 ...
## $ Outstanding.Balance : num 749114 2955120 2935260 3366 3419174 ...
## $ Total.No.of.Trades : int 8 3 3 2 8 5 21 5 4 3 ...
## $ Presence.of.open.auto.loan : int 0 0 0 0 0 0 0 0 0 0 ...event_col<-c("Performance.Tag")
fact_cols <- c("Gender","Marital.Status..at.the.time.of.application."
,"Education","Profession","Type.of.residence")
numeric_cols<-c('Age','Income','No.of.months.in.current.residence','No.of.months.in.current.company'
,'Total.No.of.Trades','Outstanding.Balance','Avgas.CC.Utilization.in.last.12.months'
,'No.of.times.90.DPD.or.worse.in.last.6.months','No.of.times.60.DPD.or.worse.in.last.6.months','No.of.times.30.DPD.or.worse.in.last.6.months'
,'No.of.times.90.DPD.or.worse.in.last.12.months','No.of.times.60.DPD.or.worse.in.last.12.months','No.of.times.30.DPD.or.worse.in.last.12.months'
,'No.of.trades.opened.in.last.6.months','No.of.trades.opened.in.last.12.months'
,'No.of.PL.trades.opened.in.last.6.months','No.of.PL.trades.opened.in.last.6.months'
,'No.of.Inquiries.in.last.6.months..excluding.home...auto.loans.'
,'No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.'
,'No.of.PL.trades.opened.in.last.12.months','Presence.of.open.home.loan','Presence.of.open.auto.loan')- Explore data by Univariate and Multivariate Analysis
Univariate analysis
out_df<-data.frame(prop.table(table(data_for_eda$Performance.Tag)*100))
ggplot(out_df,aes(x= reorder(Var1,-Freq),Freq))+geom_bar(stat ="identity",col='blue')+ xlab("Performance.Tag") + ylab("Frequency") 
out_df<-data.frame(prop.table(table(data_for_eda$Gender)*100))
ggplot(out_df,aes(x= reorder(Var1,-Freq),Freq))+geom_bar(stat ="identity")+ xlab("Gender") + ylab("Frequency") 
out_df<-data.frame(prop.table(table(data_for_eda$Education)*100))
ggplot(out_df,aes(x= reorder(Var1,-Freq),Freq))+geom_bar(stat ="identity")+ xlab("Education") + ylab("Frequency") 
out_df<-data.frame(prop.table(table(data_for_eda$Profession)*100))
ggplot(out_df,aes(x= reorder(Var1,-Freq),Freq))+geom_bar(stat ="identity")+ xlab("Profession") + ylab("Frequency") 
out_df<-data.frame(prop.table(table(data_for_eda$Marital.Status..at.the.time.of.application.)*100))
ggplot(out_df,aes(x= reorder(Var1,-Freq),Freq))+geom_bar(stat ="identity")+ xlab("Marital.Status") + ylab("Frequency") 
out_df<-data.frame(prop.table(table(data_for_eda$Type.of.residence)*100))
ggplot(out_df,aes(x= reorder(Var1,-Freq),Freq))+geom_bar(stat ="identity")+ xlab("Type.of.residence") + ylab("Frequency")
ggplot(data_for_eda,aes(Age))+geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
boxplot(data_for_eda$Age,horizontal = T) 
Most users are in late 30 to early 50 age range. Some outliers(very small age value, may be invalid) values are present.
hist(data_for_eda$Income, xlab = "Income")
boxplot(data_for_eda$Income,horizontal = T)
Most users are in 15 to 40 income range. Not many outliers found.
ggplot(data_for_eda[which(data_for_eda$Performance.Tag == 1),] , aes(x = Income)) +
geom_density() 
hist(data_for_eda$No.of.months.in.current.company, xlab = "No.of.months.in.current.company")
boxplot(data_for_eda$No.of.months.in.current.company,horizontal = T) 
Most users are new job holders with 0-5yr experience. population size is low in high experience category. Some outliers do exist.
summary(data_for_eda$No.of.times.90.DPD.or.worse.in.last.6.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.249 0.000 3.000hist(data_for_eda$No.of.times.90.DPD.or.worse.in.last.6.months, xlab = "No.of.times.90.DPD.or.worse.in.last.6.months")
boxplot(data_for_eda$No.of.times.90.DPD.or.worse.in.last.6.months,horizontal = T)
Most people have no such overdues. Among the very less people who have 90 days overdue, repeating offenders population size is very very small.
summary(data_for_eda$No.of.times.60.DPD.or.worse.in.last.6.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3917 1.0000 5.0000hist(data_for_eda$No.of.times.60.DPD.or.worse.in.last.6.months, xlab = "No.of.times.60.DPD.or.worse.in.last.6.months")
boxplot(data_for_eda$No.of.times.60.DPD.or.worse.in.last.6.months,horizontal = T)
Most people have no such overdues, repeating offenders population size keep on decreasing with occurances of overdue.compared to 90 days overdues, population size is higher
summary(data_for_eda$No.of.times.30.DPD.or.worse.in.last.6.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.5235 1.0000 7.0000hist(data_for_eda$No.of.times.30.DPD.or.worse.in.last.6.months, xlab = "No.of.times.30.DPD.or.worse.in.last.6.months")
boxplot(data_for_eda$No.of.times.30.DPD.or.worse.in.last.6.months,horizontal = T)
Most people have no such overdues , repeating offenders population size keep on decreasing with occurances of overdue.
summary(data_for_eda$No.of.times.90.DPD.or.worse.in.last.12.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4148 1.0000 5.0000hist(data_for_eda$No.of.times.90.DPD.or.worse.in.last.12.months, xlab = "No.of.times.90.DPD.or.worse.in.last.12.months")
boxplot(data_for_eda$No.of.times.90.DPD.or.worse.in.last.12.months,horizontal = T)
Most people have no such overdues.Among the very less people who have 90 days overdue, repeating offenders population size is very very small
summary(data_for_eda$No.of.times.60.DPD.or.worse.in.last.12.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.6034 1.0000 7.0000hist(data_for_eda$No.of.times.60.DPD.or.worse.in.last.12.months, xlab = "No.of.times.60.DPD.or.worse.in.last.12.months")
boxplot(data_for_eda$No.of.times.60.DPD.or.worse.in.last.12.months,horizontal = T)
Most people have no such overdues ,repeating offenders population size keep on decreasing with occurances of overdue.compared to 90 days overdues, population size is higher
summary(data_for_eda$No.of.times.30.DPD.or.worse.in.last.12.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.7339 1.0000 9.0000hist(data_for_eda$No.of.times.30.DPD.or.worse.in.last.12.months, xlab = "No.of.times.30.DPD.or.worse.in.last.12.months")
boxplot(data_for_eda$No.of.times.30.DPD.or.worse.in.last.12.months,horizontal = T)
Most people have no such overdues, repeating offenders population size keep on decreasing with occurances of overdue.
summary(data_for_eda$Avgas.CC.Utilization.in.last.12.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 8.00 14.00 28.84 44.00 113.00hist(data_for_eda$Avgas.CC.Utilization.in.last.12.months, xlab = "avg cc utilization")
boxplot(data_for_eda$Avgas.CC.Utilization.in.last.12.months,horizontal = T)
Most users are utilizing only upto 20% of card upper limit, population size with proper 25 to 60 % card utilization is similar
Left skewed ..outliers do exist.
ggplot(data_for_eda[which(data_for_eda$Performance.Tag == 1),] , aes(x = Avgas.CC.Utilization.in.last.12.months)) +
geom_density()
summary(data_for_eda$No.of.trades.opened.in.last.6.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 2.000 2.285 3.000 12.000hist(data_for_eda$No.of.trades.opened.in.last.6.months, xlab = "No.of.trades.opened.in.last.6.months")
boxplot(data_for_eda$No.of.trades.opened.in.last.6.months,horizontal = T)
Most users have 0-4 trades opened in last 6 mon. Outlier do exist.
summary(data_for_eda$No.of.trades.opened.in.last.12.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 4.000 5.785 9.000 28.000hist(data_for_eda$No.of.trades.opened.in.last.12.months, xlab = "No.of.trades.opened.in.last.12.months")
boxplot(data_for_eda$No.of.trades.opened.in.last.12.months,horizontal = T)
Most users have 0-10 trades opened in last 12 months. Outlier do exist.
summary(data_for_eda$No.of.PL.trades.opened.in.last.6.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 1.00 1.19 2.00 6.00hist(data_for_eda$No.of.PL.trades.opened.in.last.6.months, xlab = "No.of.PL.trades.opened.in.last.6.months")
boxplot(data_for_eda$No.of.PL.trades.opened.in.last.6.months,horizontal = T)
Most users have 0-3 PL opened in last 12 months. Very few Outlier are there.
summary(data_for_eda$No.of.PL.trades.opened.in.last.12.months)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 2.000 2.363 4.000 12.000hist(data_for_eda$No.of.PL.trades.opened.in.last.12.months, xlab = "No.of.PL.trades.opened.in.last.12.months")
boxplot(data_for_eda$No.of.PL.trades.opened.in.last.12.months,horizontal = T)
Most users have 0-6 trades opened in last 12 mon. Outlier might be there.
summary(data_for_eda$No.of.Inquiries.in.last.6.months..excluding.home...auto.loans.)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 1.758 3.000 10.000hist(data_for_eda$No.of.Inquiries.in.last.6.months..excluding.home...auto.loans.)
boxplot(data_for_eda$No.of.Inquiries.in.last.6.months..excluding.home...auto.loans.,horizontal = T)
Most users have 0-4 trades opened in last 6 months. Outlier might be there.
summary(data_for_eda$No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 3.000 3.525 5.000 20.000hist(data_for_eda$No.of.Inquiries.in.last.12.months..excluding.home...auto.loans., xlab = "Autoloans-6mon")
boxplot(data_for_eda$No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.,horizontal = T)
Most users have 0-5 trades opened in last 12 months. Outlier are present.
str(data_for_eda)## 'data.frame': 69867 obs. of 28 variables:
## $ Performance.Tag : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Age : int 47 53 41 44 53 53 59 31 39 58 ...
## $ Gender : Factor w/ 3 levels "","F","M": 3 3 3 3 3 3 3 2 3 3 ...
## $ Marital.Status..at.the.time.of.application. : Factor w/ 3 levels "","Married","Single": 2 2 2 2 2 2 2 2 2 2 ...
## $ No.of.dependents : num 5 4 1 2 4 4 3 3 4 2 ...
## $ Income : num 25 43 12 43 33 33 44 31 35 31 ...
## $ Education : Factor w/ 6 levels "","Bachelor",..: 3 6 3 2 6 2 3 2 3 6 ...
## $ Profession : Factor w/ 4 levels "","SAL","SE",..: 3 2 3 2 3 2 3 2 2 3 ...
## $ Type.of.residence : Factor w/ 6 levels "","Company provided",..: 6 6 6 5 5 6 6 6 6 5 ...
## $ No.of.months.in.current.residence : int 6 6 6 6 100 78 100 6 6 6 ...
## $ No.of.months.in.current.company : int 23 44 16 15 13 28 10 52 28 24 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : int 1 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : int 2 0 0 0 1 0 1 0 0 0 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : int 2 0 0 0 0 0 1 0 0 0 ...
## $ Avgas.CC.Utilization.in.last.12.months : num 47 3 6 12 66 8 11 6 16 8 ...
## $ No.of.trades.opened.in.last.6.months : num 3 1 0 1 2 1 6 1 2 0 ...
## $ No.of.trades.opened.in.last.12.months : int 7 2 0 1 6 2 14 3 2 1 ...
## $ No.of.PL.trades.opened.in.last.6.months : int 2 0 0 0 2 0 1 0 0 0 ...
## $ No.of.PL.trades.opened.in.last.12.months : int 4 0 0 0 3 0 2 1 0 0 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : int 2 0 0 0 2 0 4 1 2 0 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: int 7 0 0 0 4 0 10 1 4 0 ...
## $ Presence.of.open.home.loan : num 0 1 1 0 1 0 0 0 1 0 ...
## $ Outstanding.Balance : num 749114 2955120 2935260 3366 3419174 ...
## $ Total.No.of.Trades : int 8 3 3 2 8 5 21 5 4 3 ...
## $ Presence.of.open.auto.loan : int 0 0 0 0 0 0 0 0 0 0 ...summary(data_for_eda$Total.No.of.Trades)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 3.000 6.000 8.175 10.000 44.000hist(data_for_eda$Total.No.of.Trades, xlab = "Total.No.of.Trades")
boxplot(data_for_eda$Total.No.of.Trades,horizontal = T)
Most users have 0-10 trades in total. Outlier are there.
summary(data_for_eda$Outstanding.Balance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 209065 774241 1251505 2924628 5218801hist(data_for_eda$Outstanding.Balance, xlab = "Outstanding.Balance")
boxplot(data_for_eda$Outstanding.Balance,names = "Outstanding.Balance",horizontal = T)
0-200000 range higher no of users 300k upwards lower no of users Most users are starting to repay their loans
ggplot(data_for_eda[which(data_for_eda$Performance.Tag == 1),] , aes(x = Outstanding.Balance)) +
geom_density()
2) IV Analysis and WOE
WOE: Predictive power for an independent variable is determined by the amount of evidence available.
IV is a measure of the strength of the relationship between the two parties.
Information Value Predictive Power < 0.02 useless for prediction 0.02 to 0.1 Weak predictor 0.1 to 0.3 Medium predictor 0.3 to 0.5 Strong predictor >0.5 Suspicious or too good to be true
library(Information)
IV <- create_infotables(data=data_for_eda, y="Performance.Tag", bins=10, parallel=T)
IV$Tables$Age## Age N Percent WOE IV
## 1 [15,30] 5928 0.08484692 -0.038431041 0.0001231326
## 2 [31,35] 6927 0.09914552 0.034531539 0.0002432434
## 3 [36,38] 6924 0.09910258 0.069071901 0.0007312939
## 4 [39,41] 7129 0.10203673 0.068297625 0.0012224165
## 5 [42,44] 7007 0.10029055 -0.037941656 0.0013643099
## 6 [45,47] 6850 0.09804343 -0.007010706 0.0013691133
## 7 [48,50] 6743 0.09651194 -0.012629305 0.0013844182
## 8 [51,53] 6841 0.09791461 -0.136905430 0.0031088637
## 9 [54,57] 7619 0.10905005 0.043405263 0.0033184478
## 10 [58,65] 7899 0.11305767 -0.010013410 0.0033297321head(IV)## $Tables
## $Tables$Age
## Age N Percent WOE IV
## 1 [15,30] 5928 0.08484692 -0.038431041 0.0001231326
## 2 [31,35] 6927 0.09914552 0.034531539 0.0002432434
## 3 [36,38] 6924 0.09910258 0.069071901 0.0007312939
## 4 [39,41] 7129 0.10203673 0.068297625 0.0012224165
## 5 [42,44] 7007 0.10029055 -0.037941656 0.0013643099
## 6 [45,47] 6850 0.09804343 -0.007010706 0.0013691133
## 7 [48,50] 6743 0.09651194 -0.012629305 0.0013844182
## 8 [51,53] 6841 0.09791461 -0.136905430 0.0031088637
## 9 [54,57] 7619 0.10905005 0.043405263 0.0033184478
## 10 [58,65] 7899 0.11305767 -0.010013410 0.0033297321
##
## $Tables$Gender
## Gender N Percent WOE IV
## 1 2 2.862582e-05 0.00000000 0.0000000000
## 2 F 16506 2.362489e-01 0.03217430 0.0002481960
## 3 M 53359 7.637225e-01 -0.01010818 0.0003258695
##
## $Tables$Marital.Status..at.the.time.of.application.
## Marital.Status..at.the.time.of.application. N Percent WOE
## 1 6 8.587745e-05 0.000000000
## 2 Married 59544 8.522478e-01 -0.003987254
## 3 Single 10317 1.476663e-01 0.023326708
## IV
## 1 0.000000e+00
## 2 1.352450e-05
## 3 9.473857e-05
##
## $Tables$No.of.dependents
## No.of.dependents N Percent WOE IV
## 1 [1,1] 15218 0.2178138 0.04008522 0.0003564831
## 2 [2,2] 15128 0.2165257 -0.08522163 0.0018691173
## 3 [3,3] 15648 0.2239684 0.05395479 0.0025374631
## 4 [4,4] 11998 0.1717263 -0.02520439 0.0026453041
## 5 [5,5] 11875 0.1699658 0.00439087 0.0026485876
##
## $Tables$Income
## Income N Percent WOE IV
## 1 [0,5] 6330 0.09060071 0.30246890 0.009536858
## 2 [6,10] 6510 0.09317704 0.27575091 0.017587277
## 3 [11,16] 7923 0.11340118 0.06608894 0.018097848
## 4 [17,21] 6803 0.09737072 0.08080252 0.018757634
## 5 [22,26] 6828 0.09772854 0.02506399 0.018819737
## 6 [27,31] 6817 0.09757110 0.07864867 0.019445483
## 7 [32,36] 6830 0.09775717 -0.15595501 0.021660491
## 8 [37,41] 6723 0.09622569 -0.26368117 0.027599526
## 9 [42,48] 7784 0.11141168 -0.17686352 0.030815758
## 10 [49,60] 7319 0.10475618 -0.36078566 0.042410776
##
## $Tables$Education
## Education N Percent WOE IV
## 1 118 0.001688923 0.004760266 3.835474e-08
## 2 Bachelor 17302 0.247641948 0.017313988 7.486628e-05
## 3 Masters 23481 0.336081412 0.007948702 9.617797e-05
## 4 Others 119 0.001703236 0.492621513 6.163509e-04
## 5 Phd 4463 0.063878512 -0.029511963 6.712406e-04
## 6 Professional 24384 0.349005968 -0.017931398 7.825416e-04
##
## $Tables$Profession
## Profession N Percent WOE IV
## 1 13 0.0001860678 0.00000000 0.0000000000
## 2 SAL 39673 0.5678360313 -0.02806688 0.0004416093
## 3 SE 13925 0.1993072552 0.09142405 0.0021790002
## 4 SE_PROF 16256 0.2326706457 -0.01329769 0.0022198934
##
## $Tables$Type.of.residence
## Type.of.residence N Percent WOE IV
## 1 8 0.0001145033 0.000000000 0.0000000000
## 2 Company provided 1603 0.0229435928 0.080146599 0.0001529064
## 3 Living with Parents 1778 0.0254483519 0.067530440 0.0002726155
## 4 Others 198 0.0028339559 -0.530542104 0.0009025812
## 5 Owned 14003 0.2004236621 0.004148595 0.0009060373
## 6 Rented 52277 0.7482359340 -0.004293999 0.0009198065
##
## $Tables$No.of.months.in.current.residence
## No.of.months.in.current.residence N Percent WOE IV
## 1 [6,9] 34694 0.49657206 -0.27219153 0.03253516
## 2 [10,28] 6922 0.09907395 0.49872310 0.06363656
## 3 [29,49] 7210 0.10319607 0.30118432 0.07440068
## 4 [50,72] 6988 0.10001861 0.13401754 0.07631146
## 5 [73,97] 6931 0.09920277 0.13948089 0.07836953
## 6 [98,126] 7122 0.10193654 -0.07705956 0.07895394
##
## $Tables$No.of.months.in.current.company
## No.of.months.in.current.company N Percent WOE IV
## 1 [3,5] 6689 0.09573905 0.09851585 0.0009722477
## 2 [6,12] 6798 0.09729915 0.17548049 0.0042210945
## 3 [13,19] 6933 0.09923140 0.20630691 0.0088669784
## 4 [20,26] 6919 0.09903102 0.03919674 0.0090218882
## 5 [27,33] 7104 0.10167890 -0.08567605 0.0097396575
## 6 [34,40] 7182 0.10279531 0.03079397 0.0098385211
## 7 [41,47] 7217 0.10329626 -0.17614850 0.0127973671
## 8 [48,53] 6169 0.08829633 -0.21792183 0.0165964951
## 9 [54,61] 7824 0.11198420 -0.21640008 0.0213510210
## 10 [62,133] 7032 0.10064837 0.06288591 0.0217607089
##
## $Tables$No.of.times.90.DPD.or.worse.in.last.6.months
## No.of.times.90.DPD.or.worse.in.last.6.months N Percent WOE
## 1 [0,0] 54664 0.7824008 -0.2606781
## 2 [1,3] 15203 0.2175992 0.6224550
## IV
## 1 0.04725916
## 2 0.16010599
##
## $Tables$No.of.times.60.DPD.or.worse.in.last.6.months
## No.of.times.60.DPD.or.worse.in.last.6.months N Percent WOE
## 1 [0,0] 51870 0.7424106 -0.3363664
## 2 [1,5] 17997 0.2575894 0.6225361
## IV
## 1 0.07220016
## 2 0.20582586
##
## $Tables$No.of.times.30.DPD.or.worse.in.last.6.months
## No.of.times.30.DPD.or.worse.in.last.6.months N Percent WOE
## 1 [0,0] 50098 0.7170481 -0.3867918
## 2 [1,1] 9500 0.1359726 0.4643187
## 3 [2,7] 10269 0.1469793 0.7428448
## IV
## 1 0.09018455
## 2 0.12658538
## 3 0.24155115
##
## $Tables$No.of.times.90.DPD.or.worse.in.last.12.months
## No.of.times.90.DPD.or.worse.in.last.12.months N Percent WOE
## 1 [0,0] 50492 0.7226874 -0.3566331
## 2 [1,1] 11663 0.1669315 0.5088234
## 3 [2,5] 7712 0.1103812 0.7219824
## IV
## 1 0.07830347
## 2 0.13311253
## 3 0.21386327
##
## $Tables$No.of.times.60.DPD.or.worse.in.last.12.months
## No.of.times.60.DPD.or.worse.in.last.12.months N Percent WOE
## 1 [0,0] 45868 0.6565045 -0.3519211
## 2 [1,1] 12816 0.1834342 0.2141538
## 3 [2,7] 11183 0.1600613 0.6940858
## IV
## 1 0.06940922
## 2 0.07869700
## 3 0.18548895
##
## $Tables$No.of.times.30.DPD.or.worse.in.last.12.months
## No.of.times.30.DPD.or.worse.in.last.12.months N Percent WOE
## 1 [0,0] 44857 0.6420342 -0.3763960
## 2 [1,2] 17590 0.2517641 0.2805525
## 3 [3,9] 7420 0.1062018 0.7994935
## IV
## 1 0.07681744
## 2 0.09938446
## 3 0.19824100
##
## $Tables$Avgas.CC.Utilization.in.last.12.months
## Avgas.CC.Utilization.in.last.12.months N Percent WOE IV
## 1 [0,4] 6547 0.09370661 -0.5966672 0.02560966
## 2 [5,6] 5471 0.07830592 -0.8015024 0.06103732
## 3 [7,8] 6869 0.09831537 -0.7945231 0.10487322
## 4 [9,10] 6699 0.09588218 -0.7998723 0.14810578
## 5 [11,13] 7689 0.11005196 -0.4848737 0.16894628
## 6 [14,20] 7979 0.11420270 -0.1301929 0.17077072
## 7 [21,36] 7372 0.10551476 0.4244569 0.19393536
## 8 [37,51] 7175 0.10269512 0.5857896 0.24028840
## 9 [52,71] 7017 0.10043368 0.5637310 0.28183324
## 10 [72,113] 7049 0.10089169 0.3813415 0.29935174
##
## $Tables$No.of.trades.opened.in.last.6.months
## No.of.trades.opened.in.last.6.months N Percent WOE IV
## 1 [0,0] 12194 0.17453161 -0.6576285 0.0564612
## 2 [1,1] 20121 0.28799004 -0.4795153 0.1099231
## 3 [2,2] 12117 0.17342952 0.2327739 0.1203882
## 4 [3,3] 9402 0.13456997 0.4351239 0.1515920
## 5 [4,4] 6297 0.09012839 0.5242769 0.1832399
## 6 [5,12] 9736 0.13935048 0.1368556 0.1860197
##
## $Tables$No.of.trades.opened.in.last.12.months
## No.of.trades.opened.in.last.12.months N Percent WOE
## 1 [0,0] 4956 0.07093478 -0.653462151
## 2 [1,1] 11377 0.16283796 -1.019086045
## 3 [2,2] 9323 0.13343925 -0.816468838
## 4 [3,3] 4678 0.06695579 0.003598878
## 5 [4,5] 9397 0.13449840 0.109294271
## 6 [6,7] 8297 0.11875420 0.447981607
## 7 [8,9] 7175 0.10269512 0.571340073
## 8 [10,12] 6699 0.09588218 0.491781025
## 9 [13,28] 7965 0.11400232 0.006306206
## IV
## 1 0.02269765
## 2 0.13168755
## 3 0.19394762
## 4 0.19394849
## 5 0.19563796
## 6 0.22500374
## 7 0.26879653
## 8 0.29796776
## 9 0.29797230
##
## $Tables$No.of.PL.trades.opened.in.last.6.months
## No.of.PL.trades.opened.in.last.6.months N Percent WOE IV
## 1 [0,0] 31080 0.4448452 -0.6492118 0.1407488
## 2 [1,1] 13546 0.1938827 0.1993619 0.1491979
## 3 [2,2] 12565 0.1798417 0.4384356 0.1916027
## 4 [3,6] 12676 0.1814304 0.3619618 0.2197272
##
## $Tables$No.of.PL.trades.opened.in.last.12.months
## No.of.PL.trades.opened.in.last.12.months N Percent WOE
## 1 [0,0] 25824 0.36961656 -0.8938108
## 2 [1,1] 6641 0.09505203 -0.1310168
## 3 [2,2] 6830 0.09775717 0.2513399
## 4 [3,3] 8130 0.11636395 0.4122959
## 5 [4,4] 7903 0.11311492 0.5000753
## 6 [5,5] 6189 0.08858259 0.4261494
## 7 [6,12] 8350 0.11951279 0.2431575
## IV
## 1 0.2002061
## 2 0.2017433
## 3 0.2086806
## 4 0.2326462
## 5 0.2683711
## 6 0.2879895
## 7 0.2958971
##
## $Tables$No.of.Inquiries.in.last.6.months..excluding.home...auto.loans.
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. N
## 1 [0,0] 25069
## 2 [1,1] 13175
## 3 [2,2] 12831
## 4 [3,4] 11506
## 5 [5,10] 7286
## Percent WOE IV
## 1 0.3588103 -0.71823049 0.1349630
## 2 0.1885726 0.17707210 0.1413790
## 3 0.1836489 0.21609676 0.1508557
## 4 0.1646843 0.50980053 0.2051600
## 5 0.1042839 0.01241548 0.2051762
##
## $Tables$No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. N
## 1 [0,0] 20581
## 2 [1,1] 3899
## 3 [2,2] 7907
## 4 [3,3] 8978
## 5 [4,4] 7113
## 6 [5,5] 4927
## 7 [6,8] 8951
## 8 [9,20] 7511
## Percent WOE IV
## 1 0.29457398 -1.06753664 0.2122103
## 2 0.05580603 -0.06177455 0.2124173
## 3 0.11317217 0.14214469 0.2148588
## 4 0.12850130 0.16434931 0.2186030
## 5 0.10180772 0.24810534 0.2256323
## 6 0.07051970 0.58818059 0.2577593
## 7 0.12811485 0.48413154 0.2953973
## 8 0.10750426 0.01370484 0.2954176
##
## $Tables$Presence.of.open.home.loan
## Presence.of.open.home.loan N Percent WOE IV
## 1 [0,0] 51796 0.7413514 0.07177772 0.003947498
## 2 [1,1] 18071 0.2586486 -0.23665793 0.016962772
##
## $Tables$Outstanding.Balance
## Outstanding.Balance N Percent WOE IV
## 1 [0,6881] 6986 0.09998998 -0.7743818 0.04270684
## 2 [6883,26081] 6986 0.09998998 -0.8990054 0.09738248
## 3 [26082,388130] 6987 0.10000429 -0.1344950 0.09908411
## 4 [388140,586657] 6987 0.10000429 0.2529947 0.10628017
## 5 [586671,774228] 6851 0.09805774 0.4581949 0.13176912
## 6 [774241,970891] 7122 0.10193654 0.4191443 0.15353683
## 7 [970893,1355399] 6987 0.10000429 0.3957261 0.17236317
## 8 [1355448,2960641] 6987 0.10000429 -0.3613366 0.18346336
## 9 [2960642,3279050] 6986 0.09998998 -0.8426348 0.23262102
## 10 [3279414,5218801] 6988 0.10001861 0.2970475 0.24274942
##
## $Tables$Total.No.of.Trades
## Total.No.of.Trades N Percent WOE IV
## 1 [0,1] 3914 0.05602073 -0.67304028 0.01885887
## 2 [2,2] 6766 0.09684114 -1.01772550 0.08353841
## 3 [3,3] 8615 0.12330571 -0.70202474 0.12815199
## 4 [4,4] 7490 0.10720369 -0.44785257 0.14575218
## 5 [5,5] 5714 0.08178396 -0.04880056 0.14594265
## 6 [6,6] 4966 0.07107791 0.12930127 0.14720390
## 7 [7,8] 9361 0.13398314 0.37936559 0.17020640
## 8 [9,10] 7133 0.10209398 0.54394026 0.20915717
## 9 [11,19] 8476 0.12131622 0.42717578 0.23616781
## 10 [20,44] 7432 0.10637354 -0.06689796 0.23662955
##
## $Tables$Presence.of.open.auto.loan
## Presence.of.open.auto.loan N Percent WOE IV
## 1 [0,0] 63937 0.91512445 0.01198467 0.0001321651
## 2 [1,1] 5930 0.08487555 -0.13836752 0.0016580606
##
##
## $Summary
## Variable IV
## 17 Avgas.CC.Utilization.in.last.12.months 2.993517e-01
## 19 No.of.trades.opened.in.last.12.months 2.979723e-01
## 21 No.of.PL.trades.opened.in.last.12.months 2.958971e-01
## 23 No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 2.954176e-01
## 25 Outstanding.Balance 2.427494e-01
## 13 No.of.times.30.DPD.or.worse.in.last.6.months 2.415512e-01
## 26 Total.No.of.Trades 2.366296e-01
## 20 No.of.PL.trades.opened.in.last.6.months 2.197272e-01
## 14 No.of.times.90.DPD.or.worse.in.last.12.months 2.138633e-01
## 12 No.of.times.60.DPD.or.worse.in.last.6.months 2.058259e-01
## 22 No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 2.051762e-01
## 16 No.of.times.30.DPD.or.worse.in.last.12.months 1.982410e-01
## 18 No.of.trades.opened.in.last.6.months 1.860197e-01
## 15 No.of.times.60.DPD.or.worse.in.last.12.months 1.854889e-01
## 11 No.of.times.90.DPD.or.worse.in.last.6.months 1.601060e-01
## 9 No.of.months.in.current.residence 7.895394e-02
## 5 Income 4.241078e-02
## 10 No.of.months.in.current.company 2.176071e-02
## 24 Presence.of.open.home.loan 1.696277e-02
## 1 Age 3.329732e-03
## 4 No.of.dependents 2.648588e-03
## 7 Profession 2.219893e-03
## 27 Presence.of.open.auto.loan 1.658061e-03
## 8 Type.of.residence 9.198065e-04
## 6 Education 7.825416e-04
## 2 Gender 3.258695e-04
## 3 Marital.Status..at.the.time.of.application. 9.473857e-05IV_Value = data.frame(IV$Summary)
grid.table(IV$Summary[seq(from=1,to=20,by=1),], rows=NULL)
plotFrame <- IV$Summary[order(-IV$Summary$IV), ]
plotFrame$Variable <- factor(plotFrame$Variable, levels = plotFrame$Variable[order(-plotFrame$IV)])
ggplot(plotFrame, aes(x = Variable, y = IV)) +
geom_bar(width = .35, stat = "identity", color = "red", fill = "yellow") +
ggtitle("WOE:Important variable based on Information Value") +
theme_bw() +
theme(plot.title = element_text(size = 10)) +
theme(axis.text.x = element_text(angle = 90))
plotFrame## Variable IV
## 17 Avgas.CC.Utilization.in.last.12.months 2.993517e-01
## 19 No.of.trades.opened.in.last.12.months 2.979723e-01
## 21 No.of.PL.trades.opened.in.last.12.months 2.958971e-01
## 23 No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 2.954176e-01
## 25 Outstanding.Balance 2.427494e-01
## 13 No.of.times.30.DPD.or.worse.in.last.6.months 2.415512e-01
## 26 Total.No.of.Trades 2.366296e-01
## 20 No.of.PL.trades.opened.in.last.6.months 2.197272e-01
## 14 No.of.times.90.DPD.or.worse.in.last.12.months 2.138633e-01
## 12 No.of.times.60.DPD.or.worse.in.last.6.months 2.058259e-01
## 22 No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 2.051762e-01
## 16 No.of.times.30.DPD.or.worse.in.last.12.months 1.982410e-01
## 18 No.of.trades.opened.in.last.6.months 1.860197e-01
## 15 No.of.times.60.DPD.or.worse.in.last.12.months 1.854889e-01
## 11 No.of.times.90.DPD.or.worse.in.last.6.months 1.601060e-01
## 9 No.of.months.in.current.residence 7.895394e-02
## 5 Income 4.241078e-02
## 10 No.of.months.in.current.company 2.176071e-02
## 24 Presence.of.open.home.loan 1.696277e-02
## 1 Age 3.329732e-03
## 4 No.of.dependents 2.648588e-03
## 7 Profession 2.219893e-03
## 27 Presence.of.open.auto.loan 1.658061e-03
## 8 Type.of.residence 9.198065e-04
## 6 Education 7.825416e-04
## 2 Gender 3.258695e-04
## 3 Marital.Status..at.the.time.of.application. 9.473857e-05From above WOE analysis below parameters show relatively higher significance Variable IV Avgas.CC.Utilization.in.last.12.months 2.993909e-01 No.of.trades.opened.in.last.12.months 2.979855e-01 No.of.PL.trades.opened.in.last.12.months 2.959382e-01 No.of.Inquiries.in.last.12.months..excluding.home…auto.loans. 2.953999e-01 Outstanding.Balance 2.427715e-01 No.of.times.30.DPD.or.worse.in.last.6.months 2.415777e-01 Total.No.of.Trades 2.366340e-01 No.of.PL.trades.opened.in.last.6.months 2.197726e-01 No.of.times.90.DPD.or.worse.in.last.12.months 2.139246e-01 No.of.times.60.DPD.or.worse.in.last.6.months 2.058494e-01 No.of.Inquiries.in.last.6.months..excluding.home…auto.loans. 2.051641e-01 No.of.times.30.DPD.or.worse.in.last.12.months 1.982677e-01 No.of.trades.opened.in.last.6.months 1.860467e-01 No.of.times.60.DPD.or.worse.in.last.12.months 1.855141e-01 No.of.times.90.DPD.or.worse.in.last.6.months 1.601541e-01 No.of.months.in.current.residence 7.896308e-02 Income 4.236710e-02 No.of.months.in.current.company 2.176502e-02 Presence.of.open.home.loan 1.695583e-02
Correlation analysis
library(corrplot)## corrplot 0.90 loadedcor_df<- data_for_eda[,numeric_cols]
corr_index<- cor(cor_df)
corrplot(corr_index, type = "upper", tl.pos = "td",
method = "circle", tl.cex = 0.01, tl.col = 'black',
order = "hclust", diag = FALSE)
colnames(cor_df)## [1] "Age"
## [2] "Income"
## [3] "No.of.months.in.current.residence"
## [4] "No.of.months.in.current.company"
## [5] "Total.No.of.Trades"
## [6] "Outstanding.Balance"
## [7] "Avgas.CC.Utilization.in.last.12.months"
## [8] "No.of.times.90.DPD.or.worse.in.last.6.months"
## [9] "No.of.times.60.DPD.or.worse.in.last.6.months"
## [10] "No.of.times.30.DPD.or.worse.in.last.6.months"
## [11] "No.of.times.90.DPD.or.worse.in.last.12.months"
## [12] "No.of.times.60.DPD.or.worse.in.last.12.months"
## [13] "No.of.times.30.DPD.or.worse.in.last.12.months"
## [14] "No.of.trades.opened.in.last.6.months"
## [15] "No.of.trades.opened.in.last.12.months"
## [16] "No.of.PL.trades.opened.in.last.6.months"
## [17] "No.of.PL.trades.opened.in.last.6.months.1"
## [18] "No.of.Inquiries.in.last.6.months..excluding.home...auto.loans."
## [19] "No.of.Inquiries.in.last.12.months..excluding.home...auto.loans."
## [20] "No.of.PL.trades.opened.in.last.12.months"
## [21] "Presence.of.open.home.loan"
## [22] "Presence.of.open.auto.loan"- Bi/multi-variate analysis
ggplot(data_for_eda, aes(x = Avgas.CC.Utilization.in.last.12.months, y = No.of.PL.trades.opened.in.last.12.months, group=Performance.Tag, color=Performance.Tag))+
geom_line(stat='summary', fun.y='mean') +
geom_point(stat='summary', fun.y='mean')
No of PL-trades opened is relatively higher for default users.
ggplot(data=data_for_eda, aes(x=No.of.times.90.DPD.or.worse.in.last.6.months, y=Avgas.CC.Utilization.in.last.12.months, group=Performance.Tag, color=Performance.Tag))+
geom_line(stat='summary', fun.y='mean') +
geom_point(stat='summary', fun.y='mean')
For default users Avg-CC-utilization is overall higher , Also CC-usage is going high with increasing DPD values.
ggplot(data=data_for_eda, aes(x=Total.No.of.Trades, y=Outstanding.Balance, group=Performance.Tag, color=Performance.Tag))+
geom_line(stat='summary', fun.y='mean') +
geom_point(stat='summary', fun.y='mean')
Total no of trades is overall in higher nos for default users. Also outstanding balance is relatively higher for most of default users.
ggplot(data=data_for_eda, aes(x=Income, y=Outstanding.Balance, group=Performance.Tag, color=Performance.Tag))+
geom_line(stat='summary', fun.y='mean') ## Warning: Ignoring unknown parameters: fun.y
For defaulters Outstanding balance is higher. No upward/downward trend for outstanding balance with increasing income. If outstanding is more than 12.5lakh its a matter of concern.
ggplot(data_for_eda, aes(x = Income, y = Avgas.CC.Utilization.in.last.12.months, group=Performance.Tag, color=Performance.Tag))+
geom_line(stat='summary', fun.y='mean') +
geom_point(stat='summary', fun.y='mean')## Warning: Ignoring unknown parameters: fun.y
## Warning: Ignoring unknown parameters: fun.y
With increasing income avg-cc-usage decreases for whole population. If avg cc usage is >40 for a low income, >30 for middle income, >25 for higher income,they should be looked.
ggplot(data=data_for_eda, aes(x=Income, y=No.of.times.90.DPD.or.worse.in.last.6.months, group=Performance.Tag, color=Performance.Tag))+
geom_line(stat='summary', fun.y='mean') +
geom_point(stat='summary', fun.y='mean') ## Warning: Ignoring unknown parameters: fun.y
## Warning: Ignoring unknown parameters: fun.y
With increasing Income, DPD nos are decreasing. Also for defaulting users DPD nos are way higher. High no of defaulters are in lower to medium income range.
ggplot(data=data_for_eda, aes(x=Income, y=No.of.Inquiries.in.last.12.months..excluding.home...auto.loans., group=Performance.Tag, color=Performance.Tag))+
geom_line(stat='summary', fun.y='mean') +
geom_point(stat='summary', fun.y='mean') ## Warning: Ignoring unknown parameters: fun.y
## Warning: Ignoring unknown parameters: fun.y
With increase in income no of inquiries are decreasing for non defaulters. With increase in income no of inquiries relatively higher for defaulters.
ggplot(data=data_for_eda, aes(x=No.of.dependents, y=Income, group=Performance.Tag, color=Performance.Tag))+
geom_line(stat='summary', fun.y='mean') +
geom_point(stat='summary', fun.y='mean') ## Warning: Ignoring unknown parameters: fun.y
## Warning: Ignoring unknown parameters: fun.y
Income per no of dependants is very low for defaulters compared to non-defaulters.
ggplot(data=data_for_eda, aes(x=No.of.Inquiries.in.last.12.months..excluding.home...auto.loans., y=Total.No.of.Trades , group=Performance.Tag, color=Performance.Tag))+
geom_line(stat='summary', fun.y='mean') +
geom_point(stat='summary', fun.y='mean')## No summary function supplied, defaulting to `mean_se()`
## No summary function supplied, defaulting to `mean_se()`
With increasing no of inquiries in last 12months,total no of trades increases, then gradually it becomes constant.For default users total no of trades is higher.
Scaling numeric columns & creating dummies for factor attributes
table(data_for_eda$Performance.Tag)##
## 0 1
## 66920 2947prop.table(table(data_for_eda$Performance.Tag))##
## 0 1
## 0.95781986 0.04218014Only 4% of observations are under default category. So it is a highly imbalanced data which would result in-effictive models if not treated properly.
Data before scaling
table(data_for_eda$Performance.Tag)##
## 0 1
## 66920 2947Create a copy to be used during Random forest processing.
master_df<- data_for_eda
data_for_scaling<-data.frame(sapply(data_for_eda[numeric_cols], scale))
str(data_for_scaling)## 'data.frame': 69867 obs. of 22 variables:
## $ Age : num 0.201 0.807 -0.405 -0.102 0.807 ...
## $ Income : num -0.156 1.007 -0.996 1.007 0.361 ...
## $ No.of.months.in.current.residence : num -0.776 -0.776 -0.776 -0.776 1.775 ...
## $ No.of.months.in.current.company : num -0.55 0.482 -0.894 -0.943 -1.042 ...
## $ Total.No.of.Trades : num -0.0244 -0.7234 -0.7234 -0.8632 -0.0244 ...
## $ Outstanding.Balance : num -0.391 1.327 1.311 -0.972 1.688 ...
## $ Avgas.CC.Utilization.in.last.12.months : num 0.616 -0.876 -0.774 -0.571 1.26 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : num 1.485 -0.492 -0.492 -0.492 -0.492 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : num 2.084 -0.507 -0.507 -0.507 -0.507 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : num 1.475 -0.523 -0.523 -0.523 -0.523 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : num 2.076 -0.543 -0.543 -0.543 -0.543 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : num 1.367 -0.591 -0.591 -0.591 0.388 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : num 1.02 -0.59 -0.59 -0.59 -0.59 ...
## $ No.of.trades.opened.in.last.6.months : num 0.343 -0.617 -1.098 -0.617 -0.137 ...
## $ No.of.trades.opened.in.last.12.months : num 0.2385 -0.7428 -1.1353 -0.9391 0.0422 ...
## $ No.of.PL.trades.opened.in.last.6.months : num 0.599 -0.879 -0.879 -0.879 0.599 ...
## $ No.of.PL.trades.opened.in.last.6.months.1 : num 0.599 -0.879 -0.879 -0.879 0.599 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : num 0.122 -0.886 -0.886 -0.886 0.122 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: num 0.962 -0.976 -0.976 -0.976 0.132 ...
## $ No.of.PL.trades.opened.in.last.12.months : num 0.675 -0.975 -0.975 -0.975 0.263 ...
## $ Presence.of.open.home.loan : num -0.591 1.693 1.693 -0.591 1.693 ...
## $ Presence.of.open.auto.loan : num -0.305 -0.305 -0.305 -0.305 -0.305 ...data_for_creating_dummies <- data_for_eda[fact_cols]
str(data_for_creating_dummies)## 'data.frame': 69867 obs. of 5 variables:
## $ Gender : Factor w/ 3 levels "","F","M": 3 3 3 3 3 3 3 2 3 3 ...
## $ Marital.Status..at.the.time.of.application.: Factor w/ 3 levels "","Married","Single": 2 2 2 2 2 2 2 2 2 2 ...
## $ Education : Factor w/ 6 levels "","Bachelor",..: 3 6 3 2 6 2 3 2 3 6 ...
## $ Profession : Factor w/ 4 levels "","SAL","SE",..: 3 2 3 2 3 2 3 2 2 3 ...
## $ Type.of.residence : Factor w/ 6 levels "","Company provided",..: 6 6 6 5 5 6 6 6 6 5 ...Create dummy variables for factor attributes
dummies<- data.frame(sapply(data_for_creating_dummies,function(x) data.frame(model.matrix(~x-1,data =data_for_creating_dummies))[,-1])) Combine all relevant columns to build final training data
final_df<- cbind(data_for_eda[event_col],data_for_scaling,dummies)
final_df$Performance.Tag<-as.factor(final_df$Performance.Tag)
str(final_df)## 'data.frame': 69867 obs. of 40 variables:
## $ Performance.Tag : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Age : num 0.201 0.807 -0.405 -0.102 0.807 ...
## $ Income : num -0.156 1.007 -0.996 1.007 0.361 ...
## $ No.of.months.in.current.residence : num -0.776 -0.776 -0.776 -0.776 1.775 ...
## $ No.of.months.in.current.company : num -0.55 0.482 -0.894 -0.943 -1.042 ...
## $ Total.No.of.Trades : num -0.0244 -0.7234 -0.7234 -0.8632 -0.0244 ...
## $ Outstanding.Balance : num -0.391 1.327 1.311 -0.972 1.688 ...
## $ Avgas.CC.Utilization.in.last.12.months : num 0.616 -0.876 -0.774 -0.571 1.26 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : num 1.485 -0.492 -0.492 -0.492 -0.492 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : num 2.084 -0.507 -0.507 -0.507 -0.507 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : num 1.475 -0.523 -0.523 -0.523 -0.523 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : num 2.076 -0.543 -0.543 -0.543 -0.543 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : num 1.367 -0.591 -0.591 -0.591 0.388 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : num 1.02 -0.59 -0.59 -0.59 -0.59 ...
## $ No.of.trades.opened.in.last.6.months : num 0.343 -0.617 -1.098 -0.617 -0.137 ...
## $ No.of.trades.opened.in.last.12.months : num 0.2385 -0.7428 -1.1353 -0.9391 0.0422 ...
## $ No.of.PL.trades.opened.in.last.6.months : num 0.599 -0.879 -0.879 -0.879 0.599 ...
## $ No.of.PL.trades.opened.in.last.6.months.1 : num 0.599 -0.879 -0.879 -0.879 0.599 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : num 0.122 -0.886 -0.886 -0.886 0.122 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: num 0.962 -0.976 -0.976 -0.976 0.132 ...
## $ No.of.PL.trades.opened.in.last.12.months : num 0.675 -0.975 -0.975 -0.975 0.263 ...
## $ Presence.of.open.home.loan : num -0.591 1.693 1.693 -0.591 1.693 ...
## $ Presence.of.open.auto.loan : num -0.305 -0.305 -0.305 -0.305 -0.305 ...
## $ Gender.xF : num 0 0 0 0 0 0 0 1 0 0 ...
## $ Gender.xM : num 1 1 1 1 1 1 1 0 1 1 ...
## $ Marital.Status..at.the.time.of.application..xMarried : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Marital.Status..at.the.time.of.application..xSingle : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Education.xBachelor : num 0 0 0 1 0 1 0 1 0 0 ...
## $ Education.xMasters : num 1 0 1 0 0 0 1 0 1 0 ...
## $ Education.xOthers : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Education.xPhd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Education.xProfessional : num 0 1 0 0 1 0 0 0 0 1 ...
## $ Profession.xSAL : num 0 1 0 1 0 1 0 1 1 0 ...
## $ Profession.xSE : num 1 0 1 0 1 0 1 0 0 1 ...
## $ Profession.xSE_PROF : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xCompany.provided : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xLiving.with.Parents : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xOthers : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xOwned : num 0 0 0 1 1 0 0 0 0 1 ...
## $ Type.of.residence.xRented : num 1 1 1 0 0 1 1 1 1 0 ...Above “final_df” dataset would be used for Logistic and SVM modelling both.
- Model Building
Before going to apply ove/under/synthetic sampling only on training data.We need to devide the main data to train and test data and then apply sampling on the training data only.Otherwise there is a risk of - having unreal synthetic data in the test dataset.
- Split data into train and test
Splitting whole date to separate test data for model evaluation
set.seed(100)
split_indices <- sample.split(final_df, SplitRatio = 7/10)
data_for_sampling <- final_df[split_indices, ]
test<- final_df[!split_indices, ]Model 1: Build Models using Logistic Regression
logistic_1 <- glm(Performance.Tag ~ ., family = "binomial", data = train)
summary(logistic_1)##
## Call:
## glm(formula = Performance.Tag ~ ., family = "binomial", data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.216 -1.081 0.472 1.102 1.867
##
## Coefficients:
## Estimate
## (Intercept) -0.239722
## Age 0.000685
## Income -0.044270
## No.of.months.in.current.residence -0.037134
## No.of.months.in.current.company -0.018606
## Total.No.of.Trades -0.037987
## Outstanding.Balance -0.010765
## Avgas.CC.Utilization.in.last.12.months 0.159113
## No.of.times.90.DPD.or.worse.in.last.6.months 0.027910
## No.of.times.60.DPD.or.worse.in.last.6.months 0.034694
## No.of.times.30.DPD.or.worse.in.last.6.months 0.068250
## No.of.times.90.DPD.or.worse.in.last.12.months 0.062186
## No.of.times.60.DPD.or.worse.in.last.12.months 0.016935
## No.of.times.30.DPD.or.worse.in.last.12.months 0.068526
## No.of.trades.opened.in.last.6.months 0.013929
## No.of.trades.opened.in.last.12.months 0.015436
## No.of.PL.trades.opened.in.last.6.months 0.074882
## No.of.PL.trades.opened.in.last.6.months.1 0.054227
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. -0.012525
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.070729
## No.of.PL.trades.opened.in.last.12.months 0.095725
## Presence.of.open.home.loan -0.039888
## Presence.of.open.auto.loan -0.016884
## Gender.xF 0.033144
## Gender.xM 0.031344
## Marital.Status..at.the.time.of.application..xMarried 0.067735
## Marital.Status..at.the.time.of.application..xSingle 0.129737
## Education.xBachelor -0.006917
## Education.xMasters 0.024754
## Education.xOthers 0.057642
## Education.xPhd -0.054370
## Education.xProfessional 0.011044
## Profession.xSAL -0.063485
## Profession.xSE 0.047221
## Profession.xSE_PROF -0.030536
## Type.of.residence.xCompany.provided 0.058045
## Type.of.residence.xLiving.with.Parents 0.100850
## Type.of.residence.xOthers -0.267911
## Type.of.residence.xOwned 0.005816
## Type.of.residence.xRented 0.015180
## Std. Error
## (Intercept) 0.045663
## Age 0.007674
## Income 0.007792
## No.of.months.in.current.residence 0.007793
## No.of.months.in.current.company 0.007435
## Total.No.of.Trades 0.010945
## Outstanding.Balance 0.009997
## Avgas.CC.Utilization.in.last.12.months 0.007843
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008748
## No.of.times.60.DPD.or.worse.in.last.6.months 0.009402
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009379
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008750
## No.of.times.60.DPD.or.worse.in.last.12.months 0.009142
## No.of.times.30.DPD.or.worse.in.last.12.months 0.009130
## No.of.trades.opened.in.last.6.months 0.010839
## No.of.trades.opened.in.last.12.months 0.011650
## No.of.PL.trades.opened.in.last.6.months 0.010856
## No.of.PL.trades.opened.in.last.6.months.1 0.010821
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 0.009890
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.010264
## No.of.PL.trades.opened.in.last.12.months 0.010957
## Presence.of.open.home.loan 0.009983
## Presence.of.open.auto.loan 0.007704
## Gender.xF 0.022648
## Gender.xM 0.022669
## Marital.Status..at.the.time.of.application..xMarried 0.026977
## Marital.Status..at.the.time.of.application..xSingle 0.026962
## Education.xBachelor 0.019867
## Education.xMasters 0.018620
## Education.xOthers 0.160286
## Education.xPhd 0.032531
## Education.xProfessional 0.018579
## Profession.xSAL 0.018580
## Profession.xSE 0.021219
## Profession.xSE_PROF 0.020762
## Type.of.residence.xCompany.provided 0.051131
## Type.of.residence.xLiving.with.Parents 0.047487
## Type.of.residence.xOthers 0.155072
## Type.of.residence.xOwned 0.022997
## Type.of.residence.xRented 0.021846
## z value
## (Intercept) -5.250
## Age 0.089
## Income -5.681
## No.of.months.in.current.residence -4.765
## No.of.months.in.current.company -2.503
## Total.No.of.Trades -3.471
## Outstanding.Balance -1.077
## Avgas.CC.Utilization.in.last.12.months 20.289
## No.of.times.90.DPD.or.worse.in.last.6.months 3.190
## No.of.times.60.DPD.or.worse.in.last.6.months 3.690
## No.of.times.30.DPD.or.worse.in.last.6.months 7.277
## No.of.times.90.DPD.or.worse.in.last.12.months 7.107
## No.of.times.60.DPD.or.worse.in.last.12.months 1.852
## No.of.times.30.DPD.or.worse.in.last.12.months 7.506
## No.of.trades.opened.in.last.6.months 1.285
## No.of.trades.opened.in.last.12.months 1.325
## No.of.PL.trades.opened.in.last.6.months 6.898
## No.of.PL.trades.opened.in.last.6.months.1 5.011
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. -1.267
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 6.891
## No.of.PL.trades.opened.in.last.12.months 8.736
## Presence.of.open.home.loan -3.996
## Presence.of.open.auto.loan -2.192
## Gender.xF 1.463
## Gender.xM 1.383
## Marital.Status..at.the.time.of.application..xMarried 2.511
## Marital.Status..at.the.time.of.application..xSingle 4.812
## Education.xBachelor -0.348
## Education.xMasters 1.329
## Education.xOthers 0.360
## Education.xPhd -1.671
## Education.xProfessional 0.594
## Profession.xSAL -3.417
## Profession.xSE 2.225
## Profession.xSE_PROF -1.471
## Type.of.residence.xCompany.provided 1.135
## Type.of.residence.xLiving.with.Parents 2.124
## Type.of.residence.xOthers -1.728
## Type.of.residence.xOwned 0.253
## Type.of.residence.xRented 0.695
## Pr(>|z|)
## (Intercept) 1.52e-07 ***
## Age 0.928875
## Income 1.34e-08 ***
## No.of.months.in.current.residence 1.89e-06 ***
## No.of.months.in.current.company 0.012330 *
## Total.No.of.Trades 0.000519 ***
## Outstanding.Balance 0.281542
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 0.001421 **
## No.of.times.60.DPD.or.worse.in.last.6.months 0.000224 ***
## No.of.times.30.DPD.or.worse.in.last.6.months 3.41e-13 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 1.19e-12 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.063982 .
## No.of.times.30.DPD.or.worse.in.last.12.months 6.10e-14 ***
## No.of.trades.opened.in.last.6.months 0.198789
## No.of.trades.opened.in.last.12.months 0.185185
## No.of.PL.trades.opened.in.last.6.months 5.27e-12 ***
## No.of.PL.trades.opened.in.last.6.months.1 5.41e-07 ***
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 0.205327
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 5.54e-12 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Presence.of.open.home.loan 6.45e-05 ***
## Presence.of.open.auto.loan 0.028402 *
## Gender.xF 0.143350
## Gender.xM 0.166767
## Marital.Status..at.the.time.of.application..xMarried 0.012044 *
## Marital.Status..at.the.time.of.application..xSingle 1.50e-06 ***
## Education.xBachelor 0.727718
## Education.xMasters 0.183687
## Education.xOthers 0.719130
## Education.xPhd 0.094653 .
## Education.xProfessional 0.552214
## Profession.xSAL 0.000633 ***
## Profession.xSE 0.026053 *
## Profession.xSE_PROF 0.141361
## Type.of.residence.xCompany.provided 0.256281
## Type.of.residence.xLiving.with.Parents 0.033694 *
## Type.of.residence.xOthers 0.084050 .
## Type.of.residence.xOwned 0.800356
## Type.of.residence.xRented 0.487127
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 63936 on 48869 degrees of freedom
## AIC: 64016
##
## Number of Fisher Scoring iterations: 4Using stepwise algorithm for removing insignificant variables
logistic_2 <- stepAIC(logistic_1, direction = "both")## Start: AIC=64015.86
## Performance.Tag ~ Age + Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Outstanding.Balance +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.60.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.trades.opened.in.last.12.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xBachelor +
## Education.xMasters + Education.xOthers + Education.xPhd +
## Education.xProfessional + Profession.xSAL + Profession.xSE +
## Profession.xSE_PROF + Type.of.residence.xCompany.provided +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers +
## Type.of.residence.xOwned + Type.of.residence.xRented
##
## Df Deviance
## - Age 1 63936
## - Type.of.residence.xOwned 1 63936
## - Education.xBachelor 1 63936
## - Education.xOthers 1 63936
## - Education.xProfessional 1 63936
## - Type.of.residence.xRented 1 63936
## - Outstanding.Balance 1 63937
## - Type.of.residence.xCompany.provided 1 63937
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63937
## - No.of.trades.opened.in.last.6.months 1 63938
## - No.of.trades.opened.in.last.12.months 1 63938
## - Education.xMasters 1 63938
## - Gender.xM 1 63938
## <none> 63936
## - Gender.xF 1 63938
## - Profession.xSE_PROF 1 63938
## - Education.xPhd 1 63939
## - Type.of.residence.xOthers 1 63939
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63939
## - Type.of.residence.xLiving.with.Parents 1 63940
## - Presence.of.open.auto.loan 1 63941
## - Profession.xSE 1 63941
## - No.of.months.in.current.company 1 63942
## - Marital.Status..at.the.time.of.application..xMarried 1 63942
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63946
## - Profession.xSAL 1 63948
## - Total.No.of.Trades 1 63948
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63949
## - Presence.of.open.home.loan 1 63952
## - No.of.months.in.current.residence 1 63959
## - Marital.Status..at.the.time.of.application..xSingle 1 63959
## - No.of.PL.trades.opened.in.last.6.months.1 1 63961
## - Income 1 63968
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63983
## - No.of.PL.trades.opened.in.last.6.months 1 63984
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63986
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63989
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63992
## - No.of.PL.trades.opened.in.last.12.months 1 64012
## - Avgas.CC.Utilization.in.last.12.months 1 64352
## AIC
## - Age 64014
## - Type.of.residence.xOwned 64014
## - Education.xBachelor 64014
## - Education.xOthers 64014
## - Education.xProfessional 64014
## - Type.of.residence.xRented 64014
## - Outstanding.Balance 64015
## - Type.of.residence.xCompany.provided 64015
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64015
## - No.of.trades.opened.in.last.6.months 64016
## - No.of.trades.opened.in.last.12.months 64016
## - Education.xMasters 64016
## - Gender.xM 64016
## <none> 64016
## - Gender.xF 64016
## - Profession.xSE_PROF 64016
## - Education.xPhd 64017
## - Type.of.residence.xOthers 64017
## - No.of.times.60.DPD.or.worse.in.last.12.months 64017
## - Type.of.residence.xLiving.with.Parents 64018
## - Presence.of.open.auto.loan 64019
## - Profession.xSE 64019
## - No.of.months.in.current.company 64020
## - Marital.Status..at.the.time.of.application..xMarried 64020
## - No.of.times.90.DPD.or.worse.in.last.6.months 64024
## - Profession.xSAL 64026
## - Total.No.of.Trades 64026
## - No.of.times.60.DPD.or.worse.in.last.6.months 64027
## - Presence.of.open.home.loan 64030
## - No.of.months.in.current.residence 64037
## - Marital.Status..at.the.time.of.application..xSingle 64037
## - No.of.PL.trades.opened.in.last.6.months.1 64039
## - Income 64046
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64061
## - No.of.PL.trades.opened.in.last.6.months 64062
## - No.of.times.90.DPD.or.worse.in.last.12.months 64064
## - No.of.times.30.DPD.or.worse.in.last.6.months 64067
## - No.of.times.30.DPD.or.worse.in.last.12.months 64070
## - No.of.PL.trades.opened.in.last.12.months 64090
## - Avgas.CC.Utilization.in.last.12.months 64430
##
## Step: AIC=64013.87
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Outstanding.Balance +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.60.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.trades.opened.in.last.12.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xBachelor +
## Education.xMasters + Education.xOthers + Education.xPhd +
## Education.xProfessional + Profession.xSAL + Profession.xSE +
## Profession.xSE_PROF + Type.of.residence.xCompany.provided +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers +
## Type.of.residence.xOwned + Type.of.residence.xRented
##
## Df Deviance
## - Type.of.residence.xOwned 1 63936
## - Education.xBachelor 1 63936
## - Education.xOthers 1 63936
## - Education.xProfessional 1 63936
## - Type.of.residence.xRented 1 63936
## - Outstanding.Balance 1 63937
## - Type.of.residence.xCompany.provided 1 63937
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63937
## - No.of.trades.opened.in.last.6.months 1 63938
## - No.of.trades.opened.in.last.12.months 1 63938
## - Education.xMasters 1 63938
## - Gender.xM 1 63938
## <none> 63936
## - Gender.xF 1 63938
## - Profession.xSE_PROF 1 63938
## - Education.xPhd 1 63939
## - Type.of.residence.xOthers 1 63939
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63939
## + Age 1 63936
## - Type.of.residence.xLiving.with.Parents 1 63940
## - Presence.of.open.auto.loan 1 63941
## - Profession.xSE 1 63941
## - No.of.months.in.current.company 1 63942
## - Marital.Status..at.the.time.of.application..xMarried 1 63942
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63946
## - Profession.xSAL 1 63948
## - Total.No.of.Trades 1 63948
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63949
## - Presence.of.open.home.loan 1 63952
## - No.of.months.in.current.residence 1 63959
## - Marital.Status..at.the.time.of.application..xSingle 1 63959
## - No.of.PL.trades.opened.in.last.6.months.1 1 63961
## - Income 1 63968
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63983
## - No.of.PL.trades.opened.in.last.6.months 1 63984
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63986
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63989
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63992
## - No.of.PL.trades.opened.in.last.12.months 1 64012
## - Avgas.CC.Utilization.in.last.12.months 1 64352
## AIC
## - Type.of.residence.xOwned 64012
## - Education.xBachelor 64012
## - Education.xOthers 64012
## - Education.xProfessional 64012
## - Type.of.residence.xRented 64012
## - Outstanding.Balance 64013
## - Type.of.residence.xCompany.provided 64013
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64013
## - No.of.trades.opened.in.last.6.months 64014
## - No.of.trades.opened.in.last.12.months 64014
## - Education.xMasters 64014
## - Gender.xM 64014
## <none> 64014
## - Gender.xF 64014
## - Profession.xSE_PROF 64014
## - Education.xPhd 64015
## - Type.of.residence.xOthers 64015
## - No.of.times.60.DPD.or.worse.in.last.12.months 64015
## + Age 64016
## - Type.of.residence.xLiving.with.Parents 64016
## - Presence.of.open.auto.loan 64017
## - Profession.xSE 64017
## - No.of.months.in.current.company 64018
## - Marital.Status..at.the.time.of.application..xMarried 64018
## - No.of.times.90.DPD.or.worse.in.last.6.months 64022
## - Profession.xSAL 64024
## - Total.No.of.Trades 64024
## - No.of.times.60.DPD.or.worse.in.last.6.months 64025
## - Presence.of.open.home.loan 64028
## - No.of.months.in.current.residence 64035
## - Marital.Status..at.the.time.of.application..xSingle 64035
## - No.of.PL.trades.opened.in.last.6.months.1 64037
## - Income 64044
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64059
## - No.of.PL.trades.opened.in.last.6.months 64060
## - No.of.times.90.DPD.or.worse.in.last.12.months 64062
## - No.of.times.30.DPD.or.worse.in.last.6.months 64065
## - No.of.times.30.DPD.or.worse.in.last.12.months 64068
## - No.of.PL.trades.opened.in.last.12.months 64088
## - Avgas.CC.Utilization.in.last.12.months 64428
##
## Step: AIC=64011.93
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Outstanding.Balance +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.60.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.trades.opened.in.last.12.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xBachelor +
## Education.xMasters + Education.xOthers + Education.xPhd +
## Education.xProfessional + Profession.xSAL + Profession.xSE +
## Profession.xSE_PROF + Type.of.residence.xCompany.provided +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers +
## Type.of.residence.xRented
##
## Df Deviance
## - Education.xBachelor 1 63936
## - Education.xOthers 1 63936
## - Education.xProfessional 1 63936
## - Type.of.residence.xRented 1 63936
## - Outstanding.Balance 1 63937
## - Type.of.residence.xCompany.provided 1 63937
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63938
## - No.of.trades.opened.in.last.6.months 1 63938
## - No.of.trades.opened.in.last.12.months 1 63938
## - Education.xMasters 1 63938
## - Gender.xM 1 63938
## <none> 63936
## - Gender.xF 1 63938
## - Profession.xSE_PROF 1 63938
## - Education.xPhd 1 63939
## - Type.of.residence.xOthers 1 63939
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63939
## + Type.of.residence.xOwned 1 63936
## + Age 1 63936
## - Type.of.residence.xLiving.with.Parents 1 63940
## - Presence.of.open.auto.loan 1 63941
## - Profession.xSE 1 63941
## - No.of.months.in.current.company 1 63942
## - Marital.Status..at.the.time.of.application..xMarried 1 63942
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63946
## - Profession.xSAL 1 63948
## - Total.No.of.Trades 1 63948
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63950
## - Presence.of.open.home.loan 1 63952
## - No.of.months.in.current.residence 1 63959
## - Marital.Status..at.the.time.of.application..xSingle 1 63959
## - No.of.PL.trades.opened.in.last.6.months.1 1 63961
## - Income 1 63968
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63983
## - No.of.PL.trades.opened.in.last.6.months 1 63984
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63987
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63989
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63992
## - No.of.PL.trades.opened.in.last.12.months 1 64012
## - Avgas.CC.Utilization.in.last.12.months 1 64352
## AIC
## - Education.xBachelor 64010
## - Education.xOthers 64010
## - Education.xProfessional 64010
## - Type.of.residence.xRented 64010
## - Outstanding.Balance 64011
## - Type.of.residence.xCompany.provided 64011
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64012
## - No.of.trades.opened.in.last.6.months 64012
## - No.of.trades.opened.in.last.12.months 64012
## - Education.xMasters 64012
## - Gender.xM 64012
## <none> 64012
## - Gender.xF 64012
## - Profession.xSE_PROF 64012
## - Education.xPhd 64013
## - Type.of.residence.xOthers 64013
## - No.of.times.60.DPD.or.worse.in.last.12.months 64013
## + Type.of.residence.xOwned 64014
## + Age 64014
## - Type.of.residence.xLiving.with.Parents 64014
## - Presence.of.open.auto.loan 64015
## - Profession.xSE 64015
## - No.of.months.in.current.company 64016
## - Marital.Status..at.the.time.of.application..xMarried 64016
## - No.of.times.90.DPD.or.worse.in.last.6.months 64020
## - Profession.xSAL 64022
## - Total.No.of.Trades 64022
## - No.of.times.60.DPD.or.worse.in.last.6.months 64024
## - Presence.of.open.home.loan 64026
## - No.of.months.in.current.residence 64033
## - Marital.Status..at.the.time.of.application..xSingle 64033
## - No.of.PL.trades.opened.in.last.6.months.1 64035
## - Income 64042
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64057
## - No.of.PL.trades.opened.in.last.6.months 64058
## - No.of.times.90.DPD.or.worse.in.last.12.months 64061
## - No.of.times.30.DPD.or.worse.in.last.6.months 64063
## - No.of.times.30.DPD.or.worse.in.last.12.months 64066
## - No.of.PL.trades.opened.in.last.12.months 64086
## - Avgas.CC.Utilization.in.last.12.months 64426
##
## Step: AIC=64010.05
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Outstanding.Balance +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.60.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.trades.opened.in.last.12.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xMasters +
## Education.xOthers + Education.xPhd + Education.xProfessional +
## Profession.xSAL + Profession.xSE + Profession.xSE_PROF +
## Type.of.residence.xCompany.provided + Type.of.residence.xLiving.with.Parents +
## Type.of.residence.xOthers + Type.of.residence.xRented
##
## Df Deviance
## - Education.xOthers 1 63936
## - Type.of.residence.xRented 1 63937
## - Education.xProfessional 1 63937
## - Outstanding.Balance 1 63937
## - Type.of.residence.xCompany.provided 1 63937
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63938
## - No.of.trades.opened.in.last.6.months 1 63938
## - No.of.trades.opened.in.last.12.months 1 63938
## - Gender.xM 1 63938
## <none> 63936
## - Gender.xF 1 63938
## - Profession.xSE_PROF 1 63938
## - Education.xMasters 1 63939
## - Education.xPhd 1 63939
## - Type.of.residence.xOthers 1 63939
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63939
## + Education.xBachelor 1 63936
## + Type.of.residence.xOwned 1 63936
## + Age 1 63936
## - Type.of.residence.xLiving.with.Parents 1 63941
## - Presence.of.open.auto.loan 1 63941
## - Profession.xSE 1 63941
## - No.of.months.in.current.company 1 63942
## - Marital.Status..at.the.time.of.application..xMarried 1 63942
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63946
## - Profession.xSAL 1 63948
## - Total.No.of.Trades 1 63948
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63950
## - Presence.of.open.home.loan 1 63952
## - No.of.months.in.current.residence 1 63959
## - Marital.Status..at.the.time.of.application..xSingle 1 63959
## - No.of.PL.trades.opened.in.last.6.months.1 1 63961
## - Income 1 63968
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63984
## - No.of.PL.trades.opened.in.last.6.months 1 63984
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63987
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63989
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63992
## - No.of.PL.trades.opened.in.last.12.months 1 64013
## - Avgas.CC.Utilization.in.last.12.months 1 64352
## AIC
## - Education.xOthers 64008
## - Type.of.residence.xRented 64009
## - Education.xProfessional 64009
## - Outstanding.Balance 64009
## - Type.of.residence.xCompany.provided 64009
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64010
## - No.of.trades.opened.in.last.6.months 64010
## - No.of.trades.opened.in.last.12.months 64010
## - Gender.xM 64010
## <none> 64010
## - Gender.xF 64010
## - Profession.xSE_PROF 64010
## - Education.xMasters 64011
## - Education.xPhd 64011
## - Type.of.residence.xOthers 64011
## - No.of.times.60.DPD.or.worse.in.last.12.months 64011
## + Education.xBachelor 64012
## + Type.of.residence.xOwned 64012
## + Age 64012
## - Type.of.residence.xLiving.with.Parents 64013
## - Presence.of.open.auto.loan 64013
## - Profession.xSE 64013
## - No.of.months.in.current.company 64014
## - Marital.Status..at.the.time.of.application..xMarried 64014
## - No.of.times.90.DPD.or.worse.in.last.6.months 64018
## - Profession.xSAL 64020
## - Total.No.of.Trades 64020
## - No.of.times.60.DPD.or.worse.in.last.6.months 64022
## - Presence.of.open.home.loan 64024
## - No.of.months.in.current.residence 64031
## - Marital.Status..at.the.time.of.application..xSingle 64031
## - No.of.PL.trades.opened.in.last.6.months.1 64033
## - Income 64040
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64056
## - No.of.PL.trades.opened.in.last.6.months 64056
## - No.of.times.90.DPD.or.worse.in.last.12.months 64059
## - No.of.times.30.DPD.or.worse.in.last.6.months 64061
## - No.of.times.30.DPD.or.worse.in.last.12.months 64064
## - No.of.PL.trades.opened.in.last.12.months 64085
## - Avgas.CC.Utilization.in.last.12.months 64424
##
## Step: AIC=64008.19
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Outstanding.Balance +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.60.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.trades.opened.in.last.12.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xMasters +
## Education.xPhd + Education.xProfessional + Profession.xSAL +
## Profession.xSE + Profession.xSE_PROF + Type.of.residence.xCompany.provided +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers +
## Type.of.residence.xRented
##
## Df Deviance
## - Type.of.residence.xRented 1 63937
## - Education.xProfessional 1 63937
## - Outstanding.Balance 1 63937
## - Type.of.residence.xCompany.provided 1 63937
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63938
## - No.of.trades.opened.in.last.6.months 1 63938
## - No.of.trades.opened.in.last.12.months 1 63938
## - Gender.xM 1 63938
## <none> 63936
## - Gender.xF 1 63938
## - Profession.xSE_PROF 1 63938
## - Education.xMasters 1 63939
## - Education.xPhd 1 63939
## - Type.of.residence.xOthers 1 63939
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63940
## + Education.xOthers 1 63936
## + Education.xBachelor 1 63936
## + Type.of.residence.xOwned 1 63936
## + Age 1 63936
## - Type.of.residence.xLiving.with.Parents 1 63941
## - Presence.of.open.auto.loan 1 63941
## - Profession.xSE 1 63941
## - No.of.months.in.current.company 1 63942
## - Marital.Status..at.the.time.of.application..xMarried 1 63943
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63946
## - Profession.xSAL 1 63948
## - Total.No.of.Trades 1 63948
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63950
## - Presence.of.open.home.loan 1 63952
## - No.of.months.in.current.residence 1 63959
## - Marital.Status..at.the.time.of.application..xSingle 1 63959
## - No.of.PL.trades.opened.in.last.6.months.1 1 63961
## - Income 1 63969
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63984
## - No.of.PL.trades.opened.in.last.6.months 1 63984
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63987
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63989
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63993
## - No.of.PL.trades.opened.in.last.12.months 1 64013
## - Avgas.CC.Utilization.in.last.12.months 1 64352
## AIC
## - Type.of.residence.xRented 64007
## - Education.xProfessional 64007
## - Outstanding.Balance 64007
## - Type.of.residence.xCompany.provided 64007
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64008
## - No.of.trades.opened.in.last.6.months 64008
## - No.of.trades.opened.in.last.12.months 64008
## - Gender.xM 64008
## <none> 64008
## - Gender.xF 64008
## - Profession.xSE_PROF 64008
## - Education.xMasters 64009
## - Education.xPhd 64009
## - Type.of.residence.xOthers 64009
## - No.of.times.60.DPD.or.worse.in.last.12.months 64010
## + Education.xOthers 64010
## + Education.xBachelor 64010
## + Type.of.residence.xOwned 64010
## + Age 64010
## - Type.of.residence.xLiving.with.Parents 64011
## - Presence.of.open.auto.loan 64011
## - Profession.xSE 64011
## - No.of.months.in.current.company 64012
## - Marital.Status..at.the.time.of.application..xMarried 64013
## - No.of.times.90.DPD.or.worse.in.last.6.months 64016
## - Profession.xSAL 64018
## - Total.No.of.Trades 64018
## - No.of.times.60.DPD.or.worse.in.last.6.months 64020
## - Presence.of.open.home.loan 64022
## - No.of.months.in.current.residence 64029
## - Marital.Status..at.the.time.of.application..xSingle 64029
## - No.of.PL.trades.opened.in.last.6.months.1 64031
## - Income 64039
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64054
## - No.of.PL.trades.opened.in.last.6.months 64054
## - No.of.times.90.DPD.or.worse.in.last.12.months 64057
## - No.of.times.30.DPD.or.worse.in.last.6.months 64059
## - No.of.times.30.DPD.or.worse.in.last.12.months 64063
## - No.of.PL.trades.opened.in.last.12.months 64083
## - Avgas.CC.Utilization.in.last.12.months 64422
##
## Step: AIC=64006.65
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Outstanding.Balance +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.60.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.trades.opened.in.last.12.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xMasters +
## Education.xPhd + Education.xProfessional + Profession.xSAL +
## Profession.xSE + Profession.xSE_PROF + Type.of.residence.xCompany.provided +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers
##
## Df Deviance
## - Education.xProfessional 1 63937
## - Type.of.residence.xCompany.provided 1 63938
## - Outstanding.Balance 1 63938
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63938
## - No.of.trades.opened.in.last.6.months 1 63938
## - No.of.trades.opened.in.last.12.months 1 63938
## - Gender.xM 1 63939
## <none> 63937
## - Profession.xSE_PROF 1 63939
## - Gender.xF 1 63939
## - Education.xMasters 1 63939
## - Education.xPhd 1 63939
## - Type.of.residence.xOthers 1 63940
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63940
## + Type.of.residence.xRented 1 63936
## + Education.xOthers 1 63937
## + Education.xBachelor 1 63937
## + Type.of.residence.xOwned 1 63937
## + Age 1 63937
## - Type.of.residence.xLiving.with.Parents 1 63941
## - Presence.of.open.auto.loan 1 63941
## - Profession.xSE 1 63942
## - No.of.months.in.current.company 1 63943
## - Marital.Status..at.the.time.of.application..xMarried 1 63943
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63947
## - Profession.xSAL 1 63948
## - Total.No.of.Trades 1 63949
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63950
## - Presence.of.open.home.loan 1 63953
## - No.of.months.in.current.residence 1 63959
## - Marital.Status..at.the.time.of.application..xSingle 1 63960
## - No.of.PL.trades.opened.in.last.6.months.1 1 63962
## - Income 1 63969
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63984
## - No.of.PL.trades.opened.in.last.6.months 1 63984
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63987
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63990
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63993
## - No.of.PL.trades.opened.in.last.12.months 1 64013
## - Avgas.CC.Utilization.in.last.12.months 1 64352
## AIC
## - Education.xProfessional 64005
## - Type.of.residence.xCompany.provided 64006
## - Outstanding.Balance 64006
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64006
## - No.of.trades.opened.in.last.6.months 64006
## - No.of.trades.opened.in.last.12.months 64006
## - Gender.xM 64007
## <none> 64007
## - Profession.xSE_PROF 64007
## - Gender.xF 64007
## - Education.xMasters 64007
## - Education.xPhd 64007
## - Type.of.residence.xOthers 64008
## - No.of.times.60.DPD.or.worse.in.last.12.months 64008
## + Type.of.residence.xRented 64008
## + Education.xOthers 64009
## + Education.xBachelor 64009
## + Type.of.residence.xOwned 64009
## + Age 64009
## - Type.of.residence.xLiving.with.Parents 64009
## - Presence.of.open.auto.loan 64009
## - Profession.xSE 64010
## - No.of.months.in.current.company 64011
## - Marital.Status..at.the.time.of.application..xMarried 64011
## - No.of.times.90.DPD.or.worse.in.last.6.months 64015
## - Profession.xSAL 64016
## - Total.No.of.Trades 64017
## - No.of.times.60.DPD.or.worse.in.last.6.months 64018
## - Presence.of.open.home.loan 64021
## - No.of.months.in.current.residence 64027
## - Marital.Status..at.the.time.of.application..xSingle 64028
## - No.of.PL.trades.opened.in.last.6.months.1 64030
## - Income 64037
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64052
## - No.of.PL.trades.opened.in.last.6.months 64052
## - No.of.times.90.DPD.or.worse.in.last.12.months 64055
## - No.of.times.30.DPD.or.worse.in.last.6.months 64058
## - No.of.times.30.DPD.or.worse.in.last.12.months 64061
## - No.of.PL.trades.opened.in.last.12.months 64081
## - Avgas.CC.Utilization.in.last.12.months 64420
##
## Step: AIC=64005.3
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Outstanding.Balance +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.60.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.trades.opened.in.last.12.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xMasters +
## Education.xPhd + Profession.xSAL + Profession.xSE + Profession.xSE_PROF +
## Type.of.residence.xCompany.provided + Type.of.residence.xLiving.with.Parents +
## Type.of.residence.xOthers
##
## Df Deviance
## - Type.of.residence.xCompany.provided 1 63938
## - Outstanding.Balance 1 63938
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63939
## - No.of.trades.opened.in.last.6.months 1 63939
## - No.of.trades.opened.in.last.12.months 1 63939
## - Gender.xM 1 63939
## <none> 63937
## - Education.xMasters 1 63939
## - Profession.xSE_PROF 1 63939
## - Gender.xF 1 63939
## - Education.xPhd 1 63940
## - Type.of.residence.xOthers 1 63941
## + Education.xProfessional 1 63937
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63941
## + Type.of.residence.xRented 1 63937
## + Education.xBachelor 1 63937
## + Education.xOthers 1 63937
## + Type.of.residence.xOwned 1 63937
## + Age 1 63937
## - Type.of.residence.xLiving.with.Parents 1 63941
## - Presence.of.open.auto.loan 1 63942
## - Profession.xSE 1 63942
## - No.of.months.in.current.company 1 63944
## - Marital.Status..at.the.time.of.application..xMarried 1 63944
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63947
## - Profession.xSAL 1 63949
## - Total.No.of.Trades 1 63949
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63951
## - Presence.of.open.home.loan 1 63953
## - No.of.months.in.current.residence 1 63960
## - Marital.Status..at.the.time.of.application..xSingle 1 63960
## - No.of.PL.trades.opened.in.last.6.months.1 1 63962
## - Income 1 63970
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63985
## - No.of.PL.trades.opened.in.last.6.months 1 63985
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63988
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63990
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63994
## - No.of.PL.trades.opened.in.last.12.months 1 64014
## - Avgas.CC.Utilization.in.last.12.months 1 64353
## AIC
## - Type.of.residence.xCompany.provided 64004
## - Outstanding.Balance 64004
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64005
## - No.of.trades.opened.in.last.6.months 64005
## - No.of.trades.opened.in.last.12.months 64005
## - Gender.xM 64005
## <none> 64005
## - Education.xMasters 64005
## - Profession.xSE_PROF 64005
## - Gender.xF 64005
## - Education.xPhd 64006
## - Type.of.residence.xOthers 64007
## + Education.xProfessional 64007
## - No.of.times.60.DPD.or.worse.in.last.12.months 64007
## + Type.of.residence.xRented 64007
## + Education.xBachelor 64007
## + Education.xOthers 64007
## + Type.of.residence.xOwned 64007
## + Age 64007
## - Type.of.residence.xLiving.with.Parents 64007
## - Presence.of.open.auto.loan 64008
## - Profession.xSE 64008
## - No.of.months.in.current.company 64010
## - Marital.Status..at.the.time.of.application..xMarried 64010
## - No.of.times.90.DPD.or.worse.in.last.6.months 64013
## - Profession.xSAL 64015
## - Total.No.of.Trades 64015
## - No.of.times.60.DPD.or.worse.in.last.6.months 64017
## - Presence.of.open.home.loan 64019
## - No.of.months.in.current.residence 64026
## - Marital.Status..at.the.time.of.application..xSingle 64026
## - No.of.PL.trades.opened.in.last.6.months.1 64028
## - Income 64036
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64051
## - No.of.PL.trades.opened.in.last.6.months 64051
## - No.of.times.90.DPD.or.worse.in.last.12.months 64054
## - No.of.times.30.DPD.or.worse.in.last.6.months 64056
## - No.of.times.30.DPD.or.worse.in.last.12.months 64060
## - No.of.PL.trades.opened.in.last.12.months 64080
## - Avgas.CC.Utilization.in.last.12.months 64419
##
## Step: AIC=64004.29
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Outstanding.Balance +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.60.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.trades.opened.in.last.12.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xMasters +
## Education.xPhd + Profession.xSAL + Profession.xSE + Profession.xSE_PROF +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers
##
## Df Deviance
## - Outstanding.Balance 1 63939
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63940
## - No.of.trades.opened.in.last.12.months 1 63940
## - No.of.trades.opened.in.last.6.months 1 63940
## - Gender.xM 1 63940
## - Education.xMasters 1 63940
## <none> 63938
## - Gender.xF 1 63940
## - Profession.xSE_PROF 1 63941
## + Type.of.residence.xCompany.provided 1 63937
## - Education.xPhd 1 63942
## - Type.of.residence.xOthers 1 63942
## + Education.xProfessional 1 63938
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63942
## + Education.xBachelor 1 63938
## + Type.of.residence.xRented 1 63938
## + Education.xOthers 1 63938
## + Type.of.residence.xOwned 1 63938
## + Age 1 63938
## - Type.of.residence.xLiving.with.Parents 1 63942
## - Profession.xSE 1 63943
## - Presence.of.open.auto.loan 1 63943
## - No.of.months.in.current.company 1 63945
## - Marital.Status..at.the.time.of.application..xMarried 1 63945
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63948
## - Profession.xSAL 1 63950
## - Total.No.of.Trades 1 63950
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63952
## - Presence.of.open.home.loan 1 63954
## - No.of.months.in.current.residence 1 63961
## - Marital.Status..at.the.time.of.application..xSingle 1 63961
## - No.of.PL.trades.opened.in.last.6.months.1 1 63964
## - Income 1 63971
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63986
## - No.of.PL.trades.opened.in.last.6.months 1 63986
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63989
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63991
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63995
## - No.of.PL.trades.opened.in.last.12.months 1 64015
## - Avgas.CC.Utilization.in.last.12.months 1 64354
## AIC
## - Outstanding.Balance 64003
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64004
## - No.of.trades.opened.in.last.12.months 64004
## - No.of.trades.opened.in.last.6.months 64004
## - Gender.xM 64004
## - Education.xMasters 64004
## <none> 64004
## - Gender.xF 64004
## - Profession.xSE_PROF 64005
## + Type.of.residence.xCompany.provided 64005
## - Education.xPhd 64006
## - Type.of.residence.xOthers 64006
## + Education.xProfessional 64006
## - No.of.times.60.DPD.or.worse.in.last.12.months 64006
## + Education.xBachelor 64006
## + Type.of.residence.xRented 64006
## + Education.xOthers 64006
## + Type.of.residence.xOwned 64006
## + Age 64006
## - Type.of.residence.xLiving.with.Parents 64006
## - Profession.xSE 64007
## - Presence.of.open.auto.loan 64007
## - No.of.months.in.current.company 64009
## - Marital.Status..at.the.time.of.application..xMarried 64009
## - No.of.times.90.DPD.or.worse.in.last.6.months 64012
## - Profession.xSAL 64014
## - Total.No.of.Trades 64014
## - No.of.times.60.DPD.or.worse.in.last.6.months 64016
## - Presence.of.open.home.loan 64018
## - No.of.months.in.current.residence 64025
## - Marital.Status..at.the.time.of.application..xSingle 64025
## - No.of.PL.trades.opened.in.last.6.months.1 64028
## - Income 64035
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64050
## - No.of.PL.trades.opened.in.last.6.months 64050
## - No.of.times.90.DPD.or.worse.in.last.12.months 64053
## - No.of.times.30.DPD.or.worse.in.last.6.months 64055
## - No.of.times.30.DPD.or.worse.in.last.12.months 64059
## - No.of.PL.trades.opened.in.last.12.months 64079
## - Avgas.CC.Utilization.in.last.12.months 64418
##
## Step: AIC=64003.46
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.60.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.trades.opened.in.last.12.months +
## No.of.PL.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months.1 +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xMasters +
## Education.xPhd + Profession.xSAL + Profession.xSE + Profession.xSE_PROF +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers
##
## Df Deviance
## - No.of.trades.opened.in.last.12.months 1 63941
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63941
## - No.of.trades.opened.in.last.6.months 1 63941
## - Gender.xM 1 63941
## - Education.xMasters 1 63941
## <none> 63939
## - Gender.xF 1 63942
## - Profession.xSE_PROF 1 63942
## + Outstanding.Balance 1 63938
## + Type.of.residence.xCompany.provided 1 63938
## - Type.of.residence.xOthers 1 63943
## - Education.xPhd 1 63943
## + Education.xProfessional 1 63939
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63943
## + Education.xBachelor 1 63939
## + Type.of.residence.xRented 1 63939
## + Education.xOthers 1 63939
## + Type.of.residence.xOwned 1 63939
## + Age 1 63939
## - Type.of.residence.xLiving.with.Parents 1 63943
## - Profession.xSE 1 63944
## - Presence.of.open.auto.loan 1 63945
## - No.of.months.in.current.company 1 63946
## - Marital.Status..at.the.time.of.application..xMarried 1 63946
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63950
## - Profession.xSAL 1 63951
## - Total.No.of.Trades 1 63952
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63953
## - No.of.months.in.current.residence 1 63962
## - Marital.Status..at.the.time.of.application..xSingle 1 63963
## - No.of.PL.trades.opened.in.last.6.months.1 1 63964
## - Income 1 63972
## - Presence.of.open.home.loan 1 63974
## - No.of.PL.trades.opened.in.last.6.months 1 63987
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63987
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63990
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63992
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63996
## - No.of.PL.trades.opened.in.last.12.months 1 64015
## - Avgas.CC.Utilization.in.last.12.months 1 64355
## AIC
## - No.of.trades.opened.in.last.12.months 64003
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64003
## - No.of.trades.opened.in.last.6.months 64003
## - Gender.xM 64003
## - Education.xMasters 64003
## <none> 64003
## - Gender.xF 64004
## - Profession.xSE_PROF 64004
## + Outstanding.Balance 64004
## + Type.of.residence.xCompany.provided 64004
## - Type.of.residence.xOthers 64005
## - Education.xPhd 64005
## + Education.xProfessional 64005
## - No.of.times.60.DPD.or.worse.in.last.12.months 64005
## + Education.xBachelor 64005
## + Type.of.residence.xRented 64005
## + Education.xOthers 64005
## + Type.of.residence.xOwned 64005
## + Age 64005
## - Type.of.residence.xLiving.with.Parents 64005
## - Profession.xSE 64006
## - Presence.of.open.auto.loan 64007
## - No.of.months.in.current.company 64008
## - Marital.Status..at.the.time.of.application..xMarried 64008
## - No.of.times.90.DPD.or.worse.in.last.6.months 64012
## - Profession.xSAL 64013
## - Total.No.of.Trades 64014
## - No.of.times.60.DPD.or.worse.in.last.6.months 64015
## - No.of.months.in.current.residence 64024
## - Marital.Status..at.the.time.of.application..xSingle 64025
## - No.of.PL.trades.opened.in.last.6.months.1 64026
## - Income 64034
## - Presence.of.open.home.loan 64036
## - No.of.PL.trades.opened.in.last.6.months 64049
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64049
## - No.of.times.90.DPD.or.worse.in.last.12.months 64052
## - No.of.times.30.DPD.or.worse.in.last.6.months 64054
## - No.of.times.30.DPD.or.worse.in.last.12.months 64058
## - No.of.PL.trades.opened.in.last.12.months 64077
## - Avgas.CC.Utilization.in.last.12.months 64417
##
## Step: AIC=64003.02
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.60.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xMasters +
## Education.xPhd + Profession.xSAL + Profession.xSE + Profession.xSE_PROF +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers
##
## Df Deviance
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63942
## - Gender.xM 1 63943
## - Education.xMasters 1 63943
## <none> 63941
## - Gender.xF 1 63943
## - Profession.xSE_PROF 1 63943
## + No.of.trades.opened.in.last.12.months 1 63939
## - No.of.trades.opened.in.last.6.months 1 63943
## + Outstanding.Balance 1 63940
## + Type.of.residence.xCompany.provided 1 63940
## - Education.xPhd 1 63944
## - Type.of.residence.xOthers 1 63944
## + Education.xProfessional 1 63940
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63944
## + Education.xBachelor 1 63941
## + Type.of.residence.xRented 1 63941
## + Education.xOthers 1 63941
## + Type.of.residence.xOwned 1 63941
## + Age 1 63941
## - Type.of.residence.xLiving.with.Parents 1 63945
## - Profession.xSE 1 63946
## - Presence.of.open.auto.loan 1 63946
## - No.of.months.in.current.company 1 63947
## - Marital.Status..at.the.time.of.application..xMarried 1 63947
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63951
## - Total.No.of.Trades 1 63952
## - Profession.xSAL 1 63952
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63955
## - No.of.months.in.current.residence 1 63964
## - Marital.Status..at.the.time.of.application..xSingle 1 63964
## - No.of.PL.trades.opened.in.last.6.months.1 1 63968
## - Income 1 63974
## - Presence.of.open.home.loan 1 63976
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63990
## - No.of.PL.trades.opened.in.last.6.months 1 63991
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63992
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63994
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63997
## - No.of.PL.trades.opened.in.last.12.months 1 64024
## - Avgas.CC.Utilization.in.last.12.months 1 64357
## AIC
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64002
## - Gender.xM 64003
## - Education.xMasters 64003
## <none> 64003
## - Gender.xF 64003
## - Profession.xSE_PROF 64003
## + No.of.trades.opened.in.last.12.months 64003
## - No.of.trades.opened.in.last.6.months 64003
## + Outstanding.Balance 64004
## + Type.of.residence.xCompany.provided 64004
## - Education.xPhd 64004
## - Type.of.residence.xOthers 64004
## + Education.xProfessional 64004
## - No.of.times.60.DPD.or.worse.in.last.12.months 64004
## + Education.xBachelor 64005
## + Type.of.residence.xRented 64005
## + Education.xOthers 64005
## + Type.of.residence.xOwned 64005
## + Age 64005
## - Type.of.residence.xLiving.with.Parents 64005
## - Profession.xSE 64006
## - Presence.of.open.auto.loan 64006
## - No.of.months.in.current.company 64007
## - Marital.Status..at.the.time.of.application..xMarried 64007
## - No.of.times.90.DPD.or.worse.in.last.6.months 64011
## - Total.No.of.Trades 64012
## - Profession.xSAL 64012
## - No.of.times.60.DPD.or.worse.in.last.6.months 64015
## - No.of.months.in.current.residence 64024
## - Marital.Status..at.the.time.of.application..xSingle 64024
## - No.of.PL.trades.opened.in.last.6.months.1 64028
## - Income 64034
## - Presence.of.open.home.loan 64036
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64050
## - No.of.PL.trades.opened.in.last.6.months 64051
## - No.of.times.90.DPD.or.worse.in.last.12.months 64052
## - No.of.times.30.DPD.or.worse.in.last.6.months 64054
## - No.of.times.30.DPD.or.worse.in.last.12.months 64057
## - No.of.PL.trades.opened.in.last.12.months 64084
## - Avgas.CC.Utilization.in.last.12.months 64417
##
## Step: AIC=64002.37
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.60.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Gender.xM + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xMasters +
## Education.xPhd + Profession.xSAL + Profession.xSE + Profession.xSE_PROF +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers
##
## Df Deviance
## - Gender.xM 1 63944
## - Education.xMasters 1 63944
## <none> 63942
## - Gender.xF 1 63945
## - Profession.xSE_PROF 1 63945
## - No.of.trades.opened.in.last.6.months 1 63945
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63941
## + No.of.trades.opened.in.last.12.months 1 63941
## + Outstanding.Balance 1 63941
## + Type.of.residence.xCompany.provided 1 63941
## - Education.xPhd 1 63946
## - Type.of.residence.xOthers 1 63946
## + Education.xProfessional 1 63942
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63946
## + Education.xBachelor 1 63942
## + Type.of.residence.xRented 1 63942
## + Education.xOthers 1 63942
## + Type.of.residence.xOwned 1 63942
## + Age 1 63942
## - Type.of.residence.xLiving.with.Parents 1 63946
## - Profession.xSE 1 63947
## - Presence.of.open.auto.loan 1 63947
## - No.of.months.in.current.company 1 63949
## - Marital.Status..at.the.time.of.application..xMarried 1 63949
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63952
## - Profession.xSAL 1 63954
## - Total.No.of.Trades 1 63954
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63956
## - No.of.months.in.current.residence 1 63965
## - Marital.Status..at.the.time.of.application..xSingle 1 63966
## - No.of.PL.trades.opened.in.last.6.months.1 1 63969
## - Income 1 63975
## - Presence.of.open.home.loan 1 63977
## - No.of.PL.trades.opened.in.last.6.months 1 63992
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63993
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63994
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63995
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 63999
## - No.of.PL.trades.opened.in.last.12.months 1 64025
## - Avgas.CC.Utilization.in.last.12.months 1 64359
## AIC
## - Gender.xM 64002
## - Education.xMasters 64002
## <none> 64002
## - Gender.xF 64003
## - Profession.xSE_PROF 64003
## - No.of.trades.opened.in.last.6.months 64003
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64003
## + No.of.trades.opened.in.last.12.months 64003
## + Outstanding.Balance 64003
## + Type.of.residence.xCompany.provided 64003
## - Education.xPhd 64004
## - Type.of.residence.xOthers 64004
## + Education.xProfessional 64004
## - No.of.times.60.DPD.or.worse.in.last.12.months 64004
## + Education.xBachelor 64004
## + Type.of.residence.xRented 64004
## + Education.xOthers 64004
## + Type.of.residence.xOwned 64004
## + Age 64004
## - Type.of.residence.xLiving.with.Parents 64004
## - Profession.xSE 64005
## - Presence.of.open.auto.loan 64005
## - No.of.months.in.current.company 64007
## - Marital.Status..at.the.time.of.application..xMarried 64007
## - No.of.times.90.DPD.or.worse.in.last.6.months 64010
## - Profession.xSAL 64012
## - Total.No.of.Trades 64012
## - No.of.times.60.DPD.or.worse.in.last.6.months 64014
## - No.of.months.in.current.residence 64023
## - Marital.Status..at.the.time.of.application..xSingle 64024
## - No.of.PL.trades.opened.in.last.6.months.1 64027
## - Income 64033
## - Presence.of.open.home.loan 64035
## - No.of.PL.trades.opened.in.last.6.months 64050
## - No.of.times.90.DPD.or.worse.in.last.12.months 64051
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64052
## - No.of.times.30.DPD.or.worse.in.last.6.months 64053
## - No.of.times.30.DPD.or.worse.in.last.12.months 64057
## - No.of.PL.trades.opened.in.last.12.months 64083
## - Avgas.CC.Utilization.in.last.12.months 64417
##
## Step: AIC=64002.27
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.60.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Gender.xF + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xMasters +
## Education.xPhd + Profession.xSAL + Profession.xSE + Profession.xSE_PROF +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers
##
## Df Deviance
## - Gender.xF 1 63945
## - Education.xMasters 1 63946
## <none> 63944
## + Gender.xM 1 63942
## - Profession.xSE_PROF 1 63947
## - No.of.trades.opened.in.last.6.months 1 63947
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63943
## + No.of.trades.opened.in.last.12.months 1 63943
## + Outstanding.Balance 1 63943
## + Type.of.residence.xCompany.provided 1 63943
## - Education.xPhd 1 63947
## - Type.of.residence.xOthers 1 63948
## + Education.xProfessional 1 63944
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63948
## + Education.xBachelor 1 63944
## + Type.of.residence.xRented 1 63944
## + Education.xOthers 1 63944
## + Type.of.residence.xOwned 1 63944
## + Age 1 63944
## - Type.of.residence.xLiving.with.Parents 1 63948
## - Profession.xSE 1 63949
## - Presence.of.open.auto.loan 1 63949
## - No.of.months.in.current.company 1 63950
## - Marital.Status..at.the.time.of.application..xMarried 1 63951
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63954
## - Profession.xSAL 1 63956
## - Total.No.of.Trades 1 63956
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63958
## - No.of.months.in.current.residence 1 63967
## - Marital.Status..at.the.time.of.application..xSingle 1 63967
## - No.of.PL.trades.opened.in.last.6.months.1 1 63971
## - Income 1 63977
## - Presence.of.open.home.loan 1 63979
## - No.of.PL.trades.opened.in.last.6.months 1 63994
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63994
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63996
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63997
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 64001
## - No.of.PL.trades.opened.in.last.12.months 1 64027
## - Avgas.CC.Utilization.in.last.12.months 1 64360
## AIC
## - Gender.xF 64001
## - Education.xMasters 64002
## <none> 64002
## + Gender.xM 64002
## - Profession.xSE_PROF 64003
## - No.of.trades.opened.in.last.6.months 64003
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64003
## + No.of.trades.opened.in.last.12.months 64003
## + Outstanding.Balance 64003
## + Type.of.residence.xCompany.provided 64003
## - Education.xPhd 64003
## - Type.of.residence.xOthers 64004
## + Education.xProfessional 64004
## - No.of.times.60.DPD.or.worse.in.last.12.months 64004
## + Education.xBachelor 64004
## + Type.of.residence.xRented 64004
## + Education.xOthers 64004
## + Type.of.residence.xOwned 64004
## + Age 64004
## - Type.of.residence.xLiving.with.Parents 64004
## - Profession.xSE 64005
## - Presence.of.open.auto.loan 64005
## - No.of.months.in.current.company 64006
## - Marital.Status..at.the.time.of.application..xMarried 64007
## - No.of.times.90.DPD.or.worse.in.last.6.months 64010
## - Profession.xSAL 64012
## - Total.No.of.Trades 64012
## - No.of.times.60.DPD.or.worse.in.last.6.months 64014
## - No.of.months.in.current.residence 64023
## - Marital.Status..at.the.time.of.application..xSingle 64023
## - No.of.PL.trades.opened.in.last.6.months.1 64027
## - Income 64033
## - Presence.of.open.home.loan 64035
## - No.of.PL.trades.opened.in.last.6.months 64050
## - No.of.times.90.DPD.or.worse.in.last.12.months 64050
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64052
## - No.of.times.30.DPD.or.worse.in.last.6.months 64053
## - No.of.times.30.DPD.or.worse.in.last.12.months 64057
## - No.of.PL.trades.opened.in.last.12.months 64083
## - Avgas.CC.Utilization.in.last.12.months 64416
##
## Step: AIC=64000.85
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.60.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xMasters +
## Education.xPhd + Profession.xSAL + Profession.xSE + Profession.xSE_PROF +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers
##
## Df Deviance
## - Education.xMasters 1 63947
## <none> 63945
## - Profession.xSE_PROF 1 63947
## - No.of.trades.opened.in.last.6.months 1 63947
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63944
## + No.of.trades.opened.in.last.12.months 1 63944
## + Outstanding.Balance 1 63944
## + Type.of.residence.xCompany.provided 1 63944
## - Education.xPhd 1 63948
## - Type.of.residence.xOthers 1 63948
## + Education.xProfessional 1 63944
## + Gender.xF 1 63944
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63948
## + Education.xBachelor 1 63944
## + Gender.xM 1 63945
## + Type.of.residence.xRented 1 63945
## + Education.xOthers 1 63945
## + Type.of.residence.xOwned 1 63945
## + Age 1 63945
## - Type.of.residence.xLiving.with.Parents 1 63949
## - Profession.xSE 1 63950
## - Presence.of.open.auto.loan 1 63950
## - No.of.months.in.current.company 1 63951
## - Marital.Status..at.the.time.of.application..xMarried 1 63951
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63955
## - Profession.xSAL 1 63956
## - Total.No.of.Trades 1 63957
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63959
## - No.of.months.in.current.residence 1 63968
## - Marital.Status..at.the.time.of.application..xSingle 1 63968
## - No.of.PL.trades.opened.in.last.6.months.1 1 63971
## - Income 1 63977
## - Presence.of.open.home.loan 1 63979
## - No.of.PL.trades.opened.in.last.6.months 1 63995
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63995
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63997
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63998
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 64001
## - No.of.PL.trades.opened.in.last.12.months 1 64027
## - Avgas.CC.Utilization.in.last.12.months 1 64361
## AIC
## - Education.xMasters 64001
## <none> 64001
## - Profession.xSE_PROF 64001
## - No.of.trades.opened.in.last.6.months 64001
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64002
## + No.of.trades.opened.in.last.12.months 64002
## + Outstanding.Balance 64002
## + Type.of.residence.xCompany.provided 64002
## - Education.xPhd 64002
## - Type.of.residence.xOthers 64002
## + Education.xProfessional 64002
## + Gender.xF 64002
## - No.of.times.60.DPD.or.worse.in.last.12.months 64002
## + Education.xBachelor 64002
## + Gender.xM 64003
## + Type.of.residence.xRented 64003
## + Education.xOthers 64003
## + Type.of.residence.xOwned 64003
## + Age 64003
## - Type.of.residence.xLiving.with.Parents 64003
## - Profession.xSE 64004
## - Presence.of.open.auto.loan 64004
## - No.of.months.in.current.company 64005
## - Marital.Status..at.the.time.of.application..xMarried 64005
## - No.of.times.90.DPD.or.worse.in.last.6.months 64009
## - Profession.xSAL 64010
## - Total.No.of.Trades 64011
## - No.of.times.60.DPD.or.worse.in.last.6.months 64013
## - No.of.months.in.current.residence 64022
## - Marital.Status..at.the.time.of.application..xSingle 64022
## - No.of.PL.trades.opened.in.last.6.months.1 64025
## - Income 64031
## - Presence.of.open.home.loan 64033
## - No.of.PL.trades.opened.in.last.6.months 64049
## - No.of.times.90.DPD.or.worse.in.last.12.months 64049
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64051
## - No.of.times.30.DPD.or.worse.in.last.6.months 64052
## - No.of.times.30.DPD.or.worse.in.last.12.months 64055
## - No.of.PL.trades.opened.in.last.12.months 64081
## - Avgas.CC.Utilization.in.last.12.months 64415
##
## Step: AIC=64000.84
## Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Total.No.of.Trades + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.60.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
## Marital.Status..at.the.time.of.application..xSingle + Education.xPhd +
## Profession.xSAL + Profession.xSE + Profession.xSE_PROF +
## Type.of.residence.xLiving.with.Parents + Type.of.residence.xOthers
##
## Df Deviance
## <none> 63947
## + Education.xMasters 1 63945
## - Profession.xSE_PROF 1 63949
## - No.of.trades.opened.in.last.6.months 1 63949
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 1 63945
## + No.of.trades.opened.in.last.12.months 1 63946
## + Outstanding.Balance 1 63946
## + Education.xBachelor 1 63946
## + Type.of.residence.xCompany.provided 1 63946
## - Type.of.residence.xOthers 1 63950
## + Gender.xF 1 63946
## - No.of.times.60.DPD.or.worse.in.last.12.months 1 63950
## + Gender.xM 1 63947
## + Type.of.residence.xRented 1 63947
## - Education.xPhd 1 63951
## + Education.xOthers 1 63947
## + Education.xProfessional 1 63947
## + Type.of.residence.xOwned 1 63947
## + Age 1 63947
## - Type.of.residence.xLiving.with.Parents 1 63951
## - Profession.xSE 1 63952
## - Presence.of.open.auto.loan 1 63952
## - No.of.months.in.current.company 1 63953
## - Marital.Status..at.the.time.of.application..xMarried 1 63953
## - No.of.times.90.DPD.or.worse.in.last.6.months 1 63957
## - Profession.xSAL 1 63958
## - Total.No.of.Trades 1 63959
## - No.of.times.60.DPD.or.worse.in.last.6.months 1 63961
## - No.of.months.in.current.residence 1 63970
## - Marital.Status..at.the.time.of.application..xSingle 1 63970
## - No.of.PL.trades.opened.in.last.6.months.1 1 63973
## - Income 1 63979
## - Presence.of.open.home.loan 1 63981
## - No.of.PL.trades.opened.in.last.6.months 1 63996
## - No.of.times.90.DPD.or.worse.in.last.12.months 1 63997
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1 63999
## - No.of.times.30.DPD.or.worse.in.last.6.months 1 63999
## - No.of.times.30.DPD.or.worse.in.last.12.months 1 64003
## - No.of.PL.trades.opened.in.last.12.months 1 64030
## - Avgas.CC.Utilization.in.last.12.months 1 64363
## AIC
## <none> 64001
## + Education.xMasters 64001
## - Profession.xSE_PROF 64001
## - No.of.trades.opened.in.last.6.months 64001
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. 64001
## + No.of.trades.opened.in.last.12.months 64002
## + Outstanding.Balance 64002
## + Education.xBachelor 64002
## + Type.of.residence.xCompany.provided 64002
## - Type.of.residence.xOthers 64002
## + Gender.xF 64002
## - No.of.times.60.DPD.or.worse.in.last.12.months 64002
## + Gender.xM 64003
## + Type.of.residence.xRented 64003
## - Education.xPhd 64003
## + Education.xOthers 64003
## + Education.xProfessional 64003
## + Type.of.residence.xOwned 64003
## + Age 64003
## - Type.of.residence.xLiving.with.Parents 64003
## - Profession.xSE 64004
## - Presence.of.open.auto.loan 64004
## - No.of.months.in.current.company 64005
## - Marital.Status..at.the.time.of.application..xMarried 64005
## - No.of.times.90.DPD.or.worse.in.last.6.months 64009
## - Profession.xSAL 64010
## - Total.No.of.Trades 64011
## - No.of.times.60.DPD.or.worse.in.last.6.months 64013
## - No.of.months.in.current.residence 64022
## - Marital.Status..at.the.time.of.application..xSingle 64022
## - No.of.PL.trades.opened.in.last.6.months.1 64025
## - Income 64031
## - Presence.of.open.home.loan 64033
## - No.of.PL.trades.opened.in.last.6.months 64048
## - No.of.times.90.DPD.or.worse.in.last.12.months 64049
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 64051
## - No.of.times.30.DPD.or.worse.in.last.6.months 64051
## - No.of.times.30.DPD.or.worse.in.last.12.months 64055
## - No.of.PL.trades.opened.in.last.12.months 64082
## - Avgas.CC.Utilization.in.last.12.months 64415logistic_3<- glm(Performance.Tag ~ Income + No.of.months.in.current.residence +
No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.60.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
Education.xOthers + Profession.xSE + Type.of.residence.xCompany.provided +
Type.of.residence.xOwned
, family = "binomial", data = train)
summary(logistic_3)##
## Call:
## glm(formula = Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.60.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months.1 + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
## Education.xOthers + Profession.xSE + Type.of.residence.xCompany.provided +
## Type.of.residence.xOwned, family = "binomial", data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2623 -1.0822 0.4572 1.1014 1.8494
##
## Coefficients:
## Estimate
## (Intercept) -0.144725
## Income -0.044903
## No.of.months.in.current.residence -0.035608
## No.of.months.in.current.company -0.018660
## Avgas.CC.Utilization.in.last.12.months 0.161467
## No.of.times.90.DPD.or.worse.in.last.6.months 0.027696
## No.of.times.60.DPD.or.worse.in.last.6.months 0.034828
## No.of.times.30.DPD.or.worse.in.last.6.months 0.069073
## No.of.times.90.DPD.or.worse.in.last.12.months 0.062865
## No.of.times.60.DPD.or.worse.in.last.12.months 0.017948
## No.of.times.30.DPD.or.worse.in.last.12.months 0.070217
## No.of.trades.opened.in.last.6.months 0.006751
## No.of.PL.trades.opened.in.last.6.months 0.071499
## No.of.PL.trades.opened.in.last.6.months.1 0.051232
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.059153
## No.of.PL.trades.opened.in.last.12.months 0.089197
## Presence.of.open.home.loan -0.045697
## Presence.of.open.auto.loan -0.017815
## Marital.Status..at.the.time.of.application..xMarried -0.014862
## Education.xOthers 0.052065
## Profession.xSE 0.081666
## Type.of.residence.xCompany.provided 0.036578
## Type.of.residence.xOwned -0.004135
## Std. Error
## (Intercept) 0.021024
## Income 0.007777
## No.of.months.in.current.residence 0.007773
## No.of.months.in.current.company 0.007425
## Avgas.CC.Utilization.in.last.12.months 0.007807
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008740
## No.of.times.60.DPD.or.worse.in.last.6.months 0.009395
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009367
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008736
## No.of.times.60.DPD.or.worse.in.last.12.months 0.009128
## No.of.times.30.DPD.or.worse.in.last.12.months 0.009111
## No.of.trades.opened.in.last.6.months 0.010242
## No.of.PL.trades.opened.in.last.6.months 0.010697
## No.of.PL.trades.opened.in.last.6.months.1 0.010648
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.009016
## No.of.PL.trades.opened.in.last.12.months 0.010419
## Presence.of.open.home.loan 0.007862
## Presence.of.open.auto.loan 0.007692
## Marital.Status..at.the.time.of.application..xMarried 0.020954
## Education.xOthers 0.159682
## Profession.xSE 0.018269
## Type.of.residence.xCompany.provided 0.049408
## Type.of.residence.xOwned 0.018718
## z value
## (Intercept) -6.884
## Income -5.774
## No.of.months.in.current.residence -4.581
## No.of.months.in.current.company -2.513
## Avgas.CC.Utilization.in.last.12.months 20.682
## No.of.times.90.DPD.or.worse.in.last.6.months 3.169
## No.of.times.60.DPD.or.worse.in.last.6.months 3.707
## No.of.times.30.DPD.or.worse.in.last.6.months 7.374
## No.of.times.90.DPD.or.worse.in.last.12.months 7.196
## No.of.times.60.DPD.or.worse.in.last.12.months 1.966
## No.of.times.30.DPD.or.worse.in.last.12.months 7.707
## No.of.trades.opened.in.last.6.months 0.659
## No.of.PL.trades.opened.in.last.6.months 6.684
## No.of.PL.trades.opened.in.last.6.months.1 4.811
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 6.561
## No.of.PL.trades.opened.in.last.12.months 8.561
## Presence.of.open.home.loan -5.813
## Presence.of.open.auto.loan -2.316
## Marital.Status..at.the.time.of.application..xMarried -0.709
## Education.xOthers 0.326
## Profession.xSE 4.470
## Type.of.residence.xCompany.provided 0.740
## Type.of.residence.xOwned -0.221
## Pr(>|z|)
## (Intercept) 5.83e-12 ***
## Income 7.76e-09 ***
## No.of.months.in.current.residence 4.63e-06 ***
## No.of.months.in.current.company 0.01197 *
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 0.00153 **
## No.of.times.60.DPD.or.worse.in.last.6.months 0.00021 ***
## No.of.times.30.DPD.or.worse.in.last.6.months 1.66e-13 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 6.21e-13 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.04927 *
## No.of.times.30.DPD.or.worse.in.last.12.months 1.29e-14 ***
## No.of.trades.opened.in.last.6.months 0.50984
## No.of.PL.trades.opened.in.last.6.months 2.32e-11 ***
## No.of.PL.trades.opened.in.last.6.months.1 1.50e-06 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 5.35e-11 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Presence.of.open.home.loan 6.15e-09 ***
## Presence.of.open.auto.loan 0.02055 *
## Marital.Status..at.the.time.of.application..xMarried 0.47816
## Education.xOthers 0.74438
## Profession.xSE 7.81e-06 ***
## Type.of.residence.xCompany.provided 0.45911
## Type.of.residence.xOwned 0.82515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64004 on 48886 degrees of freedom
## AIC: 64050
##
## Number of Fisher Scoring iterations: 4vif(logistic_3)## Income
## 1.045350
## No.of.months.in.current.residence
## 1.055062
## No.of.months.in.current.company
## 1.015072
## Avgas.CC.Utilization.in.last.12.months
## 1.102501
## No.of.times.90.DPD.or.worse.in.last.6.months
## 1.728517
## No.of.times.60.DPD.or.worse.in.last.6.months
## 1.960186
## No.of.times.30.DPD.or.worse.in.last.6.months
## 1.933515
## No.of.times.90.DPD.or.worse.in.last.12.months
## 1.685530
## No.of.times.60.DPD.or.worse.in.last.12.months
## 1.833274
## No.of.times.30.DPD.or.worse.in.last.12.months
## 1.849108
## No.of.trades.opened.in.last.6.months
## 1.779419
## No.of.PL.trades.opened.in.last.6.months
## 1.990759
## No.of.PL.trades.opened.in.last.6.months.1
## 1.983199
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.
## 1.333917
## No.of.PL.trades.opened.in.last.12.months
## 1.789181
## Presence.of.open.home.loan
## 1.011409
## Presence.of.open.auto.loan
## 1.001747
## Marital.Status..at.the.time.of.application..xMarried
## 1.001079
## Education.xOthers
## 1.000419
## Profession.xSE
## 1.003997
## Type.of.residence.xCompany.provided
## 1.005964
## Type.of.residence.xOwned
## 1.003647Removing due to high vif value - No.of.PL.trades.opened.in.last.6.months.1
logistic_4<- glm(Performance.Tag ~ Income + No.of.months.in.current.residence +
No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.60.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
Education.xOthers + Profession.xSE + Type.of.residence.xCompany.provided +
Type.of.residence.xOwned
, family = "binomial", data = train)
summary(logistic_4)##
## Call:
## glm(formula = Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.60.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
## Education.xOthers + Profession.xSE + Type.of.residence.xCompany.provided +
## Type.of.residence.xOwned, family = "binomial", data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2828 -1.0837 0.4558 1.1029 1.8521
##
## Coefficients:
## Estimate
## (Intercept) -0.144313
## Income -0.045351
## No.of.months.in.current.residence -0.034803
## No.of.months.in.current.company -0.019005
## Avgas.CC.Utilization.in.last.12.months 0.162728
## No.of.times.90.DPD.or.worse.in.last.6.months 0.027818
## No.of.times.60.DPD.or.worse.in.last.6.months 0.035044
## No.of.times.30.DPD.or.worse.in.last.6.months 0.069257
## No.of.times.90.DPD.or.worse.in.last.12.months 0.063648
## No.of.times.60.DPD.or.worse.in.last.12.months 0.018385
## No.of.times.30.DPD.or.worse.in.last.12.months 0.070796
## No.of.trades.opened.in.last.6.months 0.017591
## No.of.PL.trades.opened.in.last.6.months 0.090861
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.061861
## No.of.PL.trades.opened.in.last.12.months 0.100082
## Presence.of.open.home.loan -0.045997
## Presence.of.open.auto.loan -0.017997
## Marital.Status..at.the.time.of.application..xMarried -0.014530
## Education.xOthers 0.053918
## Profession.xSE 0.081569
## Type.of.residence.xCompany.provided 0.037880
## Type.of.residence.xOwned -0.004025
## Std. Error
## (Intercept) 0.021019
## Income 0.007775
## No.of.months.in.current.residence 0.007769
## No.of.months.in.current.company 0.007423
## Avgas.CC.Utilization.in.last.12.months 0.007802
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008739
## No.of.times.60.DPD.or.worse.in.last.6.months 0.009393
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009365
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008733
## No.of.times.60.DPD.or.worse.in.last.12.months 0.009126
## No.of.times.30.DPD.or.worse.in.last.12.months 0.009109
## No.of.trades.opened.in.last.6.months 0.009989
## No.of.PL.trades.opened.in.last.6.months 0.009913
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008996
## No.of.PL.trades.opened.in.last.12.months 0.010170
## Presence.of.open.home.loan 0.007859
## Presence.of.open.auto.loan 0.007690
## Marital.Status..at.the.time.of.application..xMarried 0.020949
## Education.xOthers 0.159631
## Profession.xSE 0.018265
## Type.of.residence.xCompany.provided 0.049399
## Type.of.residence.xOwned 0.018713
## z value
## (Intercept) -6.866
## Income -5.833
## No.of.months.in.current.residence -4.480
## No.of.months.in.current.company -2.560
## Avgas.CC.Utilization.in.last.12.months 20.857
## No.of.times.90.DPD.or.worse.in.last.6.months 3.183
## No.of.times.60.DPD.or.worse.in.last.6.months 3.731
## No.of.times.30.DPD.or.worse.in.last.6.months 7.395
## No.of.times.90.DPD.or.worse.in.last.12.months 7.288
## No.of.times.60.DPD.or.worse.in.last.12.months 2.015
## No.of.times.30.DPD.or.worse.in.last.12.months 7.772
## No.of.trades.opened.in.last.6.months 1.761
## No.of.PL.trades.opened.in.last.6.months 9.166
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 6.877
## No.of.PL.trades.opened.in.last.12.months 9.841
## Presence.of.open.home.loan -5.853
## Presence.of.open.auto.loan -2.340
## Marital.Status..at.the.time.of.application..xMarried -0.694
## Education.xOthers 0.338
## Profession.xSE 4.466
## Type.of.residence.xCompany.provided 0.767
## Type.of.residence.xOwned -0.215
## Pr(>|z|)
## (Intercept) 6.61e-12 ***
## Income 5.44e-09 ***
## No.of.months.in.current.residence 7.47e-06 ***
## No.of.months.in.current.company 0.010456 *
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 0.001456 **
## No.of.times.60.DPD.or.worse.in.last.6.months 0.000191 ***
## No.of.times.30.DPD.or.worse.in.last.6.months 1.41e-13 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 3.14e-13 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.043940 *
## No.of.times.30.DPD.or.worse.in.last.12.months 7.71e-15 ***
## No.of.trades.opened.in.last.6.months 0.078248 .
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 6.13e-12 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Presence.of.open.home.loan 4.83e-09 ***
## Presence.of.open.auto.loan 0.019265 *
## Marital.Status..at.the.time.of.application..xMarried 0.487933
## Education.xOthers 0.735537
## Profession.xSE 7.97e-06 ***
## Type.of.residence.xCompany.provided 0.443191
## Type.of.residence.xOwned 0.829682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64028 on 48887 degrees of freedom
## AIC: 64072
##
## Number of Fisher Scoring iterations: 4Removing due to high value , relatively lower significance - No.of.times.60.DPD.or.worse.in.last.6.months
logistic_5<- glm(Performance.Tag ~ Income + No.of.months.in.current.residence +
No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
Education.xOthers + Profession.xSE + Type.of.residence.xCompany.provided +
Type.of.residence.xOwned
, family = "binomial", data = train)
summary(logistic_5)##
## Call:
## glm(formula = Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
## Education.xOthers + Profession.xSE + Type.of.residence.xCompany.provided +
## Type.of.residence.xOwned, family = "binomial", data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2794 -1.0844 0.4624 1.1029 1.8664
##
## Coefficients:
## Estimate
## (Intercept) -0.143597
## Income -0.045951
## No.of.months.in.current.residence -0.035318
## No.of.months.in.current.company -0.019248
## Avgas.CC.Utilization.in.last.12.months 0.163351
## No.of.times.90.DPD.or.worse.in.last.6.months 0.034092
## No.of.times.30.DPD.or.worse.in.last.6.months 0.076972
## No.of.times.90.DPD.or.worse.in.last.12.months 0.067597
## No.of.times.60.DPD.or.worse.in.last.12.months 0.025807
## No.of.times.30.DPD.or.worse.in.last.12.months 0.076351
## No.of.trades.opened.in.last.6.months 0.017831
## No.of.PL.trades.opened.in.last.6.months 0.091040
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.061587
## No.of.PL.trades.opened.in.last.12.months 0.100950
## Presence.of.open.home.loan -0.045831
## Presence.of.open.auto.loan -0.018115
## Marital.Status..at.the.time.of.application..xMarried -0.015180
## Education.xOthers 0.054142
## Profession.xSE 0.081745
## Type.of.residence.xCompany.provided 0.039747
## Type.of.residence.xOwned -0.003999
## Std. Error
## (Intercept) 0.021015
## Income 0.007771
## No.of.months.in.current.residence 0.007766
## No.of.months.in.current.company 0.007422
## Avgas.CC.Utilization.in.last.12.months 0.007800
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008574
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009134
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008668
## No.of.times.60.DPD.or.worse.in.last.12.months 0.008904
## No.of.times.30.DPD.or.worse.in.last.12.months 0.008985
## No.of.trades.opened.in.last.6.months 0.009988
## No.of.PL.trades.opened.in.last.6.months 0.009911
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008994
## No.of.PL.trades.opened.in.last.12.months 0.010166
## Presence.of.open.home.loan 0.007858
## Presence.of.open.auto.loan 0.007688
## Marital.Status..at.the.time.of.application..xMarried 0.020944
## Education.xOthers 0.159678
## Profession.xSE 0.018262
## Type.of.residence.xCompany.provided 0.049389
## Type.of.residence.xOwned 0.018710
## z value
## (Intercept) -6.833
## Income -5.913
## No.of.months.in.current.residence -4.548
## No.of.months.in.current.company -2.593
## Avgas.CC.Utilization.in.last.12.months 20.942
## No.of.times.90.DPD.or.worse.in.last.6.months 3.976
## No.of.times.30.DPD.or.worse.in.last.6.months 8.427
## No.of.times.90.DPD.or.worse.in.last.12.months 7.798
## No.of.times.60.DPD.or.worse.in.last.12.months 2.898
## No.of.times.30.DPD.or.worse.in.last.12.months 8.497
## No.of.trades.opened.in.last.6.months 1.785
## No.of.PL.trades.opened.in.last.6.months 9.185
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 6.847
## No.of.PL.trades.opened.in.last.12.months 9.930
## Presence.of.open.home.loan -5.833
## Presence.of.open.auto.loan -2.356
## Marital.Status..at.the.time.of.application..xMarried -0.725
## Education.xOthers 0.339
## Profession.xSE 4.476
## Type.of.residence.xCompany.provided 0.805
## Type.of.residence.xOwned -0.214
## Pr(>|z|)
## (Intercept) 8.31e-12 ***
## Income 3.36e-09 ***
## No.of.months.in.current.residence 5.43e-06 ***
## No.of.months.in.current.company 0.00950 **
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 7.00e-05 ***
## No.of.times.30.DPD.or.worse.in.last.6.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 6.27e-15 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.00375 **
## No.of.times.30.DPD.or.worse.in.last.12.months < 2e-16 ***
## No.of.trades.opened.in.last.6.months 0.07423 .
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 7.53e-12 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Presence.of.open.home.loan 5.46e-09 ***
## Presence.of.open.auto.loan 0.01846 *
## Marital.Status..at.the.time.of.application..xMarried 0.46859
## Education.xOthers 0.73456
## Profession.xSE 7.60e-06 ***
## Type.of.residence.xCompany.provided 0.42095
## Type.of.residence.xOwned 0.83077
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64041 on 48888 degrees of freedom
## AIC: 64083
##
## Number of Fisher Scoring iterations: 4logistic_6<- glm(Performance.Tag ~ Income + No.of.months.in.current.residence +
No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
Education.xOthers + Profession.xSE +
Type.of.residence.xOwned
, family = "binomial", data = train)
summary(logistic_6)##
## Call:
## glm(formula = Performance.Tag ~ Income + No.of.months.in.current.residence +
## No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
## Education.xOthers + Profession.xSE + Type.of.residence.xOwned,
## family = "binomial", data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.283 -1.084 0.461 1.103 1.867
##
## Coefficients:
## Estimate
## (Intercept) -0.142310
## Income -0.045893
## No.of.months.in.current.residence -0.035356
## No.of.months.in.current.company -0.019314
## Avgas.CC.Utilization.in.last.12.months 0.163365
## No.of.times.90.DPD.or.worse.in.last.6.months 0.034073
## No.of.times.30.DPD.or.worse.in.last.6.months 0.076943
## No.of.times.90.DPD.or.worse.in.last.12.months 0.067598
## No.of.times.60.DPD.or.worse.in.last.12.months 0.025802
## No.of.times.30.DPD.or.worse.in.last.12.months 0.076383
## No.of.trades.opened.in.last.6.months 0.017871
## No.of.PL.trades.opened.in.last.6.months 0.091062
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.061614
## No.of.PL.trades.opened.in.last.12.months 0.100881
## Presence.of.open.home.loan -0.045851
## Presence.of.open.auto.loan -0.018199
## Marital.Status..at.the.time.of.application..xMarried -0.015279
## Education.xOthers 0.054283
## Profession.xSE 0.081010
## Type.of.residence.xOwned -0.004764
## Std. Error
## (Intercept) 0.020954
## Income 0.007771
## No.of.months.in.current.residence 0.007766
## No.of.months.in.current.company 0.007421
## Avgas.CC.Utilization.in.last.12.months 0.007800
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008574
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009134
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008668
## No.of.times.60.DPD.or.worse.in.last.12.months 0.008904
## No.of.times.30.DPD.or.worse.in.last.12.months 0.008985
## No.of.trades.opened.in.last.6.months 0.009988
## No.of.PL.trades.opened.in.last.6.months 0.009911
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008994
## No.of.PL.trades.opened.in.last.12.months 0.010165
## Presence.of.open.home.loan 0.007858
## Presence.of.open.auto.loan 0.007688
## Marital.Status..at.the.time.of.application..xMarried 0.020944
## Education.xOthers 0.159681
## Profession.xSE 0.018240
## Type.of.residence.xOwned 0.018686
## z value
## (Intercept) -6.792
## Income -5.906
## No.of.months.in.current.residence -4.553
## No.of.months.in.current.company -2.602
## Avgas.CC.Utilization.in.last.12.months 20.944
## No.of.times.90.DPD.or.worse.in.last.6.months 3.974
## No.of.times.30.DPD.or.worse.in.last.6.months 8.424
## No.of.times.90.DPD.or.worse.in.last.12.months 7.798
## No.of.times.60.DPD.or.worse.in.last.12.months 2.898
## No.of.times.30.DPD.or.worse.in.last.12.months 8.501
## No.of.trades.opened.in.last.6.months 1.789
## No.of.PL.trades.opened.in.last.6.months 9.188
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 6.850
## No.of.PL.trades.opened.in.last.12.months 9.924
## Presence.of.open.home.loan -5.835
## Presence.of.open.auto.loan -2.367
## Marital.Status..at.the.time.of.application..xMarried -0.730
## Education.xOthers 0.340
## Profession.xSE 4.441
## Type.of.residence.xOwned -0.255
## Pr(>|z|)
## (Intercept) 1.11e-11 ***
## Income 3.51e-09 ***
## No.of.months.in.current.residence 5.30e-06 ***
## No.of.months.in.current.company 0.00926 **
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 7.06e-05 ***
## No.of.times.30.DPD.or.worse.in.last.6.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 6.27e-15 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.00376 **
## No.of.times.30.DPD.or.worse.in.last.12.months < 2e-16 ***
## No.of.trades.opened.in.last.6.months 0.07358 .
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 7.37e-12 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Presence.of.open.home.loan 5.38e-09 ***
## Presence.of.open.auto.loan 0.01792 *
## Marital.Status..at.the.time.of.application..xMarried 0.46568
## Education.xOthers 0.73390
## Profession.xSE 8.94e-06 ***
## Type.of.residence.xOwned 0.79876
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64042 on 48889 degrees of freedom
## AIC: 64082
##
## Number of Fisher Scoring iterations: 4Removing No.of.months.in.current.residence
logistic_7 <- glm(Performance.Tag ~ Income +
No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
Education.xOthers + Profession.xSE +
Type.of.residence.xOwned
, family = "binomial", data = train)
summary(logistic_7)##
## Call:
## glm(formula = Performance.Tag ~ Income + No.of.months.in.current.company +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.home.loan +
## Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
## Education.xOthers + Profession.xSE + Type.of.residence.xOwned,
## family = "binomial", data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2506 -1.0854 0.4755 1.1015 1.8710
##
## Coefficients:
## Estimate
## (Intercept) -0.142352
## Income -0.043929
## No.of.months.in.current.company -0.018019
## Avgas.CC.Utilization.in.last.12.months 0.157784
## No.of.times.90.DPD.or.worse.in.last.6.months 0.033622
## No.of.times.30.DPD.or.worse.in.last.6.months 0.077160
## No.of.times.90.DPD.or.worse.in.last.12.months 0.067021
## No.of.times.60.DPD.or.worse.in.last.12.months 0.024711
## No.of.times.30.DPD.or.worse.in.last.12.months 0.075766
## No.of.trades.opened.in.last.6.months 0.019672
## No.of.PL.trades.opened.in.last.6.months 0.090006
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.063542
## No.of.PL.trades.opened.in.last.12.months 0.099449
## Presence.of.open.home.loan -0.046066
## Presence.of.open.auto.loan -0.018632
## Marital.Status..at.the.time.of.application..xMarried -0.014927
## Education.xOthers 0.050908
## Profession.xSE 0.080682
## Type.of.residence.xOwned -0.004095
## Std. Error
## (Intercept) 0.020948
## Income 0.007757
## No.of.months.in.current.company 0.007415
## Avgas.CC.Utilization.in.last.12.months 0.007698
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008570
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009131
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008664
## No.of.times.60.DPD.or.worse.in.last.12.months 0.008897
## No.of.times.30.DPD.or.worse.in.last.12.months 0.008981
## No.of.trades.opened.in.last.6.months 0.009978
## No.of.PL.trades.opened.in.last.6.months 0.009907
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008983
## No.of.PL.trades.opened.in.last.12.months 0.010158
## Presence.of.open.home.loan 0.007855
## Presence.of.open.auto.loan 0.007685
## Marital.Status..at.the.time.of.application..xMarried 0.020937
## Education.xOthers 0.159614
## Profession.xSE 0.018236
## Type.of.residence.xOwned 0.018682
## z value
## (Intercept) -6.795
## Income -5.663
## No.of.months.in.current.company -2.430
## Avgas.CC.Utilization.in.last.12.months 20.498
## No.of.times.90.DPD.or.worse.in.last.6.months 3.923
## No.of.times.30.DPD.or.worse.in.last.6.months 8.450
## No.of.times.90.DPD.or.worse.in.last.12.months 7.735
## No.of.times.60.DPD.or.worse.in.last.12.months 2.777
## No.of.times.30.DPD.or.worse.in.last.12.months 8.436
## No.of.trades.opened.in.last.6.months 1.972
## No.of.PL.trades.opened.in.last.6.months 9.085
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 7.074
## No.of.PL.trades.opened.in.last.12.months 9.790
## Presence.of.open.home.loan -5.864
## Presence.of.open.auto.loan -2.424
## Marital.Status..at.the.time.of.application..xMarried -0.713
## Education.xOthers 0.319
## Profession.xSE 4.424
## Type.of.residence.xOwned -0.219
## Pr(>|z|)
## (Intercept) 1.08e-11 ***
## Income 1.48e-08 ***
## No.of.months.in.current.company 0.01510 *
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 8.74e-05 ***
## No.of.times.30.DPD.or.worse.in.last.6.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 1.03e-14 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.00548 **
## No.of.times.30.DPD.or.worse.in.last.12.months < 2e-16 ***
## No.of.trades.opened.in.last.6.months 0.04866 *
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 1.51e-12 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Presence.of.open.home.loan 4.51e-09 ***
## Presence.of.open.auto.loan 0.01533 *
## Marital.Status..at.the.time.of.application..xMarried 0.47589
## Education.xOthers 0.74977
## Profession.xSE 9.68e-06 ***
## Type.of.residence.xOwned 0.82648
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64063 on 48890 degrees of freedom
## AIC: 64101
##
## Number of Fisher Scoring iterations: 4removing Presence.of.open.home.loan
logistic_8<-glm(Performance.Tag ~ Income +
No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months +
Presence.of.open.auto.loan + Marital.Status..at.the.time.of.application..xMarried +
Education.xOthers + Profession.xSE +
Type.of.residence.xOwned
, family = "binomial", data = train)
summary(logistic_8)##
## Call:
## glm(formula = Performance.Tag ~ Income + No.of.months.in.current.company +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.auto.loan +
## Marital.Status..at.the.time.of.application..xMarried + Education.xOthers +
## Profession.xSE + Type.of.residence.xOwned, family = "binomial",
## data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2909 -1.0851 0.4767 1.1016 1.8457
##
## Coefficients:
## Estimate
## (Intercept) -0.141468
## Income -0.044945
## No.of.months.in.current.company -0.017928
## Avgas.CC.Utilization.in.last.12.months 0.159893
## No.of.times.90.DPD.or.worse.in.last.6.months 0.033809
## No.of.times.30.DPD.or.worse.in.last.6.months 0.077894
## No.of.times.90.DPD.or.worse.in.last.12.months 0.067794
## No.of.times.60.DPD.or.worse.in.last.12.months 0.025124
## No.of.times.30.DPD.or.worse.in.last.12.months 0.076593
## No.of.trades.opened.in.last.6.months 0.020029
## No.of.PL.trades.opened.in.last.6.months 0.090959
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.064768
## No.of.PL.trades.opened.in.last.12.months 0.099710
## Presence.of.open.auto.loan -0.018916
## Marital.Status..at.the.time.of.application..xMarried -0.014898
## Education.xOthers 0.047840
## Profession.xSE 0.083273
## Type.of.residence.xOwned -0.004158
## Std. Error
## (Intercept) 0.020936
## Income 0.007752
## No.of.months.in.current.company 0.007413
## Avgas.CC.Utilization.in.last.12.months 0.007688
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008568
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009127
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008661
## No.of.times.60.DPD.or.worse.in.last.12.months 0.008893
## No.of.times.30.DPD.or.worse.in.last.12.months 0.008977
## No.of.trades.opened.in.last.6.months 0.009975
## No.of.PL.trades.opened.in.last.6.months 0.009902
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008978
## No.of.PL.trades.opened.in.last.12.months 0.010155
## Presence.of.open.auto.loan 0.007681
## Marital.Status..at.the.time.of.application..xMarried 0.020927
## Education.xOthers 0.159765
## Profession.xSE 0.018224
## Type.of.residence.xOwned 0.018674
## z value
## (Intercept) -6.757
## Income -5.798
## No.of.months.in.current.company -2.419
## Avgas.CC.Utilization.in.last.12.months 20.798
## No.of.times.90.DPD.or.worse.in.last.6.months 3.946
## No.of.times.30.DPD.or.worse.in.last.6.months 8.534
## No.of.times.90.DPD.or.worse.in.last.12.months 7.828
## No.of.times.60.DPD.or.worse.in.last.12.months 2.825
## No.of.times.30.DPD.or.worse.in.last.12.months 8.532
## No.of.trades.opened.in.last.6.months 2.008
## No.of.PL.trades.opened.in.last.6.months 9.186
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 7.214
## No.of.PL.trades.opened.in.last.12.months 9.819
## Presence.of.open.auto.loan -2.463
## Marital.Status..at.the.time.of.application..xMarried -0.712
## Education.xOthers 0.299
## Profession.xSE 4.569
## Type.of.residence.xOwned -0.223
## Pr(>|z|)
## (Intercept) 1.41e-11 ***
## Income 6.72e-09 ***
## No.of.months.in.current.company 0.01558 *
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 7.95e-05 ***
## No.of.times.30.DPD.or.worse.in.last.6.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 4.97e-15 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.00473 **
## No.of.times.30.DPD.or.worse.in.last.12.months < 2e-16 ***
## No.of.trades.opened.in.last.6.months 0.04464 *
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 5.42e-13 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Presence.of.open.auto.loan 0.01378 *
## Marital.Status..at.the.time.of.application..xMarried 0.47652
## Education.xOthers 0.76461
## Profession.xSE 4.89e-06 ***
## Type.of.residence.xOwned 0.82381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64097 on 48891 degrees of freedom
## AIC: 64133
##
## Number of Fisher Scoring iterations: 4Removing Marital.Status..at.the.time.of.application..xMarried
logistic_9<- glm(Performance.Tag ~ Income +
No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months +
Presence.of.open.auto.loan +
Education.xOthers + Profession.xSE +
Type.of.residence.xOwned
, family = "binomial", data = train)
summary(logistic_9)##
## Call:
## glm(formula = Performance.Tag ~ Income + No.of.months.in.current.company +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.auto.loan +
## Education.xOthers + Profession.xSE + Type.of.residence.xOwned,
## family = "binomial", data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2888 -1.0853 0.4759 1.1019 1.8444
##
## Coefficients:
## Estimate
## (Intercept) -0.154131
## Income -0.045034
## No.of.months.in.current.company -0.017947
## Avgas.CC.Utilization.in.last.12.months 0.159848
## No.of.times.90.DPD.or.worse.in.last.6.months 0.033772
## No.of.times.30.DPD.or.worse.in.last.6.months 0.077949
## No.of.times.90.DPD.or.worse.in.last.12.months 0.067801
## No.of.times.60.DPD.or.worse.in.last.12.months 0.025055
## No.of.times.30.DPD.or.worse.in.last.12.months 0.076667
## No.of.trades.opened.in.last.6.months 0.020032
## No.of.PL.trades.opened.in.last.6.months 0.091049
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.064743
## No.of.PL.trades.opened.in.last.12.months 0.099664
## Presence.of.open.auto.loan -0.018935
## Education.xOthers 0.047154
## Profession.xSE 0.083347
## Type.of.residence.xOwned -0.004164
## Std. Error
## (Intercept) 0.011044
## Income 0.007751
## No.of.months.in.current.company 0.007413
## Avgas.CC.Utilization.in.last.12.months 0.007688
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008568
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009127
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008661
## No.of.times.60.DPD.or.worse.in.last.12.months 0.008893
## No.of.times.30.DPD.or.worse.in.last.12.months 0.008977
## No.of.trades.opened.in.last.6.months 0.009974
## No.of.PL.trades.opened.in.last.6.months 0.009901
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008977
## No.of.PL.trades.opened.in.last.12.months 0.010155
## Presence.of.open.auto.loan 0.007681
## Education.xOthers 0.159768
## Profession.xSE 0.018223
## Type.of.residence.xOwned 0.018674
## z value
## (Intercept) -13.956
## Income -5.810
## No.of.months.in.current.company -2.421
## Avgas.CC.Utilization.in.last.12.months 20.793
## No.of.times.90.DPD.or.worse.in.last.6.months 3.942
## No.of.times.30.DPD.or.worse.in.last.6.months 8.541
## No.of.times.90.DPD.or.worse.in.last.12.months 7.829
## No.of.times.60.DPD.or.worse.in.last.12.months 2.817
## No.of.times.30.DPD.or.worse.in.last.12.months 8.541
## No.of.trades.opened.in.last.6.months 2.008
## No.of.PL.trades.opened.in.last.6.months 9.196
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 7.212
## No.of.PL.trades.opened.in.last.12.months 9.814
## Presence.of.open.auto.loan -2.465
## Education.xOthers 0.295
## Profession.xSE 4.574
## Type.of.residence.xOwned -0.223
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Income 6.25e-09 ***
## No.of.months.in.current.company 0.01548 *
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 8.09e-05 ***
## No.of.times.30.DPD.or.worse.in.last.6.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 4.94e-15 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.00484 **
## No.of.times.30.DPD.or.worse.in.last.12.months < 2e-16 ***
## No.of.trades.opened.in.last.6.months 0.04461 *
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 5.52e-13 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Presence.of.open.auto.loan 0.01369 *
## Education.xOthers 0.76789
## Profession.xSE 4.79e-06 ***
## Type.of.residence.xOwned 0.82357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64098 on 48892 degrees of freedom
## AIC: 64132
##
## Number of Fisher Scoring iterations: 4Removing Profession.xSE
logistic_10<-glm(Performance.Tag ~ Income +
No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months +
Presence.of.open.auto.loan +
Education.xOthers +
Type.of.residence.xOwned
, family = "binomial", data = train)
summary(logistic_10)##
## Call:
## glm(formula = Performance.Tag ~ Income + No.of.months.in.current.company +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.auto.loan +
## Education.xOthers + Type.of.residence.xOwned, family = "binomial",
## data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2911 -1.0843 0.4727 1.1019 1.8371
##
## Coefficients:
## Estimate
## (Intercept) -0.136611
## Income -0.045289
## No.of.months.in.current.company -0.018249
## Avgas.CC.Utilization.in.last.12.months 0.160025
## No.of.times.90.DPD.or.worse.in.last.6.months 0.033783
## No.of.times.30.DPD.or.worse.in.last.6.months 0.077849
## No.of.times.90.DPD.or.worse.in.last.12.months 0.067983
## No.of.times.60.DPD.or.worse.in.last.12.months 0.025472
## No.of.times.30.DPD.or.worse.in.last.12.months 0.076639
## No.of.trades.opened.in.last.6.months 0.019797
## No.of.PL.trades.opened.in.last.6.months 0.090918
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.064652
## No.of.PL.trades.opened.in.last.12.months 0.100138
## Presence.of.open.auto.loan -0.018832
## Education.xOthers 0.038408
## Type.of.residence.xOwned -0.002921
## Std. Error
## (Intercept) 0.010353
## Income 0.007749
## No.of.months.in.current.company 0.007411
## Avgas.CC.Utilization.in.last.12.months 0.007686
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008566
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009125
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008659
## No.of.times.60.DPD.or.worse.in.last.12.months 0.008890
## No.of.times.30.DPD.or.worse.in.last.12.months 0.008975
## No.of.trades.opened.in.last.6.months 0.009972
## No.of.PL.trades.opened.in.last.6.months 0.009899
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008976
## No.of.PL.trades.opened.in.last.12.months 0.010153
## Presence.of.open.auto.loan 0.007678
## Education.xOthers 0.159778
## Type.of.residence.xOwned 0.018666
## z value
## (Intercept) -13.195
## Income -5.844
## No.of.months.in.current.company -2.462
## Avgas.CC.Utilization.in.last.12.months 20.820
## No.of.times.90.DPD.or.worse.in.last.6.months 3.944
## No.of.times.30.DPD.or.worse.in.last.6.months 8.532
## No.of.times.90.DPD.or.worse.in.last.12.months 7.851
## No.of.times.60.DPD.or.worse.in.last.12.months 2.865
## No.of.times.30.DPD.or.worse.in.last.12.months 8.539
## No.of.trades.opened.in.last.6.months 1.985
## No.of.PL.trades.opened.in.last.6.months 9.185
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 7.203
## No.of.PL.trades.opened.in.last.12.months 9.863
## Presence.of.open.auto.loan -2.453
## Education.xOthers 0.240
## Type.of.residence.xOwned -0.156
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Income 5.09e-09 ***
## No.of.months.in.current.company 0.01380 *
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 8.02e-05 ***
## No.of.times.30.DPD.or.worse.in.last.6.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 4.13e-15 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.00417 **
## No.of.times.30.DPD.or.worse.in.last.12.months < 2e-16 ***
## No.of.trades.opened.in.last.6.months 0.04712 *
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 5.89e-13 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Presence.of.open.auto.loan 0.01417 *
## Education.xOthers 0.81003
## Type.of.residence.xOwned 0.87566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64119 on 48893 degrees of freedom
## AIC: 64151
##
## Number of Fisher Scoring iterations: 4removing Type.of.residence.xOwned
logistic_11<-glm(Performance.Tag ~ Income +
No.of.months.in.current.company + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months +
Presence.of.open.auto.loan +
Education.xOthers
, family = "binomial", data = train)
summary(logistic_11)##
## Call:
## glm(formula = Performance.Tag ~ Income + No.of.months.in.current.company +
## Avgas.CC.Utilization.in.last.12.months + No.of.times.90.DPD.or.worse.in.last.6.months +
## No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
## No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
## No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Presence.of.open.auto.loan +
## Education.xOthers, family = "binomial", data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2905 -1.0843 0.4731 1.1018 1.8366
##
## Coefficients:
## Estimate
## (Intercept) -0.137188
## Income -0.045297
## No.of.months.in.current.company -0.018232
## Avgas.CC.Utilization.in.last.12.months 0.160028
## No.of.times.90.DPD.or.worse.in.last.6.months 0.033778
## No.of.times.30.DPD.or.worse.in.last.6.months 0.077834
## No.of.times.90.DPD.or.worse.in.last.12.months 0.067977
## No.of.times.60.DPD.or.worse.in.last.12.months 0.025473
## No.of.times.30.DPD.or.worse.in.last.12.months 0.076644
## No.of.trades.opened.in.last.6.months 0.019797
## No.of.PL.trades.opened.in.last.6.months 0.090935
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.064650
## No.of.PL.trades.opened.in.last.12.months 0.100131
## Presence.of.open.auto.loan -0.018833
## Education.xOthers 0.038534
## Std. Error
## (Intercept) 0.009675
## Income 0.007749
## No.of.months.in.current.company 0.007410
## Avgas.CC.Utilization.in.last.12.months 0.007686
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008566
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009124
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008659
## No.of.times.60.DPD.or.worse.in.last.12.months 0.008890
## No.of.times.30.DPD.or.worse.in.last.12.months 0.008975
## No.of.trades.opened.in.last.6.months 0.009972
## No.of.PL.trades.opened.in.last.6.months 0.009898
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008976
## No.of.PL.trades.opened.in.last.12.months 0.010153
## Presence.of.open.auto.loan 0.007677
## Education.xOthers 0.159772
## z value
## (Intercept) -14.179
## Income -5.845
## No.of.months.in.current.company -2.460
## Avgas.CC.Utilization.in.last.12.months 20.821
## No.of.times.90.DPD.or.worse.in.last.6.months 3.943
## No.of.times.30.DPD.or.worse.in.last.6.months 8.530
## No.of.times.90.DPD.or.worse.in.last.12.months 7.850
## No.of.times.60.DPD.or.worse.in.last.12.months 2.865
## No.of.times.30.DPD.or.worse.in.last.12.months 8.540
## No.of.trades.opened.in.last.6.months 1.985
## No.of.PL.trades.opened.in.last.6.months 9.187
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 7.203
## No.of.PL.trades.opened.in.last.12.months 9.863
## Presence.of.open.auto.loan -2.453
## Education.xOthers 0.241
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Income 5.05e-09 ***
## No.of.months.in.current.company 0.01388 *
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 8.04e-05 ***
## No.of.times.30.DPD.or.worse.in.last.6.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 4.15e-15 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.00417 **
## No.of.times.30.DPD.or.worse.in.last.12.months < 2e-16 ***
## No.of.trades.opened.in.last.6.months 0.04712 *
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 5.90e-13 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Presence.of.open.auto.loan 0.01416 *
## Education.xOthers 0.80941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64119 on 48894 degrees of freedom
## AIC: 64149
##
## Number of Fisher Scoring iterations: 4removing Presence.of.open.auto.loan
logistic_12<-glm(Performance.Tag ~ Income + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months +Education.xOthers
, family = "binomial", data = train)
summary(logistic_12)##
## Call:
## glm(formula = Performance.Tag ~ Income + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months + Education.xOthers,
## family = "binomial", data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2757 -1.0854 0.4778 1.1018 1.8372
##
## Coefficients:
## Estimate
## (Intercept) -0.136440
## Income -0.044278
## Avgas.CC.Utilization.in.last.12.months 0.160587
## No.of.times.90.DPD.or.worse.in.last.6.months 0.033706
## No.of.times.30.DPD.or.worse.in.last.6.months 0.077984
## No.of.times.90.DPD.or.worse.in.last.12.months 0.068771
## No.of.times.60.DPD.or.worse.in.last.12.months 0.025995
## No.of.times.30.DPD.or.worse.in.last.12.months 0.077144
## No.of.trades.opened.in.last.6.months 0.019399
## No.of.PL.trades.opened.in.last.6.months 0.090875
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.064488
## No.of.PL.trades.opened.in.last.12.months 0.100560
## Education.xOthers 0.041347
## Std. Error
## (Intercept) 0.009672
## Income 0.007731
## Avgas.CC.Utilization.in.last.12.months 0.007683
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008565
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009122
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008655
## No.of.times.60.DPD.or.worse.in.last.12.months 0.008888
## No.of.times.30.DPD.or.worse.in.last.12.months 0.008969
## No.of.trades.opened.in.last.6.months 0.009970
## No.of.PL.trades.opened.in.last.6.months 0.009897
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008973
## No.of.PL.trades.opened.in.last.12.months 0.010150
## Education.xOthers 0.159683
## z value
## (Intercept) -14.107
## Income -5.728
## Avgas.CC.Utilization.in.last.12.months 20.902
## No.of.times.90.DPD.or.worse.in.last.6.months 3.935
## No.of.times.30.DPD.or.worse.in.last.6.months 8.549
## No.of.times.90.DPD.or.worse.in.last.12.months 7.946
## No.of.times.60.DPD.or.worse.in.last.12.months 2.925
## No.of.times.30.DPD.or.worse.in.last.12.months 8.601
## No.of.trades.opened.in.last.6.months 1.946
## No.of.PL.trades.opened.in.last.6.months 9.182
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 7.187
## No.of.PL.trades.opened.in.last.12.months 9.907
## Education.xOthers 0.259
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Income 1.02e-08 ***
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 8.31e-05 ***
## No.of.times.30.DPD.or.worse.in.last.6.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 1.93e-15 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.00345 **
## No.of.times.30.DPD.or.worse.in.last.12.months < 2e-16 ***
## No.of.trades.opened.in.last.6.months 0.05168 .
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 6.64e-13 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## Education.xOthers 0.79569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64131 on 48896 degrees of freedom
## AIC: 64157
##
## Number of Fisher Scoring iterations: 4removing Education.xOthers
logistic_13<-glm(Performance.Tag ~ Income + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.trades.opened.in.last.6.months + No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months
, family = "binomial", data = train)
summary(logistic_13)##
## Call:
## glm(formula = Performance.Tag ~ Income + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.trades.opened.in.last.6.months +
## No.of.PL.trades.opened.in.last.6.months + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months, family = "binomial",
## data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2756 -1.0854 0.4775 1.1020 1.8375
##
## Coefficients:
## Estimate
## (Intercept) -0.136354
## Income -0.044289
## Avgas.CC.Utilization.in.last.12.months 0.160586
## No.of.times.90.DPD.or.worse.in.last.6.months 0.033688
## No.of.times.30.DPD.or.worse.in.last.6.months 0.077994
## No.of.times.90.DPD.or.worse.in.last.12.months 0.068782
## No.of.times.60.DPD.or.worse.in.last.12.months 0.025998
## No.of.times.30.DPD.or.worse.in.last.12.months 0.077136
## No.of.trades.opened.in.last.6.months 0.019404
## No.of.PL.trades.opened.in.last.6.months 0.090883
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.064485
## No.of.PL.trades.opened.in.last.12.months 0.100557
## Std. Error
## (Intercept) 0.009666
## Income 0.007731
## Avgas.CC.Utilization.in.last.12.months 0.007683
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008565
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009122
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008655
## No.of.times.60.DPD.or.worse.in.last.12.months 0.008888
## No.of.times.30.DPD.or.worse.in.last.12.months 0.008969
## No.of.trades.opened.in.last.6.months 0.009970
## No.of.PL.trades.opened.in.last.6.months 0.009897
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008973
## No.of.PL.trades.opened.in.last.12.months 0.010150
## z value
## (Intercept) -14.107
## Income -5.729
## Avgas.CC.Utilization.in.last.12.months 20.902
## No.of.times.90.DPD.or.worse.in.last.6.months 3.933
## No.of.times.30.DPD.or.worse.in.last.6.months 8.550
## No.of.times.90.DPD.or.worse.in.last.12.months 7.947
## No.of.times.60.DPD.or.worse.in.last.12.months 2.925
## No.of.times.30.DPD.or.worse.in.last.12.months 8.600
## No.of.trades.opened.in.last.6.months 1.946
## No.of.PL.trades.opened.in.last.6.months 9.183
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 7.186
## No.of.PL.trades.opened.in.last.12.months 9.907
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Income 1.01e-08 ***
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 8.37e-05 ***
## No.of.times.30.DPD.or.worse.in.last.6.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 1.91e-15 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.00344 **
## No.of.times.30.DPD.or.worse.in.last.12.months < 2e-16 ***
## No.of.trades.opened.in.last.6.months 0.05162 .
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 6.65e-13 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64131 on 48897 degrees of freedom
## AIC: 64155
##
## Number of Fisher Scoring iterations: 4#removing No.of.trades.opened.in.last.6.months
logistic_14<-glm(Performance.Tag ~ Income + Avgas.CC.Utilization.in.last.12.months +
No.of.times.90.DPD.or.worse.in.last.6.months +
No.of.times.30.DPD.or.worse.in.last.6.months + No.of.times.90.DPD.or.worse.in.last.12.months +
No.of.times.60.DPD.or.worse.in.last.12.months + No.of.times.30.DPD.or.worse.in.last.12.months +
No.of.PL.trades.opened.in.last.6.months +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
No.of.PL.trades.opened.in.last.12.months
, family = "binomial", data = train)
summary(logistic_14)##
## Call:
## glm(formula = Performance.Tag ~ Income + Avgas.CC.Utilization.in.last.12.months +
## No.of.times.90.DPD.or.worse.in.last.6.months + No.of.times.30.DPD.or.worse.in.last.6.months +
## No.of.times.90.DPD.or.worse.in.last.12.months + No.of.times.60.DPD.or.worse.in.last.12.months +
## No.of.times.30.DPD.or.worse.in.last.12.months + No.of.PL.trades.opened.in.last.6.months +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. +
## No.of.PL.trades.opened.in.last.12.months, family = "binomial",
## data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2629 -1.0844 0.4752 1.1026 1.8372
##
## Coefficients:
## Estimate
## (Intercept) -0.136268
## Income -0.044359
## Avgas.CC.Utilization.in.last.12.months 0.159772
## No.of.times.90.DPD.or.worse.in.last.6.months 0.033673
## No.of.times.30.DPD.or.worse.in.last.6.months 0.077880
## No.of.times.90.DPD.or.worse.in.last.12.months 0.068576
## No.of.times.60.DPD.or.worse.in.last.12.months 0.025705
## No.of.times.30.DPD.or.worse.in.last.12.months 0.076935
## No.of.PL.trades.opened.in.last.6.months 0.097407
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.068492
## No.of.PL.trades.opened.in.last.12.months 0.105693
## Std. Error
## (Intercept) 0.009665
## Income 0.007730
## Avgas.CC.Utilization.in.last.12.months 0.007671
## No.of.times.90.DPD.or.worse.in.last.6.months 0.008564
## No.of.times.30.DPD.or.worse.in.last.6.months 0.009121
## No.of.times.90.DPD.or.worse.in.last.12.months 0.008654
## No.of.times.60.DPD.or.worse.in.last.12.months 0.008886
## No.of.times.30.DPD.or.worse.in.last.12.months 0.008968
## No.of.PL.trades.opened.in.last.6.months 0.009314
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 0.008734
## No.of.PL.trades.opened.in.last.12.months 0.009802
## z value
## (Intercept) -14.099
## Income -5.738
## Avgas.CC.Utilization.in.last.12.months 20.828
## No.of.times.90.DPD.or.worse.in.last.6.months 3.932
## No.of.times.30.DPD.or.worse.in.last.6.months 8.538
## No.of.times.90.DPD.or.worse.in.last.12.months 7.925
## No.of.times.60.DPD.or.worse.in.last.12.months 2.893
## No.of.times.30.DPD.or.worse.in.last.12.months 8.579
## No.of.PL.trades.opened.in.last.6.months 10.458
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 7.842
## No.of.PL.trades.opened.in.last.12.months 10.783
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Income 9.56e-09 ***
## Avgas.CC.Utilization.in.last.12.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.6.months 8.43e-05 ***
## No.of.times.30.DPD.or.worse.in.last.6.months < 2e-16 ***
## No.of.times.90.DPD.or.worse.in.last.12.months 2.29e-15 ***
## No.of.times.60.DPD.or.worse.in.last.12.months 0.00382 **
## No.of.times.30.DPD.or.worse.in.last.12.months < 2e-16 ***
## No.of.PL.trades.opened.in.last.6.months < 2e-16 ***
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans. 4.45e-15 ***
## No.of.PL.trades.opened.in.last.12.months < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67802 on 48908 degrees of freedom
## Residual deviance: 64135 on 48898 degrees of freedom
## AIC: 64157
##
## Number of Fisher Scoring iterations: 4Significance is very high now for existing attributes.Lets take this model as final LR model for now.
Consider Final LR model as logistic model
final_lr_model <- logistic_14Model Evaluation with Test Data
test_pred = predict(final_lr_model, type = "response", newdata = test[,-1])Use the probability cutoff of 50%.
test_pred_default <- as.factor(ifelse(test_pred >= 0.50, 1,0))
test_actual_default <- as.factor(ifelse(test$Performance.Tag==1,1,0))
conf_mtr_50_cutoff <- confusionMatrix(test_pred_default, test_actual_default)
conf_mtr_50_cutoff ## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 11856 323
## 1 8182 597
##
## Accuracy : 0.5942
## 95% CI : (0.5875, 0.6008)
## No Information Rate : 0.9561
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0474
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.5917
## Specificity : 0.6489
## Pos Pred Value : 0.9735
## Neg Pred Value : 0.0680
## Prevalence : 0.9561
## Detection Rate : 0.5657
## Detection Prevalence : 0.5811
## Balanced Accuracy : 0.6203
##
## 'Positive' Class : 0
## Find out the optimal probalility cutoff
perform_fn <- function(cutoff)
{
predicted_default <- as.factor(ifelse(test_pred >= cutoff, 1,0))
conf <- confusionMatrix(predicted_default, test_actual_default)
acc <- conf$overall[1]
sens <- conf$byClass[1]
spec <- conf$byClass[2]
out <- t(as.matrix(c(sens, spec, acc)))
colnames(out) <- c("sensitivity", "specificity", "accuracy")
return(out)
}
s = seq(.01,.95,length=100)
OUT = matrix(0,100,3)
for(i in 1:100)
{
OUT[i,] = perform_fn(s[i])
}
plot(s, OUT[,1],xlab="Cutoff",ylab="Value",cex.lab=1.5,cex.axis=1.5,ylim=c(0,1),type="l",lwd=2,axes=FALSE,col=2)
grid(50, lwd = 2)
axis(1,seq(0,1,length=5),seq(0,1,length=5),cex.lab=1.5)
axis(2,seq(0,1,length=5),seq(0,1,length=5),cex.lab=1.5)
lines(s,OUT[,2],col="orange",lwd=2)
lines(s,OUT[,3],col= "darkgreen",lwd=2)
box()
legend("bottomright", legend=c("Sensitivity","Specificity","Accuracy"),
col=c(2,"orange",4,"darkred"), cex=0.5,lwd=c(3,3,3,3), text.font=14,y.intersp = 0.3)
test_pred_optimal<- as.factor(ifelse(test_pred >= 0.502, 1,0))
optimal_conf <- confusionMatrix(test_pred_optimal, test_actual_default)
optimal_conf## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 11958 334
## 1 8080 586
##
## Accuracy : 0.5985
## 95% CI : (0.5919, 0.6052)
## No Information Rate : 0.9561
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0466
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.59677
## Specificity : 0.63696
## Pos Pred Value : 0.97283
## Neg Pred Value : 0.06762
## Prevalence : 0.95610
## Detection Rate : 0.57057
## Detection Prevalence : 0.58651
## Balanced Accuracy : 0.61686
##
## 'Positive' Class : 0
## for 50 % optimal threshold,
accuracy = 59.85% ,
specificity = 63.69% ,
sensitivity = 59.67%
So cutoff value is 0.502 for final model
Model 2: Linear SVM
SVM needs all numeric attribubtes so reusing the SMOTE balanced train dataset.
library(plyr)
library(caret)
library(kernlab)##
## Attaching package: 'kernlab'## The following object is masked from 'package:ggplot2':
##
## alphalibrary(readr)
library(caret)
library(caTools)train_svm <-train
test_svm<-test
nrow(train_svm)## [1] 48909table(train_svm$Performance.Tag)##
## 0 1
## 24451 24458str(train_svm)## 'data.frame': 48909 obs. of 40 variables:
## $ Performance.Tag : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Age : num 0.0535 -0.6802 0.1694 2.0649 -0.1504 ...
## $ Income : num 1.869 1.63 -1.736 0.119 0.708 ...
## $ No.of.months.in.current.residence : num -1.587 1.898 -0.802 -0.778 -0.509 ...
## $ No.of.months.in.current.company : num -1.0631 0.0735 -0.851 0.1909 1.8471 ...
## $ Total.No.of.Trades : num -0.2318 -0.0769 -0.128 -0.0653 -0.1129 ...
## $ Outstanding.Balance : num 0.548 -1.35 0.887 -1.957 -0.325 ...
## $ Avgas.CC.Utilization.in.last.12.months : num -0.737 -0.3 -0.959 -0.284 -0.443 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : num 0.00403 -1.37311 -0.66921 -1.14146 1.76945 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : num -1.319 -0.592 -0.894 -0.687 1.644 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : num -0.738 -0.305 -0.368 -0.146 2.047 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : num -1.027 -0.22 1.785 0.156 0.338 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : num -0.933 -0.126 -0.734 -0.647 1.899 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : num -0.4427 -1.6659 1.9003 0.0949 1.048 ...
## $ No.of.trades.opened.in.last.6.months : num -1.671 0.721 1.432 -0.361 -0.582 ...
## $ No.of.trades.opened.in.last.12.months : num -0.6517 -1.2774 0.0789 -1.6127 0.4678 ...
## $ No.of.PL.trades.opened.in.last.6.months : num -0.955 -1.901 1.284 -1.05 0.905 ...
## $ No.of.PL.trades.opened.in.last.6.months.1 : num -0.616 -0.903 0.469 -1.117 1.94 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : num -1.174 -2.853 2.916 -1.054 -0.221 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: num -1.356 -0.361 0.413 -3.357 0.293 ...
## $ No.of.PL.trades.opened.in.last.12.months : num -0.9796 -0.5348 0.8387 -1.0438 0.0191 ...
## $ Presence.of.open.home.loan : num 2.2021 -1.7688 0.9944 -0.0491 -0.2479 ...
## $ Presence.of.open.auto.loan : num 0.4905 -0.0927 0.937 -1.1983 -0.2059 ...
## $ Gender.xF : num 0.2169 -0.5466 -0.085 -0.425 -0.0865 ...
## $ Gender.xM : num 0.944 0.398 0.876 0.573 0.455 ...
## $ Marital.Status..at.the.time.of.application..xMarried : num -0.205 0.99 1.133 -0.1 0.111 ...
## $ Marital.Status..at.the.time.of.application..xSingle : num 1.34107 -0.20824 -0.00759 1.05927 -0.34185 ...
## $ Education.xBachelor : num 1.6492 0.9436 -0.5779 0.5796 -0.0764 ...
## $ Education.xMasters : num 0.218 -0.901 -0.508 -0.162 0.947 ...
## $ Education.xOthers : num 0.000768 0.019226 -0.008169 -0.024575 -0.013782 ...
## $ Education.xPhd : num -0.3532 -0.1363 -0.2217 -0.1974 0.0998 ...
## $ Education.xProfessional : num -0.6765 -0.0592 1.5252 -0.6171 -0.204 ...
## $ Profession.xSAL : num 0.1717 1.1324 1.3599 0.7124 -0.0441 ...
## $ Profession.xSE : num 0.321 1.055 -0.341 -0.407 1.935 ...
## $ Profession.xSE_PROF : num 1.1009 -0.0719 0.1607 0.0815 0.0125 ...
## $ Type.of.residence.xCompany.provided : num -0.0414 0.2222 0.1695 0.0338 -0.0713 ...
## $ Type.of.residence.xLiving.with.Parents : num 0.1543 -0.0104 -0.0968 -0.0952 0.0888 ...
## $ Type.of.residence.xOthers : num 0.03425 -0.02431 -0.00173 -0.02977 0.04076 ...
## $ Type.of.residence.xOwned : num 0.542 -0.431 -0.12 -0.489 1.588 ...
## $ Type.of.residence.xRented : num 1.295 0.785 0.931 1.029 -0.134 ...nrow(test_svm)## [1] 20958table(test_svm$Performance.Tag)##
## 0 1
## 20038 920str(test_svm)## 'data.frame': 20958 obs. of 40 variables:
## $ Performance.Tag : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Age : num 1.413 -1.011 0.1 -0.304 1.514 ...
## $ Income : num 1.07 -1.06 -1.13 -1.48 -1.48 ...
## $ No.of.months.in.current.residence : num 1.775 -0.776 1.015 -0.776 -0.776 ...
## $ No.of.months.in.current.company : num -1.18915 1.56272 0.62905 0.23593 -0.00978 ...
## $ Total.No.of.Trades : num 1.793 0.675 2.492 -0.863 -0.863 ...
## $ Outstanding.Balance : num -0.6578 -0.8217 -0.0752 -0.973 1.3182 ...
## $ Avgas.CC.Utilization.in.last.12.months : num -0.604 -0.435 -0.604 -0.638 -0.638 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : num 1.485 -0.492 -0.492 -0.492 -0.492 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : num 0.788 -0.507 -0.507 -0.507 -0.507 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : num 0.476 -0.523 -0.523 -0.523 -0.523 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : num 0.766 -0.543 -0.543 -0.543 -0.543 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : num 0.388 -0.591 -0.591 -0.591 -0.591 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : num 0.214 -0.59 -0.59 -0.59 -0.59 ...
## $ No.of.trades.opened.in.last.6.months : num 1.784 0.824 1.784 -1.098 -0.617 ...
## $ No.of.trades.opened.in.last.12.months : num 1.612 0.435 1.808 -0.939 -0.743 ...
## $ No.of.PL.trades.opened.in.last.6.months : num -0.14 -0.14 2.078 -0.879 -0.879 ...
## $ No.of.PL.trades.opened.in.last.6.months.1 : num -0.14 -0.14 2.078 -0.879 -0.879 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : num 1.129 1.633 -0.886 -0.886 -0.886 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: num 1.793 0.962 0.408 -0.976 -0.976 ...
## $ No.of.PL.trades.opened.in.last.12.months : num -0.15 -0.563 1.5 -0.975 -0.975 ...
## $ Presence.of.open.home.loan : num -0.591 -0.591 -0.591 -0.591 1.693 ...
## $ Presence.of.open.auto.loan : num -0.305 -0.305 -0.305 -0.305 -0.305 ...
## $ Gender.xF : num 0 1 0 0 1 1 0 1 0 0 ...
## $ Gender.xM : num 1 0 1 1 0 0 1 0 1 1 ...
## $ Marital.Status..at.the.time.of.application..xMarried : num 1 0 1 1 1 1 1 1 1 1 ...
## $ Marital.Status..at.the.time.of.application..xSingle : num 0 1 0 0 0 0 0 0 0 0 ...
## $ Education.xBachelor : num 0 0 0 1 0 0 0 1 0 0 ...
## $ Education.xMasters : num 1 0 1 0 0 0 1 0 0 1 ...
## $ Education.xOthers : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Education.xPhd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Education.xProfessional : num 0 1 0 0 1 1 0 0 1 0 ...
## $ Profession.xSAL : num 0 1 1 1 1 1 1 1 1 1 ...
## $ Profession.xSE : num 1 0 0 0 0 0 0 0 0 0 ...
## $ Profession.xSE_PROF : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xCompany.provided : num 0 0 0 0 0 0 0 1 0 0 ...
## $ Type.of.residence.xLiving.with.Parents : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xOthers : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xOwned : num 0 0 0 0 0 0 0 0 0 1 ...
## $ Type.of.residence.xRented : num 1 1 1 1 1 1 1 0 1 0 ...A lot of time for modeling on the whole train data, So we are taking 10% sample of the data and building the model which would make the computation faster.
train.indices = sample(2:nrow(train_svm), 0.1*nrow(train_svm))
train_svm = train_svm[train.indices, ]Model Building
- Linear model - SVM at Cost(C) = 1
model_1 <- ksvm(Performance.Tag ~ ., data = train_svm,scale = TRUE,C=1)
linear_prediction<- predict(model_1, test_svm)
confusionMatrix(linear_prediction, test_svm$Performance.Tag)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 14799 517
## 1 5239 403
##
## Accuracy : 0.7254
## 95% CI : (0.7193, 0.7314)
## No Information Rate : 0.9561
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0512
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.73855
## Specificity : 0.43804
## Pos Pred Value : 0.96624
## Neg Pred Value : 0.07143
## Prevalence : 0.95610
## Detection Rate : 0.70613
## Detection Prevalence : 0.73079
## Balanced Accuracy : 0.58830
##
## 'Positive' Class : 0
## - Linear model - SVM at Cost(C) = 10
model_2 <- ksvm(Performance.Tag ~ ., data = train_svm,scale = TRUE,C=10)
linear_prediction<- predict(model_2, test_svm)
confusionMatrix(linear_prediction, test_svm$Performance.Tag)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 19065 842
## 1 973 78
##
## Accuracy : 0.9134
## 95% CI : (0.9095, 0.9172)
## No Information Rate : 0.9561
## P-Value [Acc > NIR] : 1.000000
##
## Kappa : 0.0339
##
## Mcnemar's Test P-Value : 0.002277
##
## Sensitivity : 0.95144
## Specificity : 0.08478
## Pos Pred Value : 0.95770
## Neg Pred Value : 0.07422
## Prevalence : 0.95610
## Detection Rate : 0.90968
## Detection Prevalence : 0.94985
## Balanced Accuracy : 0.51811
##
## 'Positive' Class : 0
## Improve in Sensitivity and accuracy but drop in Specificity when we change C=1 and C=10
- Using Linear Kernel
Model_linear <- ksvm(Performance.Tag~ ., data = train_svm, scale = FALSE, kernel = "vanilladot")## Setting default kernel parametersEval_linear<- predict(Model_linear, test_svm)#confusion matrix - Linear Kernel
confusionMatrix(Eval_linear,test_svm$Performance.Tag)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 11228 307
## 1 8810 613
##
## Accuracy : 0.565
## 95% CI : (0.5582, 0.5717)
## No Information Rate : 0.9561
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0419
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.56034
## Specificity : 0.66630
## Pos Pred Value : 0.97339
## Neg Pred Value : 0.06505
## Prevalence : 0.95610
## Detection Rate : 0.53574
## Detection Prevalence : 0.55039
## Balanced Accuracy : 0.61332
##
## 'Positive' Class : 0
## - Hyperparameter tuning and Cross Validation
trainControl <- trainControl(method="cv", number=5)metric <- "Accuracy"grid <- expand.grid(C=seq(1, 5, by=1))
summary(grid)## C
## Min. :1
## 1st Qu.:2
## Median :3
## Mean :3
## 3rd Qu.:4
## Max. :5*Using Polynomial Kernel: degree=2
Model_poly <- ksvm(Performance.Tag~ ., data = train_svm, scale = FALSE, kernel = "polydot",kpar=list(degree=2))Predicting the model results
Eval_Poly<- predict(Model_poly, test_svm)#confusion matrix
confusionMatrix(Eval_Poly,test_svm$Performance.Tag)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 19629 885
## 1 409 35
##
## Accuracy : 0.9383
## 95% CI : (0.9349, 0.9415)
## No Information Rate : 0.9561
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0234
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.97959
## Specificity : 0.03804
## Pos Pred Value : 0.95686
## Neg Pred Value : 0.07883
## Prevalence : 0.95610
## Detection Rate : 0.93659
## Detection Prevalence : 0.97881
## Balanced Accuracy : 0.50882
##
## 'Positive' Class : 0
## The specificity looks really low.
- Using RBF Kernel
Model_RBF <- ksvm(Performance.Tag~ ., data = train_svm, scale = FALSE, kernel = "rbfdot")
RBF_linear<- predict(Model_RBF, test_svm)#confusion matrix - RBF Kernel
confusionMatrix(RBF_linear,test_svm$Performance.Tag)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 14703 514
## 1 5335 406
##
## Accuracy : 0.7209
## 95% CI : (0.7148, 0.727)
## No Information Rate : 0.9561
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.05
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.73376
## Specificity : 0.44130
## Pos Pred Value : 0.96622
## Neg Pred Value : 0.07072
## Prevalence : 0.95610
## Detection Rate : 0.70155
## Detection Prevalence : 0.72607
## Balanced Accuracy : 0.58753
##
## 'Positive' Class : 0
## Conclusion from SVM
After performing five fold cross validation we came to the conclusion that Polynomial kernel performs the best in terms of accuracy but its specificity is too bad Linear kernel accuracy: ~56.50 % RBF kernel accuracy: ~ 72.19% Polynomial accuracy: ~ 93.83% Accuracy = 93.83% is the best accuracy so far amongst all the models
The model gives the best accuracy in case of Polynomial accuracy during cross validation. The final values used for the model are sigma = 0.1 and C = 2. Though it’s hard to conclude anything from svm model as the the accuracy ,specificity and sensitivity are not consistent.As we are doing this modelling on a small dataset so we are not considering it our chosen model.
- Model 3:random Forest
Spliting the bank data in 70:30 ratio
library(rpart)
library(rpart.plot)
library(kernlab)
library(readr)set.seed(101)
master_df$Performance.Tag <- as.factor(ifelse(master_df$Performance.Tag==0,"no","yes"))
split_indices <- sample.split(master_df$Performance.Tag, SplitRatio = 0.70)
train_rf <- master_df[split_indices, ]
test_rf <- master_df[!split_indices, ]
nrow(train_rf)/nrow(master_df)## [1] 0.7000014nrow(test_rf)/nrow(master_df)## [1] 0.2999986train_rf<-train
test_rf<- test
train_rf$Performance.Tag<- as.factor(ifelse(train_rf$Performance.Tag==0,"no","yes"))
test_rf$Performance.Tag<- as.factor(ifelse(test_rf$Performance.Tag==0,"no","yes"))
table(train_rf$Performance.Tag)##
## no yes
## 24451 24458#check classes distribution
prop.table(table(train_rf$Performance.Tag))##
## no yes
## 0.4999284 0.5000716rf_synthetic <- randomForest(Performance.Tag ~.,
data = train_rf,
proximity = F,
do.trace = T,
mtry = 5,
ntree=1000)## ntree OOB 1 2
## 1: 39.87% 39.71% 40.02%
## 2: 39.71% 37.59% 41.84%
## 3: 39.64% 36.99% 42.31%
## 4: 39.26% 36.46% 42.05%
## 5: 38.77% 35.86% 41.68%
## 6: 38.54% 35.80% 41.28%
## 7: 37.80% 35.11% 40.48%
## 8: 37.35% 34.67% 40.03%
## 9: 37.10% 34.66% 39.55%
## 10: 36.74% 34.51% 38.97%
## 11: 36.25% 34.20% 38.30%
## 12: 35.88% 34.12% 37.64%
## 13: 35.55% 33.80% 37.29%
## 14: 35.62% 34.07% 37.17%
## 15: 34.98% 33.74% 36.22%
## 16: 34.85% 33.72% 35.98%
## 17: 34.25% 33.26% 35.25%
## 18: 34.12% 33.42% 34.82%
## 19: 33.88% 33.16% 34.60%
## 20: 33.74% 33.28% 34.20%
## 21: 33.60% 33.25% 33.94%
## 22: 33.28% 33.32% 33.24%
## 23: 33.04% 32.98% 33.10%
## 24: 32.96% 33.14% 32.79%
## 25: 32.77% 32.97% 32.57%
## 26: 32.69% 32.89% 32.49%
## 27: 32.48% 32.91% 32.05%
## 28: 32.42% 32.77% 32.07%
## 29: 32.14% 32.85% 31.42%
## 30: 32.21% 33.08% 31.33%
## 31: 31.91% 32.76% 31.07%
## 32: 31.76% 32.62% 30.90%
## 33: 31.64% 32.66% 30.62%
## 34: 31.56% 32.69% 30.44%
## 35: 31.34% 32.56% 30.11%
## 36: 31.17% 32.51% 29.83%
## 37: 31.19% 32.52% 29.86%
## 38: 31.09% 32.50% 29.69%
## 39: 31.03% 32.60% 29.46%
## 40: 30.97% 32.51% 29.43%
## 41: 30.77% 32.49% 29.05%
## 42: 30.81% 32.54% 29.09%
## 43: 30.55% 32.38% 28.72%
## 44: 30.46% 32.26% 28.66%
## 45: 30.45% 32.42% 28.49%
## 46: 30.40% 32.40% 28.40%
## 47: 30.26% 32.24% 28.27%
## 48: 30.18% 32.37% 28.00%
## 49: 30.19% 32.33% 28.05%
## 50: 30.12% 32.48% 27.75%
## 51: 30.11% 32.45% 27.76%
## 52: 30.09% 32.42% 27.77%
## 53: 29.94% 32.25% 27.64%
## 54: 29.97% 32.39% 27.55%
## 55: 29.93% 32.25% 27.60%
## 56: 29.88% 32.33% 27.44%
## 57: 29.93% 32.49% 27.38%
## 58: 29.91% 32.51% 27.31%
## 59: 29.71% 32.36% 27.07%
## 60: 29.65% 32.32% 26.98%
## 61: 29.53% 32.30% 26.77%
## 62: 29.67% 32.43% 26.91%
## 63: 29.64% 32.40% 26.90%
## 64: 29.54% 32.37% 26.71%
## 65: 29.52% 32.46% 26.58%
## 66: 29.46% 32.37% 26.55%
## 67: 29.45% 32.42% 26.48%
## 68: 29.31% 32.15% 26.48%
## 69: 29.32% 32.22% 26.42%
## 70: 29.24% 32.28% 26.21%
## 71: 29.19% 32.24% 26.14%
## 72: 29.22% 32.34% 26.11%
## 73: 29.20% 32.42% 25.98%
## 74: 29.19% 32.35% 26.03%
## 75: 29.17% 32.39% 25.94%
## 76: 29.21% 32.47% 25.95%
## 77: 29.11% 32.46% 25.75%
## 78: 29.06% 32.35% 25.77%
## 79: 29.02% 32.40% 25.65%
## 80: 28.96% 32.29% 25.62%
## 81: 28.97% 32.36% 25.58%
## 82: 28.99% 32.38% 25.59%
## 83: 28.92% 32.33% 25.52%
## 84: 28.79% 32.27% 25.31%
## 85: 28.84% 32.38% 25.29%
## 86: 28.76% 32.30% 25.22%
## 87: 28.71% 32.33% 25.09%
## 88: 28.70% 32.32% 25.09%
## 89: 28.73% 32.35% 25.10%
## 90: 28.69% 32.44% 24.95%
## 91: 28.62% 32.30% 24.93%
## 92: 28.62% 32.33% 24.91%
## 93: 28.56% 32.24% 24.88%
## 94: 28.57% 32.33% 24.80%
## 95: 28.58% 32.41% 24.76%
## 96: 28.58% 32.42% 24.75%
## 97: 28.48% 32.30% 24.65%
## 98: 28.45% 32.34% 24.56%
## 99: 28.38% 32.33% 24.44%
## 100: 28.49% 32.42% 24.55%
## 101: 28.46% 32.49% 24.43%
## 102: 28.53% 32.53% 24.53%
## 103: 28.51% 32.56% 24.45%
## 104: 28.50% 32.47% 24.52%
## 105: 28.54% 32.55% 24.53%
## 106: 28.47% 32.52% 24.42%
## 107: 28.44% 32.53% 24.35%
## 108: 28.41% 32.53% 24.28%
## 109: 28.29% 32.41% 24.17%
## 110: 28.39% 32.55% 24.23%
## 111: 28.39% 32.54% 24.24%
## 112: 28.37% 32.48% 24.26%
## 113: 28.30% 32.44% 24.15%
## 114: 28.29% 32.42% 24.17%
## 115: 28.23% 32.31% 24.14%
## 116: 28.33% 32.48% 24.19%
## 117: 28.30% 32.41% 24.19%
## 118: 28.26% 32.38% 24.14%
## 119: 28.26% 32.53% 23.99%
## 120: 28.28% 32.51% 24.05%
## 121: 28.26% 32.46% 24.07%
## 122: 28.23% 32.50% 23.97%
## 123: 28.25% 32.53% 23.97%
## 124: 28.22% 32.50% 23.94%
## 125: 28.13% 32.40% 23.87%
## 126: 28.18% 32.46% 23.90%
## 127: 28.15% 32.46% 23.83%
## 128: 28.15% 32.41% 23.90%
## 129: 28.14% 32.46% 23.82%
## 130: 28.19% 32.56% 23.82%
## 131: 28.17% 32.56% 23.78%
## 132: 28.21% 32.62% 23.80%
## 133: 28.21% 32.55% 23.88%
## 134: 28.19% 32.54% 23.84%
## 135: 28.12% 32.47% 23.76%
## 136: 28.14% 32.55% 23.72%
## 137: 28.15% 32.56% 23.73%
## 138: 28.12% 32.55% 23.70%
## 139: 28.14% 32.59% 23.69%
## 140: 28.18% 32.62% 23.74%
## 141: 28.15% 32.58% 23.72%
## 142: 28.15% 32.60% 23.69%
## 143: 28.15% 32.61% 23.70%
## 144: 28.07% 32.62% 23.52%
## 145: 28.14% 32.69% 23.59%
## 146: 28.08% 32.60% 23.56%
## 147: 28.01% 32.49% 23.53%
## 148: 28.01% 32.52% 23.49%
## 149: 28.02% 32.58% 23.47%
## 150: 27.96% 32.45% 23.46%
## 151: 27.99% 32.51% 23.47%
## 152: 27.96% 32.47% 23.46%
## 153: 27.97% 32.55% 23.38%
## 154: 28.01% 32.55% 23.46%
## 155: 27.94% 32.48% 23.39%
## 156: 27.95% 32.56% 23.34%
## 157: 28.04% 32.59% 23.49%
## 158: 27.97% 32.52% 23.43%
## 159: 27.96% 32.51% 23.42%
## 160: 28.00% 32.60% 23.40%
## 161: 28.00% 32.53% 23.46%
## 162: 27.99% 32.55% 23.42%
## 163: 27.96% 32.56% 23.37%
## 164: 27.96% 32.57% 23.35%
## 165: 27.93% 32.53% 23.33%
## 166: 27.92% 32.58% 23.25%
## 167: 27.94% 32.69% 23.19%
## 168: 27.86% 32.63% 23.09%
## 169: 27.82% 32.63% 23.00%
## 170: 27.86% 32.60% 23.13%
## 171: 27.90% 32.61% 23.19%
## 172: 27.89% 32.67% 23.12%
## 173: 27.87% 32.66% 23.08%
## 174: 27.89% 32.69% 23.09%
## 175: 27.86% 32.62% 23.09%
## 176: 27.80% 32.58% 23.02%
## 177: 27.76% 32.53% 22.99%
## 178: 27.78% 32.51% 23.06%
## 179: 27.77% 32.53% 23.01%
## 180: 27.72% 32.54% 22.90%
## 181: 27.72% 32.53% 22.91%
## 182: 27.79% 32.62% 22.97%
## 183: 27.70% 32.53% 22.88%
## 184: 27.72% 32.56% 22.89%
## 185: 27.66% 32.51% 22.81%
## 186: 27.68% 32.53% 22.84%
## 187: 27.68% 32.63% 22.73%
## 188: 27.71% 32.63% 22.79%
## 189: 27.69% 32.54% 22.84%
## 190: 27.69% 32.56% 22.82%
## 191: 27.71% 32.52% 22.90%
## 192: 27.72% 32.61% 22.83%
## 193: 27.71% 32.61% 22.82%
## 194: 27.70% 32.57% 22.82%
## 195: 27.63% 32.54% 22.72%
## 196: 27.67% 32.57% 22.77%
## 197: 27.73% 32.65% 22.80%
## 198: 27.70% 32.60% 22.79%
## 199: 27.70% 32.61% 22.80%
## 200: 27.66% 32.55% 22.77%
## 201: 27.69% 32.61% 22.76%
## 202: 27.66% 32.58% 22.74%
## 203: 27.61% 32.54% 22.67%
## 204: 27.62% 32.55% 22.68%
## 205: 27.59% 32.53% 22.64%
## 206: 27.59% 32.53% 22.65%
## 207: 27.64% 32.57% 22.71%
## 208: 27.56% 32.47% 22.66%
## 209: 27.57% 32.54% 22.60%
## 210: 27.59% 32.52% 22.65%
## 211: 27.59% 32.54% 22.64%
## 212: 27.59% 32.57% 22.61%
## 213: 27.58% 32.58% 22.59%
## 214: 27.60% 32.58% 22.61%
## 215: 27.60% 32.60% 22.60%
## 216: 27.56% 32.57% 22.55%
## 217: 27.57% 32.58% 22.55%
## 218: 27.54% 32.50% 22.59%
## 219: 27.58% 32.56% 22.60%
## 220: 27.53% 32.50% 22.57%
## 221: 27.54% 32.48% 22.61%
## 222: 27.55% 32.56% 22.53%
## 223: 27.57% 32.52% 22.61%
## 224: 27.52% 32.55% 22.49%
## 225: 27.50% 32.55% 22.45%
## 226: 27.50% 32.56% 22.43%
## 227: 27.51% 32.58% 22.43%
## 228: 27.46% 32.58% 22.34%
## 229: 27.50% 32.63% 22.36%
## 230: 27.50% 32.64% 22.36%
## 231: 27.52% 32.64% 22.40%
## 232: 27.45% 32.56% 22.34%
## 233: 27.44% 32.62% 22.27%
## 234: 27.49% 32.60% 22.37%
## 235: 27.50% 32.62% 22.38%
## 236: 27.45% 32.55% 22.35%
## 237: 27.46% 32.58% 22.34%
## 238: 27.47% 32.57% 22.37%
## 239: 27.44% 32.58% 22.30%
## 240: 27.43% 32.60% 22.26%
## 241: 27.43% 32.57% 22.29%
## 242: 27.43% 32.60% 22.26%
## 243: 27.45% 32.60% 22.31%
## 244: 27.42% 32.58% 22.26%
## 245: 27.42% 32.56% 22.28%
## 246: 27.37% 32.52% 22.21%
## 247: 27.42% 32.56% 22.28%
## 248: 27.43% 32.51% 22.35%
## 249: 27.44% 32.55% 22.32%
## 250: 27.49% 32.58% 22.40%
## 251: 27.48% 32.62% 22.34%
## 252: 27.48% 32.60% 22.35%
## 253: 27.48% 32.60% 22.37%
## 254: 27.43% 32.55% 22.31%
## 255: 27.46% 32.59% 22.34%
## 256: 27.50% 32.65% 22.34%
## 257: 27.48% 32.63% 22.33%
## 258: 27.49% 32.62% 22.37%
## 259: 27.48% 32.64% 22.32%
## 260: 27.51% 32.67% 22.35%
## 261: 27.47% 32.62% 22.32%
## 262: 27.43% 32.62% 22.25%
## 263: 27.44% 32.61% 22.27%
## 264: 27.48% 32.60% 22.36%
## 265: 27.47% 32.62% 22.32%
## 266: 27.47% 32.63% 22.30%
## 267: 27.46% 32.66% 22.25%
## 268: 27.45% 32.66% 22.24%
## 269: 27.45% 32.66% 22.24%
## 270: 27.47% 32.70% 22.24%
## 271: 27.41% 32.62% 22.20%
## 272: 27.42% 32.64% 22.20%
## 273: 27.37% 32.57% 22.17%
## 274: 27.39% 32.59% 22.20%
## 275: 27.41% 32.59% 22.23%
## 276: 27.43% 32.71% 22.16%
## 277: 27.40% 32.64% 22.16%
## 278: 27.42% 32.71% 22.12%
## 279: 27.42% 32.76% 22.08%
## 280: 27.42% 32.72% 22.12%
## 281: 27.43% 32.75% 22.11%
## 282: 27.42% 32.73% 22.11%
## 283: 27.43% 32.72% 22.14%
## 284: 27.41% 32.75% 22.07%
## 285: 27.43% 32.78% 22.08%
## 286: 27.48% 32.80% 22.15%
## 287: 27.46% 32.81% 22.10%
## 288: 27.43% 32.81% 22.06%
## 289: 27.40% 32.76% 22.05%
## 290: 27.40% 32.78% 22.03%
## 291: 27.38% 32.75% 22.00%
## 292: 27.43% 32.79% 22.06%
## 293: 27.42% 32.79% 22.05%
## 294: 27.43% 32.80% 22.06%
## 295: 27.43% 32.81% 22.05%
## 296: 27.44% 32.79% 22.09%
## 297: 27.40% 32.79% 22.01%
## 298: 27.43% 32.81% 22.06%
## 299: 27.42% 32.82% 22.02%
## 300: 27.46% 32.86% 22.06%
## 301: 27.45% 32.83% 22.07%
## 302: 27.40% 32.78% 22.03%
## 303: 27.44% 32.87% 22.02%
## 304: 27.46% 32.85% 22.07%
## 305: 27.43% 32.83% 22.03%
## 306: 27.42% 32.81% 22.03%
## 307: 27.36% 32.75% 21.98%
## 308: 27.39% 32.76% 22.03%
## 309: 27.41% 32.82% 22.01%
## 310: 27.39% 32.78% 22.01%
## 311: 27.44% 32.86% 22.03%
## 312: 27.39% 32.80% 21.97%
## 313: 27.43% 32.84% 22.03%
## 314: 27.41% 32.80% 22.02%
## 315: 27.40% 32.86% 21.95%
## 316: 27.40% 32.82% 21.98%
## 317: 27.44% 32.85% 22.03%
## 318: 27.40% 32.78% 22.02%
## 319: 27.40% 32.83% 21.97%
## 320: 27.36% 32.77% 21.95%
## 321: 27.38% 32.77% 21.99%
## 322: 27.32% 32.72% 21.92%
## 323: 27.30% 32.73% 21.87%
## 324: 27.32% 32.75% 21.90%
## 325: 27.30% 32.69% 21.92%
## 326: 27.33% 32.70% 21.95%
## 327: 27.38% 32.74% 22.01%
## 328: 27.37% 32.80% 21.95%
## 329: 27.39% 32.85% 21.94%
## 330: 27.32% 32.75% 21.89%
## 331: 27.39% 32.85% 21.93%
## 332: 27.40% 32.89% 21.92%
## 333: 27.33% 32.81% 21.85%
## 334: 27.34% 32.80% 21.89%
## 335: 27.32% 32.79% 21.84%
## 336: 27.35% 32.86% 21.85%
## 337: 27.31% 32.84% 21.79%
## 338: 27.33% 32.82% 21.84%
## 339: 27.32% 32.83% 21.81%
## 340: 27.35% 32.85% 21.85%
## 341: 27.33% 32.82% 21.83%
## 342: 27.33% 32.78% 21.87%
## 343: 27.29% 32.76% 21.83%
## 344: 27.30% 32.79% 21.81%
## 345: 27.32% 32.79% 21.85%
## 346: 27.31% 32.77% 21.85%
## 347: 27.30% 32.79% 21.81%
## 348: 27.31% 32.78% 21.84%
## 349: 27.26% 32.73% 21.80%
## 350: 27.30% 32.80% 21.80%
## 351: 27.34% 32.80% 21.88%
## 352: 27.33% 32.82% 21.84%
## 353: 27.31% 32.80% 21.81%
## 354: 27.33% 32.82% 21.83%
## 355: 27.31% 32.81% 21.82%
## 356: 27.26% 32.76% 21.76%
## 357: 27.24% 32.71% 21.76%
## 358: 27.22% 32.71% 21.74%
## 359: 27.21% 32.72% 21.71%
## 360: 27.22% 32.73% 21.71%
## 361: 27.22% 32.74% 21.71%
## 362: 27.19% 32.71% 21.68%
## 363: 27.19% 32.64% 21.74%
## 364: 27.25% 32.73% 21.77%
## 365: 27.17% 32.61% 21.72%
## 366: 27.22% 32.71% 21.72%
## 367: 27.21% 32.69% 21.72%
## 368: 27.23% 32.69% 21.78%
## 369: 27.23% 32.73% 21.74%
## 370: 27.24% 32.74% 21.75%
## 371: 27.22% 32.68% 21.75%
## 372: 27.23% 32.75% 21.71%
## 373: 27.21% 32.69% 21.72%
## 374: 27.25% 32.76% 21.74%
## 375: 27.26% 32.80% 21.72%
## 376: 27.25% 32.77% 21.73%
## 377: 27.24% 32.76% 21.73%
## 378: 27.27% 32.80% 21.74%
## 379: 27.23% 32.77% 21.70%
## 380: 27.19% 32.69% 21.69%
## 381: 27.22% 32.72% 21.72%
## 382: 27.22% 32.73% 21.71%
## 383: 27.25% 32.72% 21.78%
## 384: 27.23% 32.74% 21.72%
## 385: 27.24% 32.74% 21.75%
## 386: 27.26% 32.76% 21.76%
## 387: 27.24% 32.75% 21.73%
## 388: 27.20% 32.69% 21.71%
## 389: 27.22% 32.76% 21.69%
## 390: 27.23% 32.76% 21.70%
## 391: 27.20% 32.72% 21.69%
## 392: 27.22% 32.73% 21.71%
## 393: 27.20% 32.74% 21.67%
## 394: 27.22% 32.72% 21.71%
## 395: 27.18% 32.71% 21.66%
## 396: 27.17% 32.66% 21.68%
## 397: 27.20% 32.67% 21.73%
## 398: 27.19% 32.66% 21.73%
## 399: 27.19% 32.66% 21.72%
## 400: 27.17% 32.69% 21.65%
## 401: 27.19% 32.71% 21.67%
## 402: 27.20% 32.72% 21.68%
## 403: 27.17% 32.70% 21.64%
## 404: 27.17% 32.71% 21.63%
## 405: 27.21% 32.72% 21.69%
## 406: 27.19% 32.67% 21.70%
## 407: 27.18% 32.71% 21.65%
## 408: 27.15% 32.69% 21.62%
## 409: 27.17% 32.73% 21.62%
## 410: 27.21% 32.75% 21.67%
## 411: 27.16% 32.70% 21.63%
## 412: 27.17% 32.67% 21.68%
## 413: 27.20% 32.70% 21.70%
## 414: 27.16% 32.70% 21.62%
## 415: 27.18% 32.73% 21.64%
## 416: 27.16% 32.74% 21.58%
## 417: 27.17% 32.73% 21.62%
## 418: 27.19% 32.75% 21.63%
## 419: 27.18% 32.74% 21.62%
## 420: 27.15% 32.72% 21.58%
## 421: 27.15% 32.69% 21.60%
## 422: 27.17% 32.72% 21.62%
## 423: 27.16% 32.69% 21.63%
## 424: 27.15% 32.67% 21.64%
## 425: 27.14% 32.69% 21.59%
## 426: 27.12% 32.67% 21.58%
## 427: 27.14% 32.68% 21.61%
## 428: 27.17% 32.74% 21.60%
## 429: 27.12% 32.67% 21.58%
## 430: 27.20% 32.73% 21.67%
## 431: 27.19% 32.69% 21.68%
## 432: 27.16% 32.72% 21.61%
## 433: 27.19% 32.76% 21.62%
## 434: 27.15% 32.69% 21.62%
## 435: 27.16% 32.70% 21.62%
## 436: 27.16% 32.71% 21.62%
## 437: 27.15% 32.68% 21.62%
## 438: 27.16% 32.71% 21.62%
## 439: 27.14% 32.69% 21.58%
## 440: 27.18% 32.73% 21.63%
## 441: 27.18% 32.77% 21.59%
## 442: 27.17% 32.76% 21.58%
## 443: 27.16% 32.77% 21.54%
## 444: 27.17% 32.79% 21.55%
## 445: 27.16% 32.78% 21.54%
## 446: 27.14% 32.76% 21.52%
## 447: 27.11% 32.73% 21.50%
## 448: 27.11% 32.75% 21.46%
## 449: 27.12% 32.74% 21.51%
## 450: 27.13% 32.76% 21.51%
## 451: 27.16% 32.76% 21.56%
## 452: 27.15% 32.74% 21.56%
## 453: 27.14% 32.76% 21.51%
## 454: 27.15% 32.79% 21.51%
## 455: 27.12% 32.72% 21.51%
## 456: 27.12% 32.76% 21.48%
## 457: 27.10% 32.72% 21.48%
## 458: 27.11% 32.73% 21.49%
## 459: 27.10% 32.73% 21.47%
## 460: 27.10% 32.79% 21.42%
## 461: 27.14% 32.84% 21.45%
## 462: 27.11% 32.82% 21.40%
## 463: 27.11% 32.80% 21.42%
## 464: 27.08% 32.78% 21.38%
## 465: 27.08% 32.78% 21.39%
## 466: 27.08% 32.80% 21.37%
## 467: 27.08% 32.77% 21.39%
## 468: 27.07% 32.77% 21.37%
## 469: 27.05% 32.73% 21.38%
## 470: 27.09% 32.79% 21.40%
## 471: 27.08% 32.81% 21.35%
## 472: 27.08% 32.78% 21.38%
## 473: 27.06% 32.74% 21.39%
## 474: 27.09% 32.77% 21.41%
## 475: 27.08% 32.73% 21.42%
## 476: 27.07% 32.76% 21.38%
## 477: 27.05% 32.70% 21.41%
## 478: 27.06% 32.69% 21.44%
## 479: 27.09% 32.78% 21.40%
## 480: 27.08% 32.74% 21.42%
## 481: 27.08% 32.69% 21.48%
## 482: 27.08% 32.74% 21.42%
## 483: 27.07% 32.71% 21.44%
## 484: 27.06% 32.70% 21.42%
## 485: 27.08% 32.71% 21.44%
## 486: 27.07% 32.69% 21.44%
## 487: 27.07% 32.70% 21.44%
## 488: 27.08% 32.71% 21.46%
## 489: 27.09% 32.74% 21.44%
## 490: 27.06% 32.70% 21.42%
## 491: 27.07% 32.69% 21.44%
## 492: 27.04% 32.73% 21.35%
## 493: 27.04% 32.69% 21.38%
## 494: 27.04% 32.68% 21.40%
## 495: 27.03% 32.67% 21.38%
## 496: 27.02% 32.68% 21.35%
## 497: 27.03% 32.67% 21.40%
## 498: 27.04% 32.70% 21.39%
## 499: 27.02% 32.67% 21.38%
## 500: 27.03% 32.69% 21.38%
## 501: 27.00% 32.62% 21.37%
## 502: 27.03% 32.67% 21.39%
## 503: 27.03% 32.67% 21.39%
## 504: 27.00% 32.66% 21.34%
## 505: 27.03% 32.67% 21.39%
## 506: 27.00% 32.65% 21.34%
## 507: 26.99% 32.64% 21.34%
## 508: 27.00% 32.67% 21.33%
## 509: 27.01% 32.67% 21.36%
## 510: 27.01% 32.71% 21.32%
## 511: 27.03% 32.71% 21.34%
## 512: 27.03% 32.68% 21.39%
## 513: 27.03% 32.69% 21.38%
## 514: 26.99% 32.65% 21.33%
## 515: 27.00% 32.65% 21.35%
## 516: 27.00% 32.64% 21.35%
## 517: 27.03% 32.64% 21.42%
## 518: 27.01% 32.62% 21.40%
## 519: 27.02% 32.63% 21.41%
## 520: 27.03% 32.67% 21.40%
## 521: 27.04% 32.65% 21.44%
## 522: 27.06% 32.70% 21.42%
## 523: 27.05% 32.68% 21.42%
## 524: 27.00% 32.64% 21.36%
## 525: 27.03% 32.69% 21.37%
## 526: 27.03% 32.68% 21.38%
## 527: 27.05% 32.68% 21.42%
## 528: 27.02% 32.64% 21.40%
## 529: 27.04% 32.68% 21.40%
## 530: 27.03% 32.68% 21.39%
## 531: 27.00% 32.64% 21.37%
## 532: 27.02% 32.64% 21.40%
## 533: 27.00% 32.62% 21.39%
## 534: 26.99% 32.59% 21.39%
## 535: 27.02% 32.69% 21.35%
## 536: 27.00% 32.60% 21.40%
## 537: 26.98% 32.60% 21.36%
## 538: 27.01% 32.62% 21.40%
## 539: 27.01% 32.62% 21.40%
## 540: 26.99% 32.60% 21.37%
## 541: 27.03% 32.66% 21.41%
## 542: 27.00% 32.61% 21.38%
## 543: 27.02% 32.62% 21.42%
## 544: 26.99% 32.55% 21.42%
## 545: 27.01% 32.61% 21.42%
## 546: 27.03% 32.65% 21.41%
## 547: 27.02% 32.62% 21.41%
## 548: 27.03% 32.64% 21.42%
## 549: 27.02% 32.65% 21.38%
## 550: 27.04% 32.70% 21.38%
## 551: 27.04% 32.69% 21.38%
## 552: 27.01% 32.66% 21.37%
## 553: 27.01% 32.64% 21.38%
## 554: 27.00% 32.66% 21.34%
## 555: 26.99% 32.64% 21.34%
## 556: 27.00% 32.67% 21.33%
## 557: 26.99% 32.65% 21.33%
## 558: 27.04% 32.67% 21.40%
## 559: 27.01% 32.66% 21.37%
## 560: 27.02% 32.70% 21.35%
## 561: 27.02% 32.64% 21.40%
## 562: 27.00% 32.63% 21.36%
## 563: 27.00% 32.64% 21.35%
## 564: 27.01% 32.66% 21.36%
## 565: 26.98% 32.61% 21.36%
## 566: 26.97% 32.62% 21.31%
## 567: 27.03% 32.64% 21.42%
## 568: 27.03% 32.67% 21.39%
## 569: 27.03% 32.66% 21.40%
## 570: 27.03% 32.67% 21.39%
## 571: 27.03% 32.69% 21.38%
## 572: 27.01% 32.69% 21.33%
## 573: 27.04% 32.71% 21.36%
## 574: 27.00% 32.68% 21.32%
## 575: 26.99% 32.67% 21.31%
## 576: 27.01% 32.67% 21.35%
## 577: 26.99% 32.65% 21.34%
## 578: 26.96% 32.60% 21.33%
## 579: 26.97% 32.61% 21.33%
## 580: 26.98% 32.64% 21.33%
## 581: 26.97% 32.66% 21.28%
## 582: 27.00% 32.71% 21.30%
## 583: 27.00% 32.70% 21.30%
## 584: 26.98% 32.64% 21.32%
## 585: 26.98% 32.64% 21.31%
## 586: 26.98% 32.64% 21.31%
## 587: 26.99% 32.67% 21.31%
## 588: 26.96% 32.64% 21.27%
## 589: 26.97% 32.66% 21.29%
## 590: 26.97% 32.66% 21.29%
## 591: 26.98% 32.66% 21.31%
## 592: 27.01% 32.69% 21.33%
## 593: 26.98% 32.68% 21.29%
## 594: 26.99% 32.68% 21.29%
## 595: 26.99% 32.68% 21.31%
## 596: 27.01% 32.64% 21.38%
## 597: 27.02% 32.70% 21.33%
## 598: 27.00% 32.64% 21.35%
## 599: 27.01% 32.69% 21.33%
## 600: 26.99% 32.69% 21.29%
## 601: 27.01% 32.73% 21.30%
## 602: 26.96% 32.63% 21.29%
## 603: 27.00% 32.66% 21.34%
## 604: 27.01% 32.68% 21.33%
## 605: 26.99% 32.72% 21.26%
## 606: 27.00% 32.67% 21.32%
## 607: 26.99% 32.71% 21.27%
## 608: 27.01% 32.73% 21.29%
## 609: 26.97% 32.69% 21.25%
## 610: 26.98% 32.73% 21.24%
## 611: 27.00% 32.74% 21.27%
## 612: 27.01% 32.75% 21.28%
## 613: 27.01% 32.76% 21.26%
## 614: 27.01% 32.76% 21.27%
## 615: 27.01% 32.74% 21.27%
## 616: 27.00% 32.73% 21.27%
## 617: 27.03% 32.73% 21.33%
## 618: 27.04% 32.79% 21.29%
## 619: 27.01% 32.76% 21.27%
## 620: 27.04% 32.78% 21.30%
## 621: 27.03% 32.74% 21.31%
## 622: 27.07% 32.81% 21.33%
## 623: 27.03% 32.73% 21.33%
## 624: 27.00% 32.69% 21.31%
## 625: 27.03% 32.72% 21.33%
## 626: 27.01% 32.71% 21.32%
## 627: 27.01% 32.73% 21.28%
## 628: 27.02% 32.79% 21.24%
## 629: 26.99% 32.69% 21.29%
## 630: 27.00% 32.71% 21.29%
## 631: 26.96% 32.66% 21.27%
## 632: 26.97% 32.65% 21.30%
## 633: 26.99% 32.73% 21.24%
## 634: 26.96% 32.69% 21.24%
## 635: 27.00% 32.76% 21.24%
## 636: 27.00% 32.78% 21.23%
## 637: 26.97% 32.71% 21.22%
## 638: 26.97% 32.69% 21.24%
## 639: 26.99% 32.75% 21.24%
## 640: 27.01% 32.76% 21.26%
## 641: 27.01% 32.75% 21.27%
## 642: 27.04% 32.79% 21.29%
## 643: 27.04% 32.76% 21.31%
## 644: 26.99% 32.72% 21.26%
## 645: 27.02% 32.73% 21.30%
## 646: 27.05% 32.77% 21.33%
## 647: 27.02% 32.77% 21.27%
## 648: 27.05% 32.79% 21.32%
## 649: 27.01% 32.74% 21.28%
## 650: 27.03% 32.76% 21.30%
## 651: 27.03% 32.75% 21.30%
## 652: 27.03% 32.74% 21.33%
## 653: 27.03% 32.77% 21.29%
## 654: 27.03% 32.78% 21.27%
## 655: 27.02% 32.76% 21.29%
## 656: 27.02% 32.76% 21.29%
## 657: 27.02% 32.75% 21.30%
## 658: 27.04% 32.78% 21.29%
## 659: 26.99% 32.71% 21.27%
## 660: 27.00% 32.76% 21.24%
## 661: 27.04% 32.77% 21.31%
## 662: 27.02% 32.78% 21.27%
## 663: 27.02% 32.77% 21.28%
## 664: 27.04% 32.76% 21.32%
## 665: 27.03% 32.78% 21.28%
## 666: 27.00% 32.75% 21.25%
## 667: 27.01% 32.75% 21.27%
## 668: 26.98% 32.73% 21.24%
## 669: 27.01% 32.73% 21.29%
## 670: 27.04% 32.79% 21.30%
## 671: 27.02% 32.78% 21.27%
## 672: 27.00% 32.76% 21.24%
## 673: 27.03% 32.77% 21.29%
## 674: 27.03% 32.85% 21.22%
## 675: 27.03% 32.81% 21.25%
## 676: 27.00% 32.78% 21.21%
## 677: 27.03% 32.80% 21.27%
## 678: 27.03% 32.80% 21.27%
## 679: 27.02% 32.77% 21.27%
## 680: 27.02% 32.81% 21.24%
## 681: 26.99% 32.76% 21.22%
## 682: 27.00% 32.76% 21.25%
## 683: 27.01% 32.75% 21.28%
## 684: 27.04% 32.80% 21.28%
## 685: 27.00% 32.71% 21.29%
## 686: 27.03% 32.74% 21.31%
## 687: 27.02% 32.76% 21.28%
## 688: 27.04% 32.76% 21.32%
## 689: 27.03% 32.76% 21.31%
## 690: 27.06% 32.77% 21.34%
## 691: 27.03% 32.75% 21.31%
## 692: 27.03% 32.77% 21.30%
## 693: 27.05% 32.77% 21.34%
## 694: 27.04% 32.78% 21.29%
## 695: 27.04% 32.80% 21.28%
## 696: 27.02% 32.77% 21.27%
## 697: 27.00% 32.73% 21.27%
## 698: 26.98% 32.71% 21.25%
## 699: 27.00% 32.71% 21.30%
## 700: 27.02% 32.75% 21.30%
## 701: 27.03% 32.78% 21.29%
## 702: 27.01% 32.75% 21.27%
## 703: 27.00% 32.75% 21.25%
## 704: 27.03% 32.78% 21.29%
## 705: 27.01% 32.75% 21.28%
## 706: 27.03% 32.79% 21.27%
## 707: 27.03% 32.79% 21.27%
## 708: 27.02% 32.78% 21.26%
## 709: 27.01% 32.75% 21.28%
## 710: 27.00% 32.76% 21.25%
## 711: 27.03% 32.78% 21.29%
## 712: 27.03% 32.78% 21.28%
## 713: 27.01% 32.74% 21.29%
## 714: 27.05% 32.79% 21.32%
## 715: 27.04% 32.76% 21.32%
## 716: 27.05% 32.78% 21.32%
## 717: 27.04% 32.78% 21.30%
## 718: 27.05% 32.77% 21.33%
## 719: 27.05% 32.78% 21.33%
## 720: 27.04% 32.76% 21.31%
## 721: 27.04% 32.76% 21.33%
## 722: 27.04% 32.76% 21.31%
## 723: 27.02% 32.74% 21.29%
## 724: 27.05% 32.77% 21.33%
## 725: 27.03% 32.77% 21.28%
## 726: 27.03% 32.76% 21.30%
## 727: 27.01% 32.74% 21.27%
## 728: 27.02% 32.73% 21.30%
## 729: 27.02% 32.77% 21.28%
## 730: 27.03% 32.76% 21.30%
## 731: 27.01% 32.73% 21.30%
## 732: 27.02% 32.74% 21.29%
## 733: 27.04% 32.78% 21.31%
## 734: 27.06% 32.82% 21.31%
## 735: 27.05% 32.78% 21.32%
## 736: 27.03% 32.78% 21.28%
## 737: 27.05% 32.79% 21.30%
## 738: 27.05% 32.77% 21.33%
## 739: 27.02% 32.74% 21.29%
## 740: 27.04% 32.77% 21.31%
## 741: 27.03% 32.76% 21.31%
## 742: 27.05% 32.77% 21.33%
## 743: 27.05% 32.80% 21.31%
## 744: 27.03% 32.77% 21.29%
## 745: 27.03% 32.81% 21.25%
## 746: 27.05% 32.78% 21.32%
## 747: 27.03% 32.76% 21.31%
## 748: 27.03% 32.75% 21.31%
## 749: 27.06% 32.79% 21.33%
## 750: 27.07% 32.81% 21.34%
## 751: 27.08% 32.82% 21.35%
## 752: 27.06% 32.81% 21.32%
## 753: 27.08% 32.82% 21.33%
## 754: 27.08% 32.80% 21.36%
## 755: 27.11% 32.82% 21.40%
## 756: 27.07% 32.78% 21.36%
## 757: 27.09% 32.82% 21.35%
## 758: 27.08% 32.82% 21.33%
## 759: 27.07% 32.80% 21.35%
## 760: 27.09% 32.83% 21.35%
## 761: 27.06% 32.81% 21.32%
## 762: 27.09% 32.85% 21.33%
## 763: 27.10% 32.87% 21.33%
## 764: 27.07% 32.85% 21.30%
## 765: 27.09% 32.89% 21.30%
## 766: 27.04% 32.82% 21.27%
## 767: 27.05% 32.84% 21.27%
## 768: 27.07% 32.83% 21.31%
## 769: 27.05% 32.79% 21.31%
## 770: 27.06% 32.80% 21.32%
## 771: 27.08% 32.82% 21.33%
## 772: 27.07% 32.80% 21.33%
## 773: 27.08% 32.80% 21.35%
## 774: 27.06% 32.76% 21.35%
## 775: 27.08% 32.78% 21.39%
## 776: 27.05% 32.75% 21.35%
## 777: 27.08% 32.80% 21.37%
## 778: 27.09% 32.79% 21.40%
## 779: 27.07% 32.78% 21.35%
## 780: 27.05% 32.75% 21.35%
## 781: 27.08% 32.79% 21.37%
## 782: 27.05% 32.78% 21.33%
## 783: 27.07% 32.78% 21.35%
## 784: 27.09% 32.82% 21.36%
## 785: 27.08% 32.82% 21.35%
## 786: 27.09% 32.83% 21.35%
## 787: 27.08% 32.82% 21.34%
## 788: 27.07% 32.77% 21.37%
## 789: 27.06% 32.78% 21.33%
## 790: 27.06% 32.78% 21.35%
## 791: 27.08% 32.80% 21.36%
## 792: 27.07% 32.79% 21.34%
## 793: 27.10% 32.81% 21.39%
## 794: 27.07% 32.78% 21.35%
## 795: 27.09% 32.77% 21.40%
## 796: 27.07% 32.78% 21.35%
## 797: 27.07% 32.77% 21.37%
## 798: 27.08% 32.78% 21.38%
## 799: 27.09% 32.82% 21.35%
## 800: 27.10% 32.83% 21.37%
## 801: 27.07% 32.77% 21.38%
## 802: 27.07% 32.79% 21.36%
## 803: 27.08% 32.80% 21.37%
## 804: 27.08% 32.79% 21.37%
## 805: 27.06% 32.77% 21.35%
## 806: 27.08% 32.80% 21.36%
## 807: 27.09% 32.82% 21.37%
## 808: 27.07% 32.79% 21.35%
## 809: 27.06% 32.76% 21.37%
## 810: 27.08% 32.78% 21.38%
## 811: 27.07% 32.78% 21.37%
## 812: 27.05% 32.73% 21.36%
## 813: 27.04% 32.72% 21.37%
## 814: 27.09% 32.78% 21.40%
## 815: 27.08% 32.74% 21.42%
## 816: 27.06% 32.72% 21.41%
## 817: 27.05% 32.73% 21.37%
## 818: 27.04% 32.73% 21.36%
## 819: 27.07% 32.78% 21.35%
## 820: 27.05% 32.74% 21.37%
## 821: 27.08% 32.75% 21.41%
## 822: 27.07% 32.75% 21.40%
## 823: 27.06% 32.75% 21.37%
## 824: 27.06% 32.77% 21.35%
## 825: 27.04% 32.76% 21.33%
## 826: 27.03% 32.72% 21.34%
## 827: 27.03% 32.73% 21.33%
## 828: 27.05% 32.74% 21.37%
## 829: 27.04% 32.73% 21.35%
## 830: 27.05% 32.73% 21.37%
## 831: 27.03% 32.73% 21.33%
## 832: 27.03% 32.72% 21.33%
## 833: 27.04% 32.76% 21.31%
## 834: 27.04% 32.73% 21.34%
## 835: 27.04% 32.76% 21.33%
## 836: 27.02% 32.74% 21.31%
## 837: 27.04% 32.76% 21.31%
## 838: 27.05% 32.74% 21.35%
## 839: 27.04% 32.76% 21.33%
## 840: 27.04% 32.79% 21.30%
## 841: 27.04% 32.75% 21.33%
## 842: 27.04% 32.75% 21.32%
## 843: 27.04% 32.76% 21.33%
## 844: 27.02% 32.72% 21.33%
## 845: 27.02% 32.73% 21.31%
## 846: 27.03% 32.76% 21.31%
## 847: 27.02% 32.74% 21.29%
## 848: 27.02% 32.72% 21.32%
## 849: 27.04% 32.76% 21.32%
## 850: 27.01% 32.73% 21.29%
## 851: 26.99% 32.73% 21.26%
## 852: 26.97% 32.70% 21.25%
## 853: 26.96% 32.65% 21.27%
## 854: 27.00% 32.72% 21.28%
## 855: 26.97% 32.67% 21.27%
## 856: 26.99% 32.69% 21.30%
## 857: 26.99% 32.69% 21.29%
## 858: 27.00% 32.71% 21.29%
## 859: 27.00% 32.71% 21.28%
## 860: 27.02% 32.71% 21.33%
## 861: 27.00% 32.72% 21.28%
## 862: 27.01% 32.73% 21.28%
## 863: 27.00% 32.75% 21.26%
## 864: 26.97% 32.71% 21.24%
## 865: 26.99% 32.73% 21.25%
## 866: 26.97% 32.72% 21.23%
## 867: 27.00% 32.73% 21.27%
## 868: 26.99% 32.70% 21.28%
## 869: 27.00% 32.73% 21.27%
## 870: 27.03% 32.76% 21.30%
## 871: 27.03% 32.76% 21.30%
## 872: 27.02% 32.76% 21.27%
## 873: 27.02% 32.77% 21.26%
## 874: 27.02% 32.81% 21.23%
## 875: 27.03% 32.81% 21.25%
## 876: 27.01% 32.77% 21.26%
## 877: 27.03% 32.79% 21.27%
## 878: 27.04% 32.79% 21.29%
## 879: 27.03% 32.76% 21.29%
## 880: 27.01% 32.73% 21.28%
## 881: 27.00% 32.75% 21.25%
## 882: 27.01% 32.77% 21.25%
## 883: 27.01% 32.77% 21.24%
## 884: 27.01% 32.78% 21.25%
## 885: 27.01% 32.79% 21.24%
## 886: 27.01% 32.76% 21.26%
## 887: 27.02% 32.78% 21.26%
## 888: 27.01% 32.80% 21.23%
## 889: 27.00% 32.79% 21.22%
## 890: 26.99% 32.80% 21.18%
## 891: 27.00% 32.84% 21.17%
## 892: 27.00% 32.80% 21.20%
## 893: 27.00% 32.80% 21.21%
## 894: 26.98% 32.78% 21.19%
## 895: 26.99% 32.80% 21.18%
## 896: 26.98% 32.78% 21.19%
## 897: 26.99% 32.78% 21.20%
## 898: 27.00% 32.79% 21.22%
## 899: 26.99% 32.79% 21.18%
## 900: 27.01% 32.79% 21.22%
## 901: 26.99% 32.78% 21.21%
## 902: 27.00% 32.78% 21.23%
## 903: 26.99% 32.79% 21.20%
## 904: 27.00% 32.77% 21.22%
## 905: 26.99% 32.78% 21.20%
## 906: 26.98% 32.76% 21.21%
## 907: 26.99% 32.78% 21.21%
## 908: 27.00% 32.78% 21.23%
## 909: 26.99% 32.74% 21.23%
## 910: 26.97% 32.73% 21.21%
## 911: 26.96% 32.69% 21.23%
## 912: 26.98% 32.72% 21.24%
## 913: 26.99% 32.73% 21.25%
## 914: 26.95% 32.70% 21.21%
## 915: 26.99% 32.74% 21.23%
## 916: 27.01% 32.78% 21.24%
## 917: 26.99% 32.76% 21.22%
## 918: 26.98% 32.77% 21.20%
## 919: 26.99% 32.79% 21.19%
## 920: 26.99% 32.79% 21.19%
## 921: 26.99% 32.77% 21.22%
## 922: 26.99% 32.78% 21.20%
## 923: 27.01% 32.77% 21.24%
## 924: 26.98% 32.73% 21.24%
## 925: 26.99% 32.78% 21.20%
## 926: 27.04% 32.84% 21.25%
## 927: 27.02% 32.82% 21.21%
## 928: 27.01% 32.80% 21.22%
## 929: 27.00% 32.82% 21.18%
## 930: 26.98% 32.80% 21.17%
## 931: 27.02% 32.85% 21.19%
## 932: 26.99% 32.81% 21.18%
## 933: 27.00% 32.80% 21.20%
## 934: 26.99% 32.81% 21.17%
## 935: 27.01% 32.87% 21.15%
## 936: 27.00% 32.83% 21.17%
## 937: 27.01% 32.84% 21.18%
## 938: 27.02% 32.83% 21.20%
## 939: 27.01% 32.82% 21.20%
## 940: 27.01% 32.83% 21.18%
## 941: 26.99% 32.84% 21.14%
## 942: 27.00% 32.84% 21.17%
## 943: 27.00% 32.85% 21.15%
## 944: 26.99% 32.86% 21.12%
## 945: 26.96% 32.83% 21.09%
## 946: 26.97% 32.82% 21.13%
## 947: 27.00% 32.87% 21.12%
## 948: 27.01% 32.87% 21.14%
## 949: 27.01% 32.91% 21.11%
## 950: 26.98% 32.84% 21.12%
## 951: 26.99% 32.86% 21.13%
## 952: 26.97% 32.84% 21.10%
## 953: 26.99% 32.86% 21.13%
## 954: 26.99% 32.85% 21.13%
## 955: 26.99% 32.87% 21.11%
## 956: 27.01% 32.90% 21.11%
## 957: 27.00% 32.89% 21.11%
## 958: 26.98% 32.88% 21.08%
## 959: 26.97% 32.87% 21.07%
## 960: 26.97% 32.84% 21.10%
## 961: 27.00% 32.87% 21.12%
## 962: 26.98% 32.85% 21.13%
## 963: 26.96% 32.80% 21.11%
## 964: 26.97% 32.82% 21.12%
## 965: 26.97% 32.82% 21.12%
## 966: 26.98% 32.82% 21.14%
## 967: 27.02% 32.86% 21.17%
## 968: 26.97% 32.82% 21.13%
## 969: 27.02% 32.88% 21.15%
## 970: 27.00% 32.83% 21.16%
## 971: 27.00% 32.84% 21.15%
## 972: 27.00% 32.85% 21.15%
## 973: 26.99% 32.85% 21.13%
## 974: 26.96% 32.82% 21.11%
## 975: 26.97% 32.80% 21.14%
## 976: 26.99% 32.80% 21.18%
## 977: 26.98% 32.79% 21.17%
## 978: 26.97% 32.79% 21.15%
## 979: 26.96% 32.76% 21.16%
## 980: 27.00% 32.85% 21.15%
## 981: 26.97% 32.82% 21.13%
## 982: 27.00% 32.83% 21.16%
## 983: 27.01% 32.85% 21.16%
## 984: 27.00% 32.86% 21.15%
## 985: 27.01% 32.86% 21.16%
## 986: 26.98% 32.82% 21.15%
## 987: 27.00% 32.82% 21.17%
## 988: 27.01% 32.83% 21.18%
## 989: 27.00% 32.82% 21.17%
## 990: 26.99% 32.83% 21.15%
## 991: 27.00% 32.85% 21.15%
## 992: 27.02% 32.90% 21.13%
## 993: 27.01% 32.87% 21.15%
## 994: 27.01% 32.86% 21.15%
## 995: 27.02% 32.88% 21.16%
## 996: 26.99% 32.83% 21.15%
## 997: 27.00% 32.86% 21.15%
## 998: 27.00% 32.83% 21.17%
## 999: 27.01% 32.85% 21.17%
## 1000: 27.01% 32.85% 21.17%summary(rf_synthetic)## Length Class Mode
## call 7 -none- call
## type 1 -none- character
## predicted 48909 factor numeric
## err.rate 3000 -none- numeric
## confusion 6 -none- numeric
## votes 97818 matrix numeric
## oob.times 48909 -none- numeric
## classes 2 -none- character
## importance 39 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 48909 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms callmake predictions on the test set
tree.predict <- predict(rf_synthetic, test_rf, type = "class")evaluate the results
confusionMatrix(tree.predict, test_rf$Performance.Tag, positive = "yes")## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 17970 734
## yes 2068 186
##
## Accuracy : 0.8663
## 95% CI : (0.8616, 0.8709)
## No Information Rate : 0.9561
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0585
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.202174
## Specificity : 0.896796
## Pos Pred Value : 0.082520
## Neg Pred Value : 0.960757
## Prevalence : 0.043897
## Detection Rate : 0.008875
## Detection Prevalence : 0.107548
## Balanced Accuracy : 0.549485
##
## 'Positive' Class : yes
## #In terms of probbability
rf_pred_synthetic <- predict(rf_synthetic, test_rf, type = "prob")Let’s find out the optimal cutoff value for probalility with synthetic data
#Cutoff for randomforest to assign yes or no
perform_fn_rf <- function(cutoff)
{
predicted_response <- as.factor(ifelse(rf_pred_synthetic[, 2] >= cutoff, "yes", "no"))
conf <- confusionMatrix(predicted_response, test_rf$Performance.Tag, positive = "yes")
acc <- conf$overall[1]
sens <- conf$byClass[1]
spec <- conf$byClass[2]
OUT_rf <- t(as.matrix(c(sens, spec, acc)))
colnames(OUT_rf) <- c("sensitivity", "specificity", "accuracy")
return(OUT_rf)
}creating cutoff values from 0.01 to 0.99
s = seq(.01,.99,length=100)
OUT_rf = matrix(0,100,3)calculate the sens, spec and acc for different cutoff values
for(i in 1:100)
{
OUT_rf[i,] = perform_fn_rf(s[i])
} plotting cutoffs
plot(s, OUT_rf[,1],xlab="Cutoff",ylab="Value",cex.lab=1.5,cex.axis=1.5,ylim=c(0,1),type="l",lwd=2,axes=FALSE,col=2)
axis(1,seq(0,1,length=5),seq(0,1,length=5),cex.lab=1.5)
axis(2,seq(0,1,length=5),seq(0,1,length=5),cex.lab=1.5)
lines(s,OUT_rf[,2],col="orange",lwd=2)
lines(s,OUT_rf[,3],col=4,lwd=2)
box()
legend(0,.50,col=c(1,"orange",2,"darkred"),lwd=c(1,1,1,1),c("Sensitivity","Specificity","Accuracy"))
cutoff_rf <- s[which(abs(OUT_rf[,1]-OUT_rf[,2])<0.01)]
cutoff_rf ## numeric(0)The plot shows that cutoff value of around 12.8% optimizes sensitivity and accuracy The cut off is too low.
test_pred_optimal<- factor(ifelse(rf_pred_synthetic[, 2] >= 0.22, "yes", "no"))
conf_rf <- confusionMatrix(test_pred_optimal, test_rf$Performance.Tag, positive = "yes")
conf_rf## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 14084 432
## yes 5954 488
##
## Accuracy : 0.6953
## 95% CI : (0.689, 0.7015)
## No Information Rate : 0.9561
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0604
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.53043
## Specificity : 0.70286
## Pos Pred Value : 0.07575
## Neg Pred Value : 0.97024
## Prevalence : 0.04390
## Detection Rate : 0.02328
## Detection Prevalence : 0.30738
## Balanced Accuracy : 0.61665
##
## 'Positive' Class : yes
## KS - statistic -Random Forest - Test Data
test_actual_default<-as.factor(ifelse(test_rf$Performance.Tag == "yes", 1,0))
pred_object_test<- prediction(as.numeric(test_pred_optimal), as.numeric(test_actual_default))
performance_measures_test<- performance(pred_object_test, "tpr", "fpr")
ks_table_test <- attr(performance_measures_test, "y.values")[[1]] -
(attr(performance_measures_test, "x.values")[[1]])
max(ks_table_test)## [1] 0.2332993KS-statistic is 23.37%
#ROC Curve
auc_ROCR <- performance(pred_object_test, measure = "auc")
auc_ROCR <- auc_ROCR@y.values[[1]]
auc_ROCR ## [1] 0.6166497Area under curve is : 0.6168613
pd <- data.frame(fpr=unlist(performance_measures_test@x.values), tpr=unlist(performance_measures_test@y.values))
ggplot(pd ,aes(x=fpr, y=tpr)) +
geom_line(colour="blue") +
geom_line(data=data.frame(), aes(x=c(0,1), y=c(0,1)), colour="red") +
labs(x="False Positive Rate",
y="True Positive Rate",
title="ROC Curve for Random Forest",
caption="xgboost") +
theme(axis.text.x=element_text(hjust=1))+
annotate("text", x=0.4, y=0.00, hjust=0, vjust=0, size=5,
label=paste("AUC =", round(auc_ROCR, 3))) 
gini<-(auc_ROCR*2)-1
gini## [1] 0.2332993library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:randomForest':
##
## combine## The following object is masked from 'package:car':
##
## recode## The following object is masked from 'package:MASS':
##
## select## The following object is masked from 'package:gridExtra':
##
## combine## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlift <- function(labels , predicted_prob,groups=10) {
if(is.factor(labels)) labels <- as.integer(as.character(labels ))
if(is.factor(predicted_prob)) predicted_prob <- as.integer(as.character(predicted_prob))
helper = data.frame(cbind(labels , predicted_prob))
helper[,"bucket"] = ntile(-helper[,"predicted_prob"], groups)
gaintable = helper %>% group_by(bucket) %>%
summarise_at(vars(labels ), funs(total = n(),
totalresp=sum(., na.rm = TRUE))) %>%
mutate(Cumresp = cumsum(totalresp),
Gain=Cumresp/sum(totalresp)*100,
Cumlift=Gain/(bucket*(100/groups)))
return(gaintable)
}
lift_decile_info = lift(test_actual_default, test_pred, groups = 10)## Warning: `funs()` was deprecated in dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))print(lift_decile_info)## # A tibble: 10 x 6
## bucket total totalresp Cumresp Gain Cumlift
## <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 2096 159 159 17.3 1.73
## 2 2 2096 160 319 34.7 1.73
## 3 3 2096 146 465 50.5 1.68
## 4 4 2096 104 569 61.8 1.55
## 5 5 2096 112 681 74.0 1.48
## 6 6 2096 80 761 82.7 1.38
## 7 7 2096 48 809 87.9 1.26
## 8 8 2096 42 851 92.5 1.16
## 9 9 2095 38 889 96.6 1.07
## 10 10 2095 31 920 100 1write.csv(lift_decile_info, "lift.csv", row.names = FALSE)#Plotting Gain Chart
ggplot(lift_decile_info, aes(x = bucket)) +
labs(x = "Decile", y="Gain (%)")+
geom_point(data=lift_decile_info,aes(x=bucket,y=Gain),color='#FF6666', group = 1,size=2,shape=21,stroke=2.5)+
geom_line(data=lift_decile_info,aes(x=bucket,y=Gain),color='#07843b',size=1, group = 1)+
theme(panel.grid.minor = element_line(colour = "black", size = 0.5)) +
scale_x_continuous(breaks = seq(1, 10, 1))+
scale_y_continuous(breaks = seq(20, 100, 10),labels=function(x) paste0(x,"%"))+
ggtitle("Gain Chart")
#Plotting Lift Chart
ggplot(lift_decile_info, aes(x = bucket)) +
labs(x = "Decile", y="Lift")+
geom_point(data=lift_decile_info,aes(x=bucket,y=Cumlift),color='#07843b', group = 1,size=2,shape=21,stroke=2.5)+
geom_line(data=lift_decile_info,aes(x=bucket,y=Cumlift),color='#FF6666',size=1, group = 1)+
theme(panel.grid.minor = element_line(colour = "black", size = 0.5)) +
scale_x_continuous(breaks = seq(1, 10, 1))+
scale_y_continuous(breaks = seq(0.4, 4, 0.4))+
ggtitle("Lift Chart")
* 4) Model Evaluation
credit score generation process
Build scorecard with the good to bad odds of 10 to 1 at a score of 400 doubling every 20 points.
str(final_df)## 'data.frame': 69867 obs. of 40 variables:
## $ Performance.Tag : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Age : num 0.201 0.807 -0.405 -0.102 0.807 ...
## $ Income : num -0.156 1.007 -0.996 1.007 0.361 ...
## $ No.of.months.in.current.residence : num -0.776 -0.776 -0.776 -0.776 1.775 ...
## $ No.of.months.in.current.company : num -0.55 0.482 -0.894 -0.943 -1.042 ...
## $ Total.No.of.Trades : num -0.0244 -0.7234 -0.7234 -0.8632 -0.0244 ...
## $ Outstanding.Balance : num -0.391 1.327 1.311 -0.972 1.688 ...
## $ Avgas.CC.Utilization.in.last.12.months : num 0.616 -0.876 -0.774 -0.571 1.26 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : num 1.485 -0.492 -0.492 -0.492 -0.492 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : num 2.084 -0.507 -0.507 -0.507 -0.507 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : num 1.475 -0.523 -0.523 -0.523 -0.523 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : num 2.076 -0.543 -0.543 -0.543 -0.543 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : num 1.367 -0.591 -0.591 -0.591 0.388 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : num 1.02 -0.59 -0.59 -0.59 -0.59 ...
## $ No.of.trades.opened.in.last.6.months : num 0.343 -0.617 -1.098 -0.617 -0.137 ...
## $ No.of.trades.opened.in.last.12.months : num 0.2385 -0.7428 -1.1353 -0.9391 0.0422 ...
## $ No.of.PL.trades.opened.in.last.6.months : num 0.599 -0.879 -0.879 -0.879 0.599 ...
## $ No.of.PL.trades.opened.in.last.6.months.1 : num 0.599 -0.879 -0.879 -0.879 0.599 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : num 0.122 -0.886 -0.886 -0.886 0.122 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: num 0.962 -0.976 -0.976 -0.976 0.132 ...
## $ No.of.PL.trades.opened.in.last.12.months : num 0.675 -0.975 -0.975 -0.975 0.263 ...
## $ Presence.of.open.home.loan : num -0.591 1.693 1.693 -0.591 1.693 ...
## $ Presence.of.open.auto.loan : num -0.305 -0.305 -0.305 -0.305 -0.305 ...
## $ Gender.xF : num 0 0 0 0 0 0 0 1 0 0 ...
## $ Gender.xM : num 1 1 1 1 1 1 1 0 1 1 ...
## $ Marital.Status..at.the.time.of.application..xMarried : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Marital.Status..at.the.time.of.application..xSingle : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Education.xBachelor : num 0 0 0 1 0 1 0 1 0 0 ...
## $ Education.xMasters : num 1 0 1 0 0 0 1 0 1 0 ...
## $ Education.xOthers : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Education.xPhd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Education.xProfessional : num 0 1 0 0 1 0 0 0 0 1 ...
## $ Profession.xSAL : num 0 1 0 1 0 1 0 1 1 0 ...
## $ Profession.xSE : num 1 0 1 0 1 0 1 0 0 1 ...
## $ Profession.xSE_PROF : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xCompany.provided : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xLiving.with.Parents : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xOthers : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xOwned : num 0 0 0 1 1 0 0 0 0 1 ...
## $ Type.of.residence.xRented : num 1 1 1 0 0 1 1 1 1 0 ...final_df$perdict_default <- predict(final_lr_model, type = "response", newdata = final_df[,-1])
final_df$predict_NonDefault <- 1 - final_df$perdict_default
final_df$odds <- log(final_df$predict_NonDefault/final_df$perdict_default)
Offset = 400
PDO = 20
log_odds=10
Factor = PDO/log(2)
Factor ## [1] 28.8539final_df$Score = ceiling(Offset + (Factor*final_df$odds))
str(final_df$Score)## num [1:69867] 384 422 419 420 400 420 401 418 417 420 ...summary(final_df$Score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 337.0 394.0 405.0 404.4 419.0 423.0min - 337 to max - 423
quantile(final_df$Score,seq(0,1,0.2)) ## 0% 20% 40% 60% 80% 100%
## 337 391 400 415 419 423From the plot it is evident that score cut off could be set to 419
cutoff_score =419
num_of_defaults_below_419<-length(which(final_df$Performance.Tag==1 & final_df$Score<419))
total_no_of_defaults<-length(which(final_df$Performance.Tag==1))
pc_defaults_covered_under_419<-ceiling((num_of_defaults_below_419/total_no_of_defaults)*100)
pc_defaults_covered_under_419## [1] 90ggplot(final_df, aes(x = Score,color=Performance.Tag))+geom_bar(fill="pink")+geom_vline(aes(xintercept = cutoff_score))+labs(x="Score",y="Count",title="Score Distribution for all applicants")+annotate("text", x=350,y=4000, colour = "black",hjust=0, vjust=0, size=7,
label=paste("Defaults covered by 419 cut off : " ,pc_defaults_covered_under_419,"%"))
Predicting score for rejected applicants
str(rejected_applicants)## 'data.frame': 1425 obs. of 28 variables:
## $ Performance.Tag : int NA NA NA NA NA NA NA NA NA NA ...
## $ Age : int 60 55 39 39 51 53 29 34 35 56 ...
## $ Gender : Factor w/ 3 levels "","F","M": 2 3 3 2 2 3 3 2 3 3 ...
## $ Marital.Status..at.the.time.of.application. : Factor w/ 3 levels "","Married","Single": 2 2 2 3 3 2 2 3 2 2 ...
## $ No.of.dependents : int 3 4 3 3 3 3 3 1 2 3 ...
## $ Income : num 24 31 34 4.5 39 4.5 21 19 4.5 4.5 ...
## $ Education : Factor w/ 6 levels "","Bachelor",..: 2 3 6 2 2 6 2 6 2 6 ...
## $ Profession : Factor w/ 4 levels "","SAL","SE",..: 2 3 2 2 4 3 2 3 2 2 ...
## $ Type.of.residence : Factor w/ 6 levels "","Company provided",..: 6 6 6 6 6 6 6 6 5 2 ...
## $ No.of.months.in.current.residence : int 12 37 6 86 12 7 15 57 92 26 ...
## $ No.of.months.in.current.company : int 53 29 8 3 3 27 19 52 7 3 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : int 1 1 2 1 2 0 2 1 1 2 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : int 2 1 4 2 2 1 3 3 1 4 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : int 3 2 5 3 3 2 3 3 2 4 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : int 2 2 3 2 3 1 2 3 3 4 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : int 3 2 5 3 2 2 3 4 2 5 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : int 5 2 7 3 3 3 5 5 2 5 ...
## $ Avgas.CC.Utilization.in.last.12.months : int 82 37 60 64 52 93 71 33 47 27 ...
## $ No.of.trades.opened.in.last.6.months : int 3 3 3 0 1 5 4 2 1 5 ...
## $ No.of.trades.opened.in.last.12.months : int 7 9 10 2 3 14 10 7 5 10 ...
## $ No.of.PL.trades.opened.in.last.6.months : int 2 2 3 0 0 4 2 1 0 3 ...
## $ No.of.PL.trades.opened.in.last.12.months : int 3 6 6 1 1 7 5 4 3 5 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : int 0 3 1 3 3 2 2 4 2 1 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: int 3 3 4 5 7 3 3 6 5 2 ...
## $ Presence.of.open.home.loan : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Outstanding.Balance : int 585600 1141868 1082090 178605 188186 1640589 940347 845488 527485 1253049 ...
## $ Total.No.of.Trades : int 8 10 10 3 3 15 11 7 6 10 ...
## $ Presence.of.open.auto.loan : int 0 0 0 0 0 1 0 0 0 1 ...rejects_for_scaling<-rejected_applicants[numeric_cols]
rejected_scaled_data<-data.frame(sapply(rejects_for_scaling, scale))
str(rejected_scaled_data)## 'data.frame': 1425 obs. of 22 variables:
## $ Age : num 1.754 1.257 -0.335 -0.335 0.859 ...
## $ Income : num 0.529 1.04 1.259 -0.893 1.624 ...
## $ No.of.months.in.current.residence : num -0.633 0.144 -0.819 1.665 -0.633 ...
## $ No.of.months.in.current.company : num 1.551 0.345 -0.711 -0.963 -0.963 ...
## $ Total.No.of.Trades : num -0.3 0.477 0.477 -2.243 -2.243 ...
## $ Outstanding.Balance : num -0.5574 0.1172 0.0447 -1.0509 -1.0393 ...
## $ Avgas.CC.Utilization.in.last.12.months : num 1.446 -0.658 0.417 0.604 0.043 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : num -0.401 -0.401 0.866 -0.401 0.866 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : num -0.311 -1.237 1.542 -0.311 -0.311 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : num -0.167 -0.962 1.424 -0.167 -0.167 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : num -0.189 -0.189 0.801 -0.189 0.801 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : num -0.168 -0.971 1.438 -0.168 -0.971 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : num 0.651 -1.487 2.077 -0.774 -0.774 ...
## $ No.of.trades.opened.in.last.6.months : num 0.055 0.055 0.055 -2.395 -1.578 ...
## $ No.of.trades.opened.in.last.12.months : num -0.353 0.446 0.845 -2.349 -1.949 ...
## $ No.of.PL.trades.opened.in.last.6.months : num -0.0535 -0.0535 0.8874 -1.9353 -1.9353 ...
## $ No.of.PL.trades.opened.in.last.6.months.1 : num -0.0535 -0.0535 0.8874 -1.9353 -1.9353 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : num -1.899 0.899 -0.966 0.899 0.899 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: num -0.7026 -0.7026 -0.0243 0.654 2.0106 ...
## $ No.of.PL.trades.opened.in.last.12.months : num -0.72 1.31 1.31 -2.07 -2.07 ...
## $ Presence.of.open.home.loan : num -0.331 -0.331 -0.331 -0.331 -0.331 ...
## $ Presence.of.open.auto.loan : num -0.279 -0.279 -0.279 -0.279 -0.279 ...rejects_for_dummies<-rejected_applicants[fact_cols]creating dummy variables for factor attributes
rejected_dummies<- data.frame(sapply(rejects_for_dummies,function(x) data.frame(model.matrix(~x-1,data =rejects_for_dummies))[,-1])) combine all relevant columns to build final training data
rejected_final_df<- cbind(rejected_scaled_data,rejected_dummies)
str(rejected_final_df)## 'data.frame': 1425 obs. of 39 variables:
## $ Age : num 1.754 1.257 -0.335 -0.335 0.859 ...
## $ Income : num 0.529 1.04 1.259 -0.893 1.624 ...
## $ No.of.months.in.current.residence : num -0.633 0.144 -0.819 1.665 -0.633 ...
## $ No.of.months.in.current.company : num 1.551 0.345 -0.711 -0.963 -0.963 ...
## $ Total.No.of.Trades : num -0.3 0.477 0.477 -2.243 -2.243 ...
## $ Outstanding.Balance : num -0.5574 0.1172 0.0447 -1.0509 -1.0393 ...
## $ Avgas.CC.Utilization.in.last.12.months : num 1.446 -0.658 0.417 0.604 0.043 ...
## $ No.of.times.90.DPD.or.worse.in.last.6.months : num -0.401 -0.401 0.866 -0.401 0.866 ...
## $ No.of.times.60.DPD.or.worse.in.last.6.months : num -0.311 -1.237 1.542 -0.311 -0.311 ...
## $ No.of.times.30.DPD.or.worse.in.last.6.months : num -0.167 -0.962 1.424 -0.167 -0.167 ...
## $ No.of.times.90.DPD.or.worse.in.last.12.months : num -0.189 -0.189 0.801 -0.189 0.801 ...
## $ No.of.times.60.DPD.or.worse.in.last.12.months : num -0.168 -0.971 1.438 -0.168 -0.971 ...
## $ No.of.times.30.DPD.or.worse.in.last.12.months : num 0.651 -1.487 2.077 -0.774 -0.774 ...
## $ No.of.trades.opened.in.last.6.months : num 0.055 0.055 0.055 -2.395 -1.578 ...
## $ No.of.trades.opened.in.last.12.months : num -0.353 0.446 0.845 -2.349 -1.949 ...
## $ No.of.PL.trades.opened.in.last.6.months : num -0.0535 -0.0535 0.8874 -1.9353 -1.9353 ...
## $ No.of.PL.trades.opened.in.last.6.months.1 : num -0.0535 -0.0535 0.8874 -1.9353 -1.9353 ...
## $ No.of.Inquiries.in.last.6.months..excluding.home...auto.loans. : num -1.899 0.899 -0.966 0.899 0.899 ...
## $ No.of.Inquiries.in.last.12.months..excluding.home...auto.loans.: num -0.7026 -0.7026 -0.0243 0.654 2.0106 ...
## $ No.of.PL.trades.opened.in.last.12.months : num -0.72 1.31 1.31 -2.07 -2.07 ...
## $ Presence.of.open.home.loan : num -0.331 -0.331 -0.331 -0.331 -0.331 ...
## $ Presence.of.open.auto.loan : num -0.279 -0.279 -0.279 -0.279 -0.279 ...
## $ Gender.xF : num 1 0 0 1 1 0 0 1 0 0 ...
## $ Gender.xM : num 0 1 1 0 0 1 1 0 1 1 ...
## $ Marital.Status..at.the.time.of.application..xMarried : num 1 1 1 0 0 1 1 0 1 1 ...
## $ Marital.Status..at.the.time.of.application..xSingle : num 0 0 0 1 1 0 0 1 0 0 ...
## $ Education.xBachelor : num 1 0 0 1 1 0 1 0 1 0 ...
## $ Education.xMasters : num 0 1 0 0 0 0 0 0 0 0 ...
## $ Education.xOthers : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Education.xPhd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Education.xProfessional : num 0 0 1 0 0 1 0 1 0 1 ...
## $ Profession.xSAL : num 1 0 1 1 0 0 1 0 1 1 ...
## $ Profession.xSE : num 0 1 0 0 0 1 0 1 0 0 ...
## $ Profession.xSE_PROF : num 0 0 0 0 1 0 0 0 0 0 ...
## $ Type.of.residence.xCompany.provided : num 0 0 0 0 0 0 0 0 0 1 ...
## $ Type.of.residence.xLiving.with.Parents : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xOthers : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Type.of.residence.xOwned : num 0 0 0 0 0 0 0 0 1 0 ...
## $ Type.of.residence.xRented : num 1 1 1 1 1 1 1 1 0 0 ...rejected_final_df$perdict_default <- predict(final_lr_model, type = "response", newdata = rejected_final_df)
rejected_final_df$predict_NonDefault <- 1 - rejected_final_df$perdict_default
rejected_final_df$odds <- log(rejected_final_df$predict_NonDefault/rejected_final_df$perdict_default)
rejected_final_df$Score = ceiling(Offset + (Factor*rejected_final_df$odds))
summary(rejected_final_df$Score)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 375.0 397.0 404.0 404.4 411.0 438.0 35length(which(rejected_final_df$Score<419))/nrow(rejected_final_df)## [1] 0.8877193With our decided cutoff 419 we were able to identify 88.77% actual rejected applicants.
cutoff_score= 419
correct_rejections_by_scorecard="88.77%"
length(which(rejected_final_df$Score<419))/nrow(rejected_final_df)## [1] 0.8877193With cutoff 419 we were able to identify 88.77% actual rejected applicants.
ggplot(rejected_final_df, aes(x = Score)) +geom_bar(fill= "pink")+geom_vline(aes(xintercept = cutoff_score,col="pink"))+labs(x="Score",y="Count",title="Score Distribution of Actual Rejected applications")+annotate("text", x=380,y=1, colour = "blue",hjust=0, vjust=0, size=7,
label=paste("Correct rejections by score card% =", correct_rejections_by_scorecard))
Approach_2 - Using scorecard package s
Convert whole data to woe data and use scorecard package to get scores for each row
library(woeBinning)
library(scorecard)##
## Attaching package: 'scorecard'## The following object is masked from 'package:woeBinning':
##
## germancredit## The following object is masked from 'package:car':
##
## vifwoe binning
bins = woebin(final_df, "Performance.Tag")## [INFO] creating woe binning ...dt_woe = woebin_ply(final_df, bins)## [INFO] converting into woe values ...#modelling on woe dataframe
m = glm(Performance.Tag ~ ., family = binomial(), data = dt_woe)m_2 <- stepAIC(m, direction = "both")## Start: AIC=23363.55
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.60.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.PL.trades.opened.in.last.6.months_woe + No.of.PL.trades.opened.in.last.6.months.1_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Presence.of.open.auto.loan_woe + Gender.xF_woe + Gender.xM_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xOthers_woe +
## Education.xPhd_woe + Education.xProfessional_woe + Profession.xSAL_woe +
## Profession.xSE_woe + Profession.xSE_PROF_woe + Type.of.residence.xCompany.provided_woe +
## Type.of.residence.xLiving.with.Parents_woe + Type.of.residence.xOthers_woe +
## Type.of.residence.xOwned_woe + Type.of.residence.xRented_woe +
## perdict_default_woe + predict_NonDefault_woe + odds_woe +
## Score_woe
##
##
## Step: AIC=23363.55
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.60.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.PL.trades.opened.in.last.6.months_woe + No.of.PL.trades.opened.in.last.6.months.1_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Presence.of.open.auto.loan_woe + Gender.xF_woe + Gender.xM_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xOthers_woe +
## Education.xPhd_woe + Education.xProfessional_woe + Profession.xSAL_woe +
## Profession.xSE_woe + Profession.xSE_PROF_woe + Type.of.residence.xCompany.provided_woe +
## Type.of.residence.xLiving.with.Parents_woe + Type.of.residence.xOthers_woe +
## Type.of.residence.xOwned_woe + Type.of.residence.xRented_woe +
## perdict_default_woe + odds_woe + Score_woe
##
##
## Step: AIC=23363.55
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.60.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.PL.trades.opened.in.last.6.months_woe + No.of.PL.trades.opened.in.last.6.months.1_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Presence.of.open.auto.loan_woe + Gender.xF_woe + Gender.xM_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xOthers_woe +
## Education.xPhd_woe + Education.xProfessional_woe + Profession.xSAL_woe +
## Profession.xSE_woe + Profession.xSE_PROF_woe + Type.of.residence.xCompany.provided_woe +
## Type.of.residence.xLiving.with.Parents_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + perdict_default_woe + odds_woe +
## Score_woe
##
##
## Step: AIC=23363.55
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.60.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.PL.trades.opened.in.last.6.months_woe + No.of.PL.trades.opened.in.last.6.months.1_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Presence.of.open.auto.loan_woe + Gender.xF_woe + Gender.xM_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xOthers_woe +
## Education.xPhd_woe + Education.xProfessional_woe + Profession.xSAL_woe +
## Profession.xSE_woe + Profession.xSE_PROF_woe + Type.of.residence.xCompany.provided_woe +
## Type.of.residence.xOwned_woe + Type.of.residence.xRented_woe +
## perdict_default_woe + odds_woe + Score_woe
##
##
## Step: AIC=23363.55
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.60.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.PL.trades.opened.in.last.6.months_woe + No.of.PL.trades.opened.in.last.6.months.1_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Presence.of.open.auto.loan_woe + Gender.xF_woe + Gender.xM_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xOthers_woe +
## Education.xPhd_woe + Education.xProfessional_woe + Profession.xSAL_woe +
## Profession.xSE_woe + Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + perdict_default_woe + odds_woe +
## Score_woe
##
##
## Step: AIC=23363.55
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.60.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.PL.trades.opened.in.last.6.months_woe + No.of.PL.trades.opened.in.last.6.months.1_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Presence.of.open.auto.loan_woe + Gender.xF_woe + Gender.xM_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + perdict_default_woe + odds_woe +
## Score_woe
##
##
## Step: AIC=23363.55
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.60.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.PL.trades.opened.in.last.6.months_woe + No.of.PL.trades.opened.in.last.6.months.1_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Gender.xF_woe + Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + perdict_default_woe + odds_woe +
## Score_woe
##
##
## Step: AIC=23363.55
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.60.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.PL.trades.opened.in.last.6.months_woe + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Gender.xF_woe + Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + perdict_default_woe + odds_woe +
## Score_woe
##
## Df
## - No.of.PL.trades.opened.in.last.6.months_woe 1
## - No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## - No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## - No.of.trades.opened.in.last.12.months_woe 1
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## - perdict_default_woe 1
## - No.of.trades.opened.in.last.6.months_woe 1
## - Presence.of.open.home.loan_woe 1
## - Type.of.residence.xOwned_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - Gender.xM_woe 1
## - Gender.xF_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - Type.of.residence.xRented_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - odds_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Total.No.of.Trades_woe 1
## <none>
## - Income_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## Deviance
## - No.of.PL.trades.opened.in.last.6.months_woe 23290
## - No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## - No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## - No.of.trades.opened.in.last.12.months_woe 23290
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## - perdict_default_woe 23290
## - No.of.trades.opened.in.last.6.months_woe 23290
## - Presence.of.open.home.loan_woe 23290
## - Type.of.residence.xOwned_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23290
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## - Gender.xM_woe 23290
## - Gender.xF_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23290
## - Type.of.residence.xRented_woe 23290
## - Marital.Status..at.the.time.of.application..xSingle_woe 23290
## - Marital.Status..at.the.time.of.application..xMarried_woe 23290
## - No.of.months.in.current.residence_woe 23290
## - Education.xMasters_woe 23290
## - Education.xBachelor_woe 23290
## - odds_woe 23290
## - Education.xPhd_woe 23290
## - Education.xProfessional_woe 23290
## - Outstanding.Balance_woe 23291
## - Profession.xSAL_woe 23291
## - Profession.xSE_PROF_woe 23291
## - Profession.xSE_woe 23291
## - Score_woe 23291
## - No.of.PL.trades.opened.in.last.12.months_woe 23291
## - Total.No.of.Trades_woe 23292
## <none> 23290
## - Income_woe 23292
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23299
## - No.of.months.in.current.company_woe 23301
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23302
## - Age_woe 23302
## - Avgas.CC.Utilization.in.last.12.months_woe 23308
## AIC
## - No.of.PL.trades.opened.in.last.6.months_woe 23362
## - No.of.times.60.DPD.or.worse.in.last.12.months_woe 23362
## - No.of.times.30.DPD.or.worse.in.last.6.months_woe 23362
## - No.of.trades.opened.in.last.12.months_woe 23362
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23362
## - perdict_default_woe 23362
## - No.of.trades.opened.in.last.6.months_woe 23362
## - Presence.of.open.home.loan_woe 23362
## - Type.of.residence.xOwned_woe 23362
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23362
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23362
## - Gender.xM_woe 23362
## - Gender.xF_woe 23362
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23362
## - Type.of.residence.xRented_woe 23362
## - Marital.Status..at.the.time.of.application..xSingle_woe 23362
## - Marital.Status..at.the.time.of.application..xMarried_woe 23362
## - No.of.months.in.current.residence_woe 23362
## - Education.xMasters_woe 23362
## - Education.xBachelor_woe 23362
## - odds_woe 23362
## - Education.xPhd_woe 23362
## - Education.xProfessional_woe 23362
## - Outstanding.Balance_woe 23363
## - Profession.xSAL_woe 23363
## - Profession.xSE_PROF_woe 23363
## - Profession.xSE_woe 23363
## - Score_woe 23363
## - No.of.PL.trades.opened.in.last.12.months_woe 23363
## - Total.No.of.Trades_woe 23364
## <none> 23364
## - Income_woe 23364
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23371
## - No.of.months.in.current.company_woe 23373
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23374
## - Age_woe 23374
## - Avgas.CC.Utilization.in.last.12.months_woe 23380
##
## Step: AIC=23361.55
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.60.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Gender.xF_woe + Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + perdict_default_woe + odds_woe +
## Score_woe
##
## Df
## - No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## - No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## - No.of.trades.opened.in.last.12.months_woe 1
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## - perdict_default_woe 1
## - No.of.trades.opened.in.last.6.months_woe 1
## - Presence.of.open.home.loan_woe 1
## - Type.of.residence.xOwned_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - Gender.xM_woe 1
## - Gender.xF_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - Type.of.residence.xRented_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - odds_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Total.No.of.Trades_woe 1
## <none>
## - Income_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## Deviance
## - No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## - No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## - No.of.trades.opened.in.last.12.months_woe 23290
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## - perdict_default_woe 23290
## - No.of.trades.opened.in.last.6.months_woe 23290
## - Presence.of.open.home.loan_woe 23290
## - Type.of.residence.xOwned_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23290
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## - Gender.xM_woe 23290
## - Gender.xF_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23290
## - Type.of.residence.xRented_woe 23290
## - Marital.Status..at.the.time.of.application..xSingle_woe 23290
## - Marital.Status..at.the.time.of.application..xMarried_woe 23290
## - No.of.months.in.current.residence_woe 23290
## - Education.xMasters_woe 23290
## - Education.xBachelor_woe 23290
## - odds_woe 23290
## - Education.xPhd_woe 23290
## - Education.xProfessional_woe 23290
## - Outstanding.Balance_woe 23291
## - Profession.xSAL_woe 23291
## - Profession.xSE_PROF_woe 23291
## - Profession.xSE_woe 23291
## - Score_woe 23291
## - No.of.PL.trades.opened.in.last.12.months_woe 23291
## - Total.No.of.Trades_woe 23292
## <none> 23290
## - Income_woe 23292
## + No.of.PL.trades.opened.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23290
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23299
## - No.of.months.in.current.company_woe 23301
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23302
## - Age_woe 23302
## - Avgas.CC.Utilization.in.last.12.months_woe 23309
## AIC
## - No.of.times.60.DPD.or.worse.in.last.12.months_woe 23360
## - No.of.times.30.DPD.or.worse.in.last.6.months_woe 23360
## - No.of.trades.opened.in.last.12.months_woe 23360
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23360
## - perdict_default_woe 23360
## - No.of.trades.opened.in.last.6.months_woe 23360
## - Presence.of.open.home.loan_woe 23360
## - Type.of.residence.xOwned_woe 23360
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23360
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23360
## - Gender.xM_woe 23360
## - Gender.xF_woe 23360
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23360
## - Type.of.residence.xRented_woe 23360
## - Marital.Status..at.the.time.of.application..xSingle_woe 23360
## - Marital.Status..at.the.time.of.application..xMarried_woe 23360
## - No.of.months.in.current.residence_woe 23360
## - Education.xMasters_woe 23360
## - Education.xBachelor_woe 23360
## - odds_woe 23360
## - Education.xPhd_woe 23360
## - Education.xProfessional_woe 23360
## - Outstanding.Balance_woe 23361
## - Profession.xSAL_woe 23361
## - Profession.xSE_PROF_woe 23361
## - Profession.xSE_woe 23361
## - Score_woe 23361
## - No.of.PL.trades.opened.in.last.12.months_woe 23361
## - Total.No.of.Trades_woe 23362
## <none> 23362
## - Income_woe 23362
## + No.of.PL.trades.opened.in.last.6.months_woe 23364
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23364
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23369
## - No.of.months.in.current.company_woe 23371
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23372
## - Age_woe 23372
## - Avgas.CC.Utilization.in.last.12.months_woe 23379
##
## Step: AIC=23359.55
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.trades.opened.in.last.6.months_woe +
## No.of.trades.opened.in.last.12.months_woe + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Gender.xF_woe + Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + perdict_default_woe + odds_woe +
## Score_woe
##
## Df
## - No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## - No.of.trades.opened.in.last.12.months_woe 1
## - perdict_default_woe 1
## - No.of.trades.opened.in.last.6.months_woe 1
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## - Presence.of.open.home.loan_woe 1
## - Type.of.residence.xOwned_woe 1
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xM_woe 1
## - Gender.xF_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - Type.of.residence.xRented_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - Education.xMasters_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xBachelor_woe 1
## - odds_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## <none>
## - Total.No.of.Trades_woe 1
## - Income_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## Deviance
## - No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## - No.of.trades.opened.in.last.12.months_woe 23290
## - perdict_default_woe 23290
## - No.of.trades.opened.in.last.6.months_woe 23290
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## - Presence.of.open.home.loan_woe 23290
## - Type.of.residence.xOwned_woe 23290
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23290
## - Gender.xM_woe 23290
## - Gender.xF_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23290
## - Type.of.residence.xRented_woe 23290
## - Marital.Status..at.the.time.of.application..xSingle_woe 23290
## - Marital.Status..at.the.time.of.application..xMarried_woe 23290
## - Education.xMasters_woe 23290
## - No.of.months.in.current.residence_woe 23290
## - Education.xBachelor_woe 23290
## - odds_woe 23290
## - Education.xPhd_woe 23290
## - Education.xProfessional_woe 23290
## - Outstanding.Balance_woe 23291
## - Profession.xSAL_woe 23291
## - Profession.xSE_PROF_woe 23291
## - Profession.xSE_woe 23291
## - Score_woe 23291
## - No.of.PL.trades.opened.in.last.12.months_woe 23291
## <none> 23290
## - Total.No.of.Trades_woe 23292
## - Income_woe 23292
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23290
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23299
## - No.of.months.in.current.company_woe 23301
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23302
## - Age_woe 23302
## - Avgas.CC.Utilization.in.last.12.months_woe 23309
## AIC
## - No.of.times.30.DPD.or.worse.in.last.6.months_woe 23358
## - No.of.trades.opened.in.last.12.months_woe 23358
## - perdict_default_woe 23358
## - No.of.trades.opened.in.last.6.months_woe 23358
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23358
## - Presence.of.open.home.loan_woe 23358
## - Type.of.residence.xOwned_woe 23358
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23358
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23358
## - Gender.xM_woe 23358
## - Gender.xF_woe 23358
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23358
## - Type.of.residence.xRented_woe 23358
## - Marital.Status..at.the.time.of.application..xSingle_woe 23358
## - Marital.Status..at.the.time.of.application..xMarried_woe 23358
## - Education.xMasters_woe 23358
## - No.of.months.in.current.residence_woe 23358
## - Education.xBachelor_woe 23358
## - odds_woe 23358
## - Education.xPhd_woe 23358
## - Education.xProfessional_woe 23358
## - Outstanding.Balance_woe 23359
## - Profession.xSAL_woe 23359
## - Profession.xSE_PROF_woe 23359
## - Profession.xSE_woe 23359
## - Score_woe 23359
## - No.of.PL.trades.opened.in.last.12.months_woe 23359
## <none> 23360
## - Total.No.of.Trades_woe 23360
## - Income_woe 23360
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23362
## + No.of.PL.trades.opened.in.last.6.months_woe 23362
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23362
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23367
## - No.of.months.in.current.company_woe 23369
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23370
## - Age_woe 23370
## - Avgas.CC.Utilization.in.last.12.months_woe 23377
##
## Step: AIC=23357.58
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Gender.xF_woe + Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + perdict_default_woe + odds_woe +
## Score_woe
##
## Df
## - No.of.trades.opened.in.last.12.months_woe 1
## - perdict_default_woe 1
## - No.of.trades.opened.in.last.6.months_woe 1
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## - Presence.of.open.home.loan_woe 1
## - Type.of.residence.xOwned_woe 1
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - Gender.xM_woe 1
## - Gender.xF_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - Type.of.residence.xRented_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - odds_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## <none>
## - Total.No.of.Trades_woe 1
## - Income_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## - No.of.months.in.current.company_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Age_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## Deviance
## - No.of.trades.opened.in.last.12.months_woe 23290
## - perdict_default_woe 23290
## - No.of.trades.opened.in.last.6.months_woe 23290
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## - Presence.of.open.home.loan_woe 23290
## - Type.of.residence.xOwned_woe 23290
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## - Gender.xM_woe 23290
## - Gender.xF_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23290
## - Type.of.residence.xRented_woe 23290
## - Marital.Status..at.the.time.of.application..xSingle_woe 23290
## - Marital.Status..at.the.time.of.application..xMarried_woe 23290
## - No.of.months.in.current.residence_woe 23290
## - Education.xMasters_woe 23290
## - Education.xBachelor_woe 23290
## - odds_woe 23290
## - Education.xPhd_woe 23290
## - Education.xProfessional_woe 23290
## - Outstanding.Balance_woe 23291
## - Profession.xSAL_woe 23291
## - Profession.xSE_PROF_woe 23291
## - Profession.xSE_woe 23291
## - Score_woe 23291
## - No.of.PL.trades.opened.in.last.12.months_woe 23292
## <none> 23290
## - Total.No.of.Trades_woe 23292
## - Income_woe 23292
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23290
## - No.of.months.in.current.company_woe 23301
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23302
## - Age_woe 23302
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23306
## - Avgas.CC.Utilization.in.last.12.months_woe 23309
## AIC
## - No.of.trades.opened.in.last.12.months_woe 23356
## - perdict_default_woe 23356
## - No.of.trades.opened.in.last.6.months_woe 23356
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23356
## - Presence.of.open.home.loan_woe 23356
## - Type.of.residence.xOwned_woe 23356
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23356
## - Gender.xM_woe 23356
## - Gender.xF_woe 23356
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23356
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23356
## - Type.of.residence.xRented_woe 23356
## - Marital.Status..at.the.time.of.application..xSingle_woe 23356
## - Marital.Status..at.the.time.of.application..xMarried_woe 23356
## - No.of.months.in.current.residence_woe 23356
## - Education.xMasters_woe 23356
## - Education.xBachelor_woe 23356
## - odds_woe 23356
## - Education.xPhd_woe 23356
## - Education.xProfessional_woe 23356
## - Outstanding.Balance_woe 23357
## - Profession.xSAL_woe 23357
## - Profession.xSE_PROF_woe 23357
## - Profession.xSE_woe 23357
## - Score_woe 23357
## - No.of.PL.trades.opened.in.last.12.months_woe 23358
## <none> 23358
## - Total.No.of.Trades_woe 23358
## - Income_woe 23358
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23360
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23360
## + No.of.PL.trades.opened.in.last.6.months_woe 23360
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23360
## - No.of.months.in.current.company_woe 23367
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23368
## - Age_woe 23368
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23372
## - Avgas.CC.Utilization.in.last.12.months_woe 23375
##
## Step: AIC=23355.6
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.trades.opened.in.last.6.months_woe + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Gender.xF_woe + Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + perdict_default_woe + odds_woe +
## Score_woe
##
## Df
## - No.of.trades.opened.in.last.6.months_woe 1
## - perdict_default_woe 1
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## - Presence.of.open.home.loan_woe 1
## - Type.of.residence.xOwned_woe 1
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - Gender.xM_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xF_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - Type.of.residence.xRented_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - odds_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## <none>
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## Deviance
## - No.of.trades.opened.in.last.6.months_woe 23290
## - perdict_default_woe 23290
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## - Presence.of.open.home.loan_woe 23290
## - Type.of.residence.xOwned_woe 23290
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## - Gender.xM_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23290
## - Gender.xF_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23290
## - Type.of.residence.xRented_woe 23290
## - Marital.Status..at.the.time.of.application..xSingle_woe 23290
## - Marital.Status..at.the.time.of.application..xMarried_woe 23290
## - No.of.months.in.current.residence_woe 23290
## - Education.xMasters_woe 23290
## - Education.xBachelor_woe 23290
## - odds_woe 23290
## - Education.xPhd_woe 23290
## - Education.xProfessional_woe 23290
## - Outstanding.Balance_woe 23291
## - Profession.xSAL_woe 23291
## - Profession.xSE_PROF_woe 23291
## - Profession.xSE_woe 23291
## - Score_woe 23291
## - No.of.PL.trades.opened.in.last.12.months_woe 23292
## <none> 23290
## - Income_woe 23292
## - Total.No.of.Trades_woe 23292
## + No.of.trades.opened.in.last.12.months_woe 23290
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23290
## - No.of.months.in.current.company_woe 23301
## - Age_woe 23302
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23304
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23306
## - Avgas.CC.Utilization.in.last.12.months_woe 23309
## AIC
## - No.of.trades.opened.in.last.6.months_woe 23354
## - perdict_default_woe 23354
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23354
## - Presence.of.open.home.loan_woe 23354
## - Type.of.residence.xOwned_woe 23354
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23354
## - Gender.xM_woe 23354
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23354
## - Gender.xF_woe 23354
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23354
## - Type.of.residence.xRented_woe 23354
## - Marital.Status..at.the.time.of.application..xSingle_woe 23354
## - Marital.Status..at.the.time.of.application..xMarried_woe 23354
## - No.of.months.in.current.residence_woe 23354
## - Education.xMasters_woe 23354
## - Education.xBachelor_woe 23354
## - odds_woe 23354
## - Education.xPhd_woe 23354
## - Education.xProfessional_woe 23354
## - Outstanding.Balance_woe 23355
## - Profession.xSAL_woe 23355
## - Profession.xSE_PROF_woe 23355
## - Profession.xSE_woe 23355
## - Score_woe 23355
## - No.of.PL.trades.opened.in.last.12.months_woe 23356
## <none> 23356
## - Income_woe 23356
## - Total.No.of.Trades_woe 23356
## + No.of.trades.opened.in.last.12.months_woe 23358
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23358
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23358
## + No.of.PL.trades.opened.in.last.6.months_woe 23358
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23358
## - No.of.months.in.current.company_woe 23365
## - Age_woe 23366
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23368
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23370
## - Avgas.CC.Utilization.in.last.12.months_woe 23373
##
## Step: AIC=23353.66
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Gender.xF_woe + Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + perdict_default_woe + odds_woe +
## Score_woe
##
## Df
## - perdict_default_woe 1
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## - Presence.of.open.home.loan_woe 1
## - Type.of.residence.xOwned_woe 1
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - Gender.xM_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xF_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - Type.of.residence.xRented_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - odds_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## <none>
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## Deviance
## - perdict_default_woe 23290
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## - Presence.of.open.home.loan_woe 23290
## - Type.of.residence.xOwned_woe 23290
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## - Gender.xM_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23290
## - Gender.xF_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23290
## - Type.of.residence.xRented_woe 23290
## - Marital.Status..at.the.time.of.application..xSingle_woe 23290
## - Marital.Status..at.the.time.of.application..xMarried_woe 23290
## - No.of.months.in.current.residence_woe 23290
## - Education.xMasters_woe 23290
## - Education.xBachelor_woe 23290
## - odds_woe 23290
## - Education.xPhd_woe 23290
## - Education.xProfessional_woe 23290
## - Outstanding.Balance_woe 23291
## - Profession.xSAL_woe 23291
## - Profession.xSE_PROF_woe 23291
## - Profession.xSE_woe 23291
## - Score_woe 23291
## - No.of.PL.trades.opened.in.last.12.months_woe 23292
## <none> 23290
## - Income_woe 23292
## - Total.No.of.Trades_woe 23293
## + No.of.trades.opened.in.last.6.months_woe 23290
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23290
## + No.of.trades.opened.in.last.12.months_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## - No.of.months.in.current.company_woe 23301
## - Age_woe 23302
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23304
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23306
## - Avgas.CC.Utilization.in.last.12.months_woe 23309
## AIC
## - perdict_default_woe 23352
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23352
## - Presence.of.open.home.loan_woe 23352
## - Type.of.residence.xOwned_woe 23352
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23352
## - Gender.xM_woe 23352
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23352
## - Gender.xF_woe 23352
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23352
## - Type.of.residence.xRented_woe 23352
## - Marital.Status..at.the.time.of.application..xSingle_woe 23352
## - Marital.Status..at.the.time.of.application..xMarried_woe 23352
## - No.of.months.in.current.residence_woe 23352
## - Education.xMasters_woe 23352
## - Education.xBachelor_woe 23352
## - odds_woe 23352
## - Education.xPhd_woe 23352
## - Education.xProfessional_woe 23352
## - Outstanding.Balance_woe 23353
## - Profession.xSAL_woe 23353
## - Profession.xSE_PROF_woe 23353
## - Profession.xSE_woe 23353
## - Score_woe 23353
## - No.of.PL.trades.opened.in.last.12.months_woe 23354
## <none> 23354
## - Income_woe 23354
## - Total.No.of.Trades_woe 23355
## + No.of.trades.opened.in.last.6.months_woe 23356
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23356
## + No.of.PL.trades.opened.in.last.6.months_woe 23356
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23356
## + No.of.trades.opened.in.last.12.months_woe 23356
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23356
## - No.of.months.in.current.company_woe 23363
## - Age_woe 23364
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23366
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23368
## - Avgas.CC.Utilization.in.last.12.months_woe 23371
##
## Step: AIC=23351.73
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.60.DPD.or.worse.in.last.6.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Gender.xF_woe + Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + odds_woe + Score_woe
##
## Df
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## - Presence.of.open.home.loan_woe 1
## - Type.of.residence.xOwned_woe 1
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - Gender.xM_woe 1
## - Gender.xF_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - Type.of.residence.xRented_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - odds_woe 1
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## <none>
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## Deviance
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## - Presence.of.open.home.loan_woe 23290
## - Type.of.residence.xOwned_woe 23290
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## - Gender.xM_woe 23290
## - Gender.xF_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23290
## - Type.of.residence.xRented_woe 23290
## - Marital.Status..at.the.time.of.application..xSingle_woe 23290
## - Marital.Status..at.the.time.of.application..xMarried_woe 23290
## - No.of.months.in.current.residence_woe 23290
## - Education.xMasters_woe 23290
## - Education.xBachelor_woe 23290
## - Education.xPhd_woe 23290
## - Education.xProfessional_woe 23291
## - Outstanding.Balance_woe 23291
## - Profession.xSAL_woe 23291
## - Profession.xSE_PROF_woe 23291
## - Profession.xSE_woe 23291
## - Score_woe 23291
## - odds_woe 23292
## - No.of.PL.trades.opened.in.last.12.months_woe 23292
## <none> 23290
## - Income_woe 23292
## - Total.No.of.Trades_woe 23293
## + perdict_default_woe 23290
## + predict_NonDefault_woe 23290
## + No.of.trades.opened.in.last.6.months_woe 23290
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.trades.opened.in.last.12.months_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23290
## - No.of.months.in.current.company_woe 23301
## - Age_woe 23302
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23304
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23306
## - Avgas.CC.Utilization.in.last.12.months_woe 23310
## AIC
## - No.of.times.60.DPD.or.worse.in.last.6.months_woe 23350
## - Presence.of.open.home.loan_woe 23350
## - Type.of.residence.xOwned_woe 23350
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23350
## - Gender.xM_woe 23350
## - Gender.xF_woe 23350
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23350
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23350
## - Type.of.residence.xRented_woe 23350
## - Marital.Status..at.the.time.of.application..xSingle_woe 23350
## - Marital.Status..at.the.time.of.application..xMarried_woe 23350
## - No.of.months.in.current.residence_woe 23350
## - Education.xMasters_woe 23350
## - Education.xBachelor_woe 23350
## - Education.xPhd_woe 23350
## - Education.xProfessional_woe 23351
## - Outstanding.Balance_woe 23351
## - Profession.xSAL_woe 23351
## - Profession.xSE_PROF_woe 23351
## - Profession.xSE_woe 23351
## - Score_woe 23351
## - odds_woe 23352
## - No.of.PL.trades.opened.in.last.12.months_woe 23352
## <none> 23352
## - Income_woe 23352
## - Total.No.of.Trades_woe 23353
## + perdict_default_woe 23354
## + predict_NonDefault_woe 23354
## + No.of.trades.opened.in.last.6.months_woe 23354
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23354
## + No.of.trades.opened.in.last.12.months_woe 23354
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23354
## + No.of.PL.trades.opened.in.last.6.months_woe 23354
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23354
## - No.of.months.in.current.company_woe 23361
## - Age_woe 23362
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23364
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23366
## - Avgas.CC.Utilization.in.last.12.months_woe 23370
##
## Step: AIC=23349.85
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Presence.of.open.home.loan_woe +
## Gender.xF_woe + Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + odds_woe + Score_woe
##
## Df
## - Presence.of.open.home.loan_woe 1
## - Type.of.residence.xOwned_woe 1
## - Gender.xM_woe 1
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - Gender.xF_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Type.of.residence.xRented_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Presence.of.open.home.loan_woe 23290
## - Type.of.residence.xOwned_woe 23290
## - Gender.xM_woe 23290
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## - Gender.xF_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23290
## - Type.of.residence.xRented_woe 23290
## - Marital.Status..at.the.time.of.application..xSingle_woe 23290
## - Marital.Status..at.the.time.of.application..xMarried_woe 23290
## - Education.xMasters_woe 23290
## - Education.xBachelor_woe 23290
## - No.of.months.in.current.residence_woe 23291
## - Education.xPhd_woe 23291
## - Education.xProfessional_woe 23291
## - Outstanding.Balance_woe 23291
## - Profession.xSAL_woe 23291
## - Profession.xSE_PROF_woe 23291
## - Profession.xSE_woe 23291
## - Score_woe 23292
## - odds_woe 23292
## <none> 23290
## - No.of.PL.trades.opened.in.last.12.months_woe 23292
## - Income_woe 23292
## - Total.No.of.Trades_woe 23293
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## + perdict_default_woe 23290
## + predict_NonDefault_woe 23290
## + No.of.trades.opened.in.last.6.months_woe 23290
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23290
## + No.of.trades.opened.in.last.12.months_woe 23290
## - No.of.months.in.current.company_woe 23301
## - Age_woe 23303
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23304
## - Avgas.CC.Utilization.in.last.12.months_woe 23310
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23316
## AIC
## - Presence.of.open.home.loan_woe 23348
## - Type.of.residence.xOwned_woe 23348
## - Gender.xM_woe 23348
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23348
## - Gender.xF_woe 23348
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23348
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23348
## - Type.of.residence.xRented_woe 23348
## - Marital.Status..at.the.time.of.application..xSingle_woe 23348
## - Marital.Status..at.the.time.of.application..xMarried_woe 23348
## - Education.xMasters_woe 23348
## - Education.xBachelor_woe 23348
## - No.of.months.in.current.residence_woe 23349
## - Education.xPhd_woe 23349
## - Education.xProfessional_woe 23349
## - Outstanding.Balance_woe 23349
## - Profession.xSAL_woe 23349
## - Profession.xSE_PROF_woe 23349
## - Profession.xSE_woe 23349
## - Score_woe 23350
## - odds_woe 23350
## <none> 23350
## - No.of.PL.trades.opened.in.last.12.months_woe 23350
## - Income_woe 23350
## - Total.No.of.Trades_woe 23351
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23352
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23352
## + perdict_default_woe 23352
## + predict_NonDefault_woe 23352
## + No.of.trades.opened.in.last.6.months_woe 23352
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23352
## + No.of.PL.trades.opened.in.last.6.months_woe 23352
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23352
## + No.of.trades.opened.in.last.12.months_woe 23352
## - No.of.months.in.current.company_woe 23359
## - Age_woe 23361
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23362
## - Avgas.CC.Utilization.in.last.12.months_woe 23368
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23374
##
## Step: AIC=23348.01
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xOwned_woe +
## Type.of.residence.xRented_woe + odds_woe + Score_woe
##
## Df
## - Type.of.residence.xOwned_woe 1
## - Gender.xM_woe 1
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - Gender.xF_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Type.of.residence.xRented_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Type.of.residence.xOwned_woe 23290
## - Gender.xM_woe 23290
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## - Gender.xF_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23290
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23290
## - Type.of.residence.xRented_woe 23290
## - Marital.Status..at.the.time.of.application..xSingle_woe 23291
## - Marital.Status..at.the.time.of.application..xMarried_woe 23291
## - Education.xMasters_woe 23291
## - Education.xBachelor_woe 23291
## - No.of.months.in.current.residence_woe 23291
## - Education.xPhd_woe 23291
## - Education.xProfessional_woe 23291
## - Outstanding.Balance_woe 23291
## - Profession.xSAL_woe 23292
## - Profession.xSE_PROF_woe 23292
## - Profession.xSE_woe 23292
## - Score_woe 23292
## - odds_woe 23292
## <none> 23290
## - No.of.PL.trades.opened.in.last.12.months_woe 23292
## - Income_woe 23293
## - Total.No.of.Trades_woe 23293
## + Presence.of.open.home.loan_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## + perdict_default_woe 23290
## + predict_NonDefault_woe 23290
## + No.of.trades.opened.in.last.6.months_woe 23290
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23290
## + No.of.trades.opened.in.last.12.months_woe 23290
## - No.of.months.in.current.company_woe 23302
## - Age_woe 23303
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23304
## - Avgas.CC.Utilization.in.last.12.months_woe 23310
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23316
## AIC
## - Type.of.residence.xOwned_woe 23346
## - Gender.xM_woe 23346
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23346
## - Gender.xF_woe 23346
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23346
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23346
## - Type.of.residence.xRented_woe 23346
## - Marital.Status..at.the.time.of.application..xSingle_woe 23347
## - Marital.Status..at.the.time.of.application..xMarried_woe 23347
## - Education.xMasters_woe 23347
## - Education.xBachelor_woe 23347
## - No.of.months.in.current.residence_woe 23347
## - Education.xPhd_woe 23347
## - Education.xProfessional_woe 23347
## - Outstanding.Balance_woe 23347
## - Profession.xSAL_woe 23348
## - Profession.xSE_PROF_woe 23348
## - Profession.xSE_woe 23348
## - Score_woe 23348
## - odds_woe 23348
## <none> 23348
## - No.of.PL.trades.opened.in.last.12.months_woe 23348
## - Income_woe 23349
## - Total.No.of.Trades_woe 23349
## + Presence.of.open.home.loan_woe 23350
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23350
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23350
## + perdict_default_woe 23350
## + predict_NonDefault_woe 23350
## + No.of.trades.opened.in.last.6.months_woe 23350
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23350
## + No.of.PL.trades.opened.in.last.6.months_woe 23350
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23350
## + No.of.trades.opened.in.last.12.months_woe 23350
## - No.of.months.in.current.company_woe 23358
## - Age_woe 23359
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23360
## - Avgas.CC.Utilization.in.last.12.months_woe 23366
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23372
##
## Step: AIC=23346.2
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Gender.xM_woe + Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xRented_woe +
## odds_woe + Score_woe
##
## Df
## - Gender.xM_woe 1
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - Gender.xF_woe 1
## - Type.of.residence.xRented_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + Type.of.residence.xOwned_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Gender.xM_woe 23290
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## - Gender.xF_woe 23291
## - Type.of.residence.xRented_woe 23291
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23291
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23291
## - Marital.Status..at.the.time.of.application..xSingle_woe 23291
## - Marital.Status..at.the.time.of.application..xMarried_woe 23291
## - Education.xMasters_woe 23291
## - Education.xBachelor_woe 23291
## - No.of.months.in.current.residence_woe 23291
## - Education.xPhd_woe 23291
## - Education.xProfessional_woe 23291
## - Outstanding.Balance_woe 23292
## - Profession.xSAL_woe 23292
## - Profession.xSE_PROF_woe 23292
## - Profession.xSE_woe 23292
## - Score_woe 23292
## - odds_woe 23292
## <none> 23290
## - No.of.PL.trades.opened.in.last.12.months_woe 23292
## - Income_woe 23293
## - Total.No.of.Trades_woe 23293
## + Type.of.residence.xOwned_woe 23290
## + Presence.of.open.home.loan_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## + perdict_default_woe 23290
## + predict_NonDefault_woe 23290
## + No.of.trades.opened.in.last.6.months_woe 23290
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23290
## + No.of.trades.opened.in.last.12.months_woe 23290
## - No.of.months.in.current.company_woe 23302
## - Age_woe 23303
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23305
## - Avgas.CC.Utilization.in.last.12.months_woe 23310
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23316
## AIC
## - Gender.xM_woe 23344
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23344
## - Gender.xF_woe 23345
## - Type.of.residence.xRented_woe 23345
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23345
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23345
## - Marital.Status..at.the.time.of.application..xSingle_woe 23345
## - Marital.Status..at.the.time.of.application..xMarried_woe 23345
## - Education.xMasters_woe 23345
## - Education.xBachelor_woe 23345
## - No.of.months.in.current.residence_woe 23345
## - Education.xPhd_woe 23345
## - Education.xProfessional_woe 23345
## - Outstanding.Balance_woe 23346
## - Profession.xSAL_woe 23346
## - Profession.xSE_PROF_woe 23346
## - Profession.xSE_woe 23346
## - Score_woe 23346
## - odds_woe 23346
## <none> 23346
## - No.of.PL.trades.opened.in.last.12.months_woe 23346
## - Income_woe 23347
## - Total.No.of.Trades_woe 23347
## + Type.of.residence.xOwned_woe 23348
## + Presence.of.open.home.loan_woe 23348
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23348
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23348
## + perdict_default_woe 23348
## + predict_NonDefault_woe 23348
## + No.of.trades.opened.in.last.6.months_woe 23348
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23348
## + No.of.PL.trades.opened.in.last.6.months_woe 23348
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23348
## + No.of.trades.opened.in.last.12.months_woe 23348
## - No.of.months.in.current.company_woe 23356
## - Age_woe 23357
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23359
## - Avgas.CC.Utilization.in.last.12.months_woe 23364
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23370
##
## Step: AIC=23344.45
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xRented_woe +
## odds_woe + Score_woe
##
## Df
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## - Type.of.residence.xRented_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + Gender.xM_woe 1
## + Type.of.residence.xOwned_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23291
## - Type.of.residence.xRented_woe 23291
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23291
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23291
## - Marital.Status..at.the.time.of.application..xSingle_woe 23291
## - Marital.Status..at.the.time.of.application..xMarried_woe 23291
## - Education.xMasters_woe 23291
## - Education.xBachelor_woe 23291
## - No.of.months.in.current.residence_woe 23291
## - Education.xPhd_woe 23291
## - Education.xProfessional_woe 23291
## - Gender.xF_woe 23292
## - Outstanding.Balance_woe 23292
## - Profession.xSAL_woe 23292
## - Profession.xSE_PROF_woe 23292
## - Profession.xSE_woe 23292
## - Score_woe 23292
## - odds_woe 23292
## <none> 23290
## - No.of.PL.trades.opened.in.last.12.months_woe 23293
## - Income_woe 23293
## - Total.No.of.Trades_woe 23294
## + Gender.xM_woe 23290
## + Type.of.residence.xOwned_woe 23290
## + Presence.of.open.home.loan_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23290
## + perdict_default_woe 23290
## + predict_NonDefault_woe 23290
## + No.of.trades.opened.in.last.6.months_woe 23290
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months_woe 23290
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23290
## + No.of.trades.opened.in.last.12.months_woe 23290
## - No.of.months.in.current.company_woe 23302
## - Age_woe 23303
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23305
## - Avgas.CC.Utilization.in.last.12.months_woe 23311
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23316
## AIC
## - No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23343
## - Type.of.residence.xRented_woe 23343
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23343
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23343
## - Marital.Status..at.the.time.of.application..xSingle_woe 23343
## - Marital.Status..at.the.time.of.application..xMarried_woe 23343
## - Education.xMasters_woe 23343
## - Education.xBachelor_woe 23343
## - No.of.months.in.current.residence_woe 23343
## - Education.xPhd_woe 23343
## - Education.xProfessional_woe 23343
## - Gender.xF_woe 23344
## - Outstanding.Balance_woe 23344
## - Profession.xSAL_woe 23344
## - Profession.xSE_PROF_woe 23344
## - Profession.xSE_woe 23344
## - Score_woe 23344
## - odds_woe 23344
## <none> 23344
## - No.of.PL.trades.opened.in.last.12.months_woe 23345
## - Income_woe 23345
## - Total.No.of.Trades_woe 23346
## + Gender.xM_woe 23346
## + Type.of.residence.xOwned_woe 23346
## + Presence.of.open.home.loan_woe 23346
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23346
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23346
## + perdict_default_woe 23346
## + predict_NonDefault_woe 23346
## + No.of.trades.opened.in.last.6.months_woe 23346
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23346
## + No.of.PL.trades.opened.in.last.6.months_woe 23346
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23346
## + No.of.trades.opened.in.last.12.months_woe 23346
## - No.of.months.in.current.company_woe 23354
## - Age_woe 23355
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23357
## - Avgas.CC.Utilization.in.last.12.months_woe 23363
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23368
##
## Step: AIC=23342.7
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + Type.of.residence.xRented_woe +
## odds_woe + Score_woe
##
## Df
## - Type.of.residence.xRented_woe 1
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Gender.xM_woe 1
## + Type.of.residence.xOwned_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## Deviance
## - Type.of.residence.xRented_woe 23291
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23291
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23291
## - Marital.Status..at.the.time.of.application..xSingle_woe 23291
## - Marital.Status..at.the.time.of.application..xMarried_woe 23291
## - Education.xMasters_woe 23291
## - Education.xBachelor_woe 23291
## - No.of.months.in.current.residence_woe 23291
## - Education.xPhd_woe 23291
## - Education.xProfessional_woe 23292
## - Gender.xF_woe 23292
## - Outstanding.Balance_woe 23292
## - Profession.xSAL_woe 23292
## - Profession.xSE_PROF_woe 23292
## - Profession.xSE_woe 23292
## - Score_woe 23292
## - odds_woe 23293
## <none> 23291
## - No.of.PL.trades.opened.in.last.12.months_woe 23293
## - Income_woe 23293
## - Total.No.of.Trades_woe 23294
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23290
## + Gender.xM_woe 23290
## + Type.of.residence.xOwned_woe 23291
## + Presence.of.open.home.loan_woe 23291
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23291
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23291
## + perdict_default_woe 23291
## + predict_NonDefault_woe 23291
## + No.of.trades.opened.in.last.6.months_woe 23291
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23291
## + No.of.PL.trades.opened.in.last.6.months_woe 23291
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23291
## + No.of.trades.opened.in.last.12.months_woe 23291
## - No.of.months.in.current.company_woe 23302
## - Age_woe 23303
## - Avgas.CC.Utilization.in.last.12.months_woe 23311
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23317
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23317
## AIC
## - Type.of.residence.xRented_woe 23341
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23341
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23341
## - Marital.Status..at.the.time.of.application..xSingle_woe 23341
## - Marital.Status..at.the.time.of.application..xMarried_woe 23341
## - Education.xMasters_woe 23341
## - Education.xBachelor_woe 23341
## - No.of.months.in.current.residence_woe 23341
## - Education.xPhd_woe 23341
## - Education.xProfessional_woe 23342
## - Gender.xF_woe 23342
## - Outstanding.Balance_woe 23342
## - Profession.xSAL_woe 23342
## - Profession.xSE_PROF_woe 23342
## - Profession.xSE_woe 23342
## - Score_woe 23342
## - odds_woe 23343
## <none> 23343
## - No.of.PL.trades.opened.in.last.12.months_woe 23343
## - Income_woe 23343
## - Total.No.of.Trades_woe 23344
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23344
## + Gender.xM_woe 23344
## + Type.of.residence.xOwned_woe 23345
## + Presence.of.open.home.loan_woe 23345
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23345
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23345
## + perdict_default_woe 23345
## + predict_NonDefault_woe 23345
## + No.of.trades.opened.in.last.6.months_woe 23345
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23345
## + No.of.PL.trades.opened.in.last.6.months_woe 23345
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23345
## + No.of.trades.opened.in.last.12.months_woe 23345
## - No.of.months.in.current.company_woe 23352
## - Age_woe 23353
## - Avgas.CC.Utilization.in.last.12.months_woe 23361
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23367
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23367
##
## Step: AIC=23341.02
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + odds_woe + Score_woe
##
## Df
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + Type.of.residence.xRented_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Gender.xM_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + Type.of.residence.xOwned_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## Deviance
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23291
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23291
## - Marital.Status..at.the.time.of.application..xSingle_woe 23292
## - Marital.Status..at.the.time.of.application..xMarried_woe 23292
## - Education.xMasters_woe 23292
## - Education.xBachelor_woe 23292
## - No.of.months.in.current.residence_woe 23292
## - Education.xPhd_woe 23292
## - Education.xProfessional_woe 23292
## - Gender.xF_woe 23292
## - Outstanding.Balance_woe 23292
## - Profession.xSAL_woe 23292
## - Profession.xSE_PROF_woe 23293
## - Profession.xSE_woe 23293
## - Score_woe 23293
## - odds_woe 23293
## <none> 23291
## - No.of.PL.trades.opened.in.last.12.months_woe 23293
## - Income_woe 23294
## - Total.No.of.Trades_woe 23294
## + Type.of.residence.xRented_woe 23291
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23291
## + Gender.xM_woe 23291
## + Presence.of.open.home.loan_woe 23291
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23291
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23291
## + Type.of.residence.xOwned_woe 23291
## + perdict_default_woe 23291
## + predict_NonDefault_woe 23291
## + No.of.trades.opened.in.last.6.months_woe 23291
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23291
## + No.of.PL.trades.opened.in.last.6.months_woe 23291
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23291
## + No.of.trades.opened.in.last.12.months_woe 23291
## - No.of.months.in.current.company_woe 23303
## - Age_woe 23304
## - Avgas.CC.Utilization.in.last.12.months_woe 23312
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23317
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23318
## AIC
## - No.of.times.90.DPD.or.worse.in.last.6.months_woe 23339
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23339
## - Marital.Status..at.the.time.of.application..xSingle_woe 23340
## - Marital.Status..at.the.time.of.application..xMarried_woe 23340
## - Education.xMasters_woe 23340
## - Education.xBachelor_woe 23340
## - No.of.months.in.current.residence_woe 23340
## - Education.xPhd_woe 23340
## - Education.xProfessional_woe 23340
## - Gender.xF_woe 23340
## - Outstanding.Balance_woe 23340
## - Profession.xSAL_woe 23340
## - Profession.xSE_PROF_woe 23341
## - Profession.xSE_woe 23341
## - Score_woe 23341
## - odds_woe 23341
## <none> 23341
## - No.of.PL.trades.opened.in.last.12.months_woe 23341
## - Income_woe 23342
## - Total.No.of.Trades_woe 23342
## + Type.of.residence.xRented_woe 23343
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23343
## + Gender.xM_woe 23343
## + Presence.of.open.home.loan_woe 23343
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23343
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23343
## + Type.of.residence.xOwned_woe 23343
## + perdict_default_woe 23343
## + predict_NonDefault_woe 23343
## + No.of.trades.opened.in.last.6.months_woe 23343
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23343
## + No.of.PL.trades.opened.in.last.6.months_woe 23343
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23343
## + No.of.trades.opened.in.last.12.months_woe 23343
## - No.of.months.in.current.company_woe 23351
## - Age_woe 23352
## - Avgas.CC.Utilization.in.last.12.months_woe 23360
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23365
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23366
##
## Step: AIC=23339.41
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Marital.Status..at.the.time.of.application..xSingle_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + odds_woe + Score_woe
##
## Df
## - Marital.Status..at.the.time.of.application..xSingle_woe 1
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + Type.of.residence.xRented_woe 1
## + Gender.xM_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Marital.Status..at.the.time.of.application..xSingle_woe 23292
## - Marital.Status..at.the.time.of.application..xMarried_woe 23292
## - Education.xMasters_woe 23292
## - Education.xBachelor_woe 23292
## - No.of.months.in.current.residence_woe 23292
## - Education.xPhd_woe 23292
## - Education.xProfessional_woe 23292
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23293
## - Gender.xF_woe 23293
## - Outstanding.Balance_woe 23293
## - Profession.xSAL_woe 23293
## - Profession.xSE_PROF_woe 23293
## - Profession.xSE_woe 23293
## - Score_woe 23293
## - odds_woe 23293
## <none> 23291
## - No.of.PL.trades.opened.in.last.12.months_woe 23294
## - Income_woe 23294
## - Total.No.of.Trades_woe 23295
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23291
## + Type.of.residence.xRented_woe 23291
## + Gender.xM_woe 23291
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23291
## + Presence.of.open.home.loan_woe 23291
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23291
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23291
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23291
## + perdict_default_woe 23291
## + predict_NonDefault_woe 23291
## + Type.of.residence.xOwned_woe 23291
## + No.of.trades.opened.in.last.6.months_woe 23291
## + No.of.PL.trades.opened.in.last.6.months_woe 23291
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23291
## + No.of.trades.opened.in.last.12.months_woe 23291
## - No.of.months.in.current.company_woe 23303
## - Age_woe 23304
## - Avgas.CC.Utilization.in.last.12.months_woe 23312
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23318
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23319
## AIC
## - Marital.Status..at.the.time.of.application..xSingle_woe 23338
## - Marital.Status..at.the.time.of.application..xMarried_woe 23338
## - Education.xMasters_woe 23338
## - Education.xBachelor_woe 23338
## - No.of.months.in.current.residence_woe 23338
## - Education.xPhd_woe 23338
## - Education.xProfessional_woe 23338
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23339
## - Gender.xF_woe 23339
## - Outstanding.Balance_woe 23339
## - Profession.xSAL_woe 23339
## - Profession.xSE_PROF_woe 23339
## - Profession.xSE_woe 23339
## - Score_woe 23339
## - odds_woe 23339
## <none> 23339
## - No.of.PL.trades.opened.in.last.12.months_woe 23340
## - Income_woe 23340
## - Total.No.of.Trades_woe 23341
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23341
## + Type.of.residence.xRented_woe 23341
## + Gender.xM_woe 23341
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23341
## + Presence.of.open.home.loan_woe 23341
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23341
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23341
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23341
## + perdict_default_woe 23341
## + predict_NonDefault_woe 23341
## + Type.of.residence.xOwned_woe 23341
## + No.of.trades.opened.in.last.6.months_woe 23341
## + No.of.PL.trades.opened.in.last.6.months_woe 23341
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23341
## + No.of.trades.opened.in.last.12.months_woe 23341
## - No.of.months.in.current.company_woe 23349
## - Age_woe 23350
## - Avgas.CC.Utilization.in.last.12.months_woe 23358
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23364
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23365
##
## Step: AIC=23337.92
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Marital.Status..at.the.time.of.application..xMarried_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + odds_woe + Score_woe
##
## Df
## - Marital.Status..at.the.time.of.application..xMarried_woe 1
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Score_woe 1
## - Profession.xSE_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + Type.of.residence.xRented_woe 1
## + Gender.xM_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Marital.Status..at.the.time.of.application..xMarried_woe 23292
## - Education.xMasters_woe 23293
## - Education.xBachelor_woe 23293
## - No.of.months.in.current.residence_woe 23293
## - Education.xPhd_woe 23293
## - Education.xProfessional_woe 23293
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23293
## - Gender.xF_woe 23293
## - Outstanding.Balance_woe 23293
## - Profession.xSAL_woe 23294
## - Profession.xSE_PROF_woe 23294
## - Score_woe 23294
## - Profession.xSE_woe 23294
## - odds_woe 23294
## <none> 23292
## - No.of.PL.trades.opened.in.last.12.months_woe 23294
## - Income_woe 23294
## - Total.No.of.Trades_woe 23295
## + Marital.Status..at.the.time.of.application..xSingle_woe 23291
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23292
## + Type.of.residence.xRented_woe 23292
## + Gender.xM_woe 23292
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23292
## + Presence.of.open.home.loan_woe 23292
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23292
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23292
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23292
## + perdict_default_woe 23292
## + predict_NonDefault_woe 23292
## + Type.of.residence.xOwned_woe 23292
## + No.of.trades.opened.in.last.6.months_woe 23292
## + No.of.PL.trades.opened.in.last.6.months_woe 23292
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23292
## + No.of.trades.opened.in.last.12.months_woe 23292
## - No.of.months.in.current.company_woe 23304
## - Age_woe 23304
## - Avgas.CC.Utilization.in.last.12.months_woe 23312
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23319
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23319
## AIC
## - Marital.Status..at.the.time.of.application..xMarried_woe 23336
## - Education.xMasters_woe 23337
## - Education.xBachelor_woe 23337
## - No.of.months.in.current.residence_woe 23337
## - Education.xPhd_woe 23337
## - Education.xProfessional_woe 23337
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23337
## - Gender.xF_woe 23337
## - Outstanding.Balance_woe 23337
## - Profession.xSAL_woe 23338
## - Profession.xSE_PROF_woe 23338
## - Score_woe 23338
## - Profession.xSE_woe 23338
## - odds_woe 23338
## <none> 23338
## - No.of.PL.trades.opened.in.last.12.months_woe 23338
## - Income_woe 23338
## - Total.No.of.Trades_woe 23339
## + Marital.Status..at.the.time.of.application..xSingle_woe 23339
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23340
## + Type.of.residence.xRented_woe 23340
## + Gender.xM_woe 23340
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23340
## + Presence.of.open.home.loan_woe 23340
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23340
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23340
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23340
## + perdict_default_woe 23340
## + predict_NonDefault_woe 23340
## + Type.of.residence.xOwned_woe 23340
## + No.of.trades.opened.in.last.6.months_woe 23340
## + No.of.PL.trades.opened.in.last.6.months_woe 23340
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23340
## + No.of.trades.opened.in.last.12.months_woe 23340
## - No.of.months.in.current.company_woe 23348
## - Age_woe 23348
## - Avgas.CC.Utilization.in.last.12.months_woe 23356
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23363
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23363
##
## Step: AIC=23336.01
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Education.xBachelor_woe + Education.xMasters_woe + Education.xPhd_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + odds_woe + Score_woe
##
## Df
## - Education.xMasters_woe 1
## - Education.xBachelor_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xPhd_woe 1
## - Education.xProfessional_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Score_woe 1
## - Profession.xSE_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + Type.of.residence.xRented_woe 1
## + Gender.xM_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Education.xMasters_woe 23293
## - Education.xBachelor_woe 23293
## - No.of.months.in.current.residence_woe 23293
## - Education.xPhd_woe 23293
## - Education.xProfessional_woe 23293
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23293
## - Gender.xF_woe 23293
## - Outstanding.Balance_woe 23293
## - Profession.xSAL_woe 23294
## - Profession.xSE_PROF_woe 23294
## - Score_woe 23294
## - Profession.xSE_woe 23294
## - odds_woe 23294
## <none> 23292
## - No.of.PL.trades.opened.in.last.12.months_woe 23294
## - Income_woe 23295
## - Total.No.of.Trades_woe 23295
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23292
## + Type.of.residence.xRented_woe 23292
## + Gender.xM_woe 23292
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23292
## + Presence.of.open.home.loan_woe 23292
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23292
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23292
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23292
## + Marital.Status..at.the.time.of.application..xMarried_woe 23292
## + Marital.Status..at.the.time.of.application..xSingle_woe 23292
## + perdict_default_woe 23292
## + predict_NonDefault_woe 23292
## + Type.of.residence.xOwned_woe 23292
## + No.of.trades.opened.in.last.6.months_woe 23292
## + No.of.PL.trades.opened.in.last.6.months_woe 23292
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23292
## + No.of.trades.opened.in.last.12.months_woe 23292
## - No.of.months.in.current.company_woe 23304
## - Age_woe 23304
## - Avgas.CC.Utilization.in.last.12.months_woe 23313
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23319
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23319
## AIC
## - Education.xMasters_woe 23335
## - Education.xBachelor_woe 23335
## - No.of.months.in.current.residence_woe 23335
## - Education.xPhd_woe 23335
## - Education.xProfessional_woe 23335
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23335
## - Gender.xF_woe 23335
## - Outstanding.Balance_woe 23335
## - Profession.xSAL_woe 23336
## - Profession.xSE_PROF_woe 23336
## - Score_woe 23336
## - Profession.xSE_woe 23336
## - odds_woe 23336
## <none> 23336
## - No.of.PL.trades.opened.in.last.12.months_woe 23336
## - Income_woe 23337
## - Total.No.of.Trades_woe 23337
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23338
## + Type.of.residence.xRented_woe 23338
## + Gender.xM_woe 23338
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23338
## + Presence.of.open.home.loan_woe 23338
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23338
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23338
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23338
## + Marital.Status..at.the.time.of.application..xMarried_woe 23338
## + Marital.Status..at.the.time.of.application..xSingle_woe 23338
## + perdict_default_woe 23338
## + predict_NonDefault_woe 23338
## + Type.of.residence.xOwned_woe 23338
## + No.of.trades.opened.in.last.6.months_woe 23338
## + No.of.PL.trades.opened.in.last.6.months_woe 23338
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23338
## + No.of.trades.opened.in.last.12.months_woe 23338
## - No.of.months.in.current.company_woe 23346
## - Age_woe 23346
## - Avgas.CC.Utilization.in.last.12.months_woe 23355
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23361
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23361
##
## Step: AIC=23334.6
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Education.xBachelor_woe + Education.xPhd_woe + Education.xProfessional_woe +
## Profession.xSAL_woe + Profession.xSE_woe + Profession.xSE_PROF_woe +
## odds_woe + Score_woe
##
## Df
## - Education.xBachelor_woe 1
## - Education.xPhd_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xProfessional_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Score_woe 1
## - Profession.xSE_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + Education.xMasters_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + Type.of.residence.xRented_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Gender.xM_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Education.xBachelor_woe 23293
## - Education.xPhd_woe 23293
## - No.of.months.in.current.residence_woe 23293
## - Education.xProfessional_woe 23293
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23294
## - Gender.xF_woe 23294
## - Outstanding.Balance_woe 23294
## - Profession.xSAL_woe 23294
## - Profession.xSE_PROF_woe 23294
## - Score_woe 23294
## - Profession.xSE_woe 23294
## - odds_woe 23295
## <none> 23293
## - No.of.PL.trades.opened.in.last.12.months_woe 23295
## - Income_woe 23295
## - Total.No.of.Trades_woe 23296
## + Education.xMasters_woe 23292
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23292
## + Type.of.residence.xRented_woe 23292
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23292
## + Gender.xM_woe 23292
## + Presence.of.open.home.loan_woe 23292
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23293
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23293
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23293
## + Marital.Status..at.the.time.of.application..xMarried_woe 23293
## + Marital.Status..at.the.time.of.application..xSingle_woe 23293
## + perdict_default_woe 23293
## + predict_NonDefault_woe 23293
## + No.of.trades.opened.in.last.6.months_woe 23293
## + Type.of.residence.xOwned_woe 23293
## + No.of.PL.trades.opened.in.last.6.months_woe 23293
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23293
## + No.of.trades.opened.in.last.12.months_woe 23293
## - No.of.months.in.current.company_woe 23304
## - Age_woe 23305
## - Avgas.CC.Utilization.in.last.12.months_woe 23313
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23319
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23320
## AIC
## - Education.xBachelor_woe 23333
## - Education.xPhd_woe 23333
## - No.of.months.in.current.residence_woe 23333
## - Education.xProfessional_woe 23333
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23334
## - Gender.xF_woe 23334
## - Outstanding.Balance_woe 23334
## - Profession.xSAL_woe 23334
## - Profession.xSE_PROF_woe 23334
## - Score_woe 23334
## - Profession.xSE_woe 23334
## - odds_woe 23335
## <none> 23335
## - No.of.PL.trades.opened.in.last.12.months_woe 23335
## - Income_woe 23335
## - Total.No.of.Trades_woe 23336
## + Education.xMasters_woe 23336
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23336
## + Type.of.residence.xRented_woe 23336
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23336
## + Gender.xM_woe 23336
## + Presence.of.open.home.loan_woe 23336
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23337
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23337
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23337
## + Marital.Status..at.the.time.of.application..xMarried_woe 23337
## + Marital.Status..at.the.time.of.application..xSingle_woe 23337
## + perdict_default_woe 23337
## + predict_NonDefault_woe 23337
## + No.of.trades.opened.in.last.6.months_woe 23337
## + Type.of.residence.xOwned_woe 23337
## + No.of.PL.trades.opened.in.last.6.months_woe 23337
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23337
## + No.of.trades.opened.in.last.12.months_woe 23337
## - No.of.months.in.current.company_woe 23344
## - Age_woe 23345
## - Avgas.CC.Utilization.in.last.12.months_woe 23353
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23359
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23360
##
## Step: AIC=23332.61
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Education.xPhd_woe + Education.xProfessional_woe + Profession.xSAL_woe +
## Profession.xSE_woe + Profession.xSE_PROF_woe + odds_woe +
## Score_woe
##
## Df
## - Education.xPhd_woe 1
## - No.of.months.in.current.residence_woe 1
## - Education.xProfessional_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Score_woe 1
## - Profession.xSE_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + Type.of.residence.xRented_woe 1
## + Gender.xM_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + Education.xBachelor_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## + Education.xMasters_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Education.xPhd_woe 23293
## - No.of.months.in.current.residence_woe 23293
## - Education.xProfessional_woe 23293
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23294
## - Gender.xF_woe 23294
## - Outstanding.Balance_woe 23294
## - Profession.xSAL_woe 23294
## - Profession.xSE_PROF_woe 23294
## - Score_woe 23294
## - Profession.xSE_woe 23294
## - odds_woe 23295
## <none> 23293
## - No.of.PL.trades.opened.in.last.12.months_woe 23295
## - Income_woe 23295
## - Total.No.of.Trades_woe 23296
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23292
## + Type.of.residence.xRented_woe 23292
## + Gender.xM_woe 23292
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23292
## + Presence.of.open.home.loan_woe 23292
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23293
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23293
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23293
## + Marital.Status..at.the.time.of.application..xMarried_woe 23293
## + Marital.Status..at.the.time.of.application..xSingle_woe 23293
## + perdict_default_woe 23293
## + predict_NonDefault_woe 23293
## + Type.of.residence.xOwned_woe 23293
## + No.of.trades.opened.in.last.6.months_woe 23293
## + Education.xBachelor_woe 23293
## + No.of.PL.trades.opened.in.last.6.months_woe 23293
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23293
## + No.of.trades.opened.in.last.12.months_woe 23293
## + Education.xMasters_woe 23293
## - No.of.months.in.current.company_woe 23304
## - Age_woe 23305
## - Avgas.CC.Utilization.in.last.12.months_woe 23313
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23319
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23320
## AIC
## - Education.xPhd_woe 23331
## - No.of.months.in.current.residence_woe 23331
## - Education.xProfessional_woe 23331
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23332
## - Gender.xF_woe 23332
## - Outstanding.Balance_woe 23332
## - Profession.xSAL_woe 23332
## - Profession.xSE_PROF_woe 23332
## - Score_woe 23332
## - Profession.xSE_woe 23332
## - odds_woe 23333
## <none> 23333
## - No.of.PL.trades.opened.in.last.12.months_woe 23333
## - Income_woe 23333
## - Total.No.of.Trades_woe 23334
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23334
## + Type.of.residence.xRented_woe 23334
## + Gender.xM_woe 23334
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23334
## + Presence.of.open.home.loan_woe 23334
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23335
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23335
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23335
## + Marital.Status..at.the.time.of.application..xMarried_woe 23335
## + Marital.Status..at.the.time.of.application..xSingle_woe 23335
## + perdict_default_woe 23335
## + predict_NonDefault_woe 23335
## + Type.of.residence.xOwned_woe 23335
## + No.of.trades.opened.in.last.6.months_woe 23335
## + Education.xBachelor_woe 23335
## + No.of.PL.trades.opened.in.last.6.months_woe 23335
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23335
## + No.of.trades.opened.in.last.12.months_woe 23335
## + Education.xMasters_woe 23335
## - No.of.months.in.current.company_woe 23342
## - Age_woe 23343
## - Avgas.CC.Utilization.in.last.12.months_woe 23351
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23357
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23358
##
## Step: AIC=23330.78
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.residence_woe +
## No.of.months.in.current.company_woe + Total.No.of.Trades_woe +
## Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + odds_woe + Score_woe
##
## Df
## - No.of.months.in.current.residence_woe 1
## - Education.xProfessional_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Score_woe 1
## - Profession.xSE_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + Type.of.residence.xRented_woe 1
## + Gender.xM_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Presence.of.open.home.loan_woe 1
## + Education.xPhd_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + Education.xMasters_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## + Education.xBachelor_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - No.of.months.in.current.residence_woe 23293
## - Education.xProfessional_woe 23294
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23294
## - Gender.xF_woe 23294
## - Outstanding.Balance_woe 23294
## - Profession.xSAL_woe 23294
## - Profession.xSE_PROF_woe 23294
## - Score_woe 23295
## - Profession.xSE_woe 23295
## - odds_woe 23295
## <none> 23293
## - No.of.PL.trades.opened.in.last.12.months_woe 23295
## - Income_woe 23295
## - Total.No.of.Trades_woe 23296
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23292
## + Type.of.residence.xRented_woe 23293
## + Gender.xM_woe 23293
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23293
## + Presence.of.open.home.loan_woe 23293
## + Education.xPhd_woe 23293
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23293
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23293
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23293
## + Marital.Status..at.the.time.of.application..xMarried_woe 23293
## + Marital.Status..at.the.time.of.application..xSingle_woe 23293
## + perdict_default_woe 23293
## + predict_NonDefault_woe 23293
## + Type.of.residence.xOwned_woe 23293
## + No.of.trades.opened.in.last.6.months_woe 23293
## + Education.xMasters_woe 23293
## + No.of.PL.trades.opened.in.last.6.months_woe 23293
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23293
## + No.of.trades.opened.in.last.12.months_woe 23293
## + Education.xBachelor_woe 23293
## - No.of.months.in.current.company_woe 23304
## - Age_woe 23305
## - Avgas.CC.Utilization.in.last.12.months_woe 23313
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23320
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23320
## AIC
## - No.of.months.in.current.residence_woe 23329
## - Education.xProfessional_woe 23330
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23330
## - Gender.xF_woe 23330
## - Outstanding.Balance_woe 23330
## - Profession.xSAL_woe 23330
## - Profession.xSE_PROF_woe 23330
## - Score_woe 23331
## - Profession.xSE_woe 23331
## - odds_woe 23331
## <none> 23331
## - No.of.PL.trades.opened.in.last.12.months_woe 23331
## - Income_woe 23331
## - Total.No.of.Trades_woe 23332
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23332
## + Type.of.residence.xRented_woe 23333
## + Gender.xM_woe 23333
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23333
## + Presence.of.open.home.loan_woe 23333
## + Education.xPhd_woe 23333
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23333
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23333
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23333
## + Marital.Status..at.the.time.of.application..xMarried_woe 23333
## + Marital.Status..at.the.time.of.application..xSingle_woe 23333
## + perdict_default_woe 23333
## + predict_NonDefault_woe 23333
## + Type.of.residence.xOwned_woe 23333
## + No.of.trades.opened.in.last.6.months_woe 23333
## + Education.xMasters_woe 23333
## + No.of.PL.trades.opened.in.last.6.months_woe 23333
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23333
## + No.of.trades.opened.in.last.12.months_woe 23333
## + Education.xBachelor_woe 23333
## - No.of.months.in.current.company_woe 23340
## - Age_woe 23341
## - Avgas.CC.Utilization.in.last.12.months_woe 23349
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23356
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23356
##
## Step: AIC=23329.41
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Total.No.of.Trades_woe + Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Education.xProfessional_woe + Profession.xSAL_woe + Profession.xSE_woe +
## Profession.xSE_PROF_woe + odds_woe + Score_woe
##
## Df
## - Education.xProfessional_woe 1
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Score_woe 1
## - Profession.xSE_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.months.in.current.residence_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + Type.of.residence.xRented_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Gender.xM_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + Education.xPhd_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + Education.xMasters_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## + Education.xBachelor_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Education.xProfessional_woe 23294
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23295
## - Gender.xF_woe 23295
## - Outstanding.Balance_woe 23295
## - Profession.xSAL_woe 23295
## - Profession.xSE_PROF_woe 23295
## - Score_woe 23295
## - Profession.xSE_woe 23295
## - odds_woe 23295
## <none> 23293
## - No.of.PL.trades.opened.in.last.12.months_woe 23296
## - Income_woe 23296
## - Total.No.of.Trades_woe 23297
## + No.of.months.in.current.residence_woe 23293
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23293
## + Type.of.residence.xRented_woe 23293
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23293
## + Gender.xM_woe 23293
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23293
## + Education.xPhd_woe 23293
## + Presence.of.open.home.loan_woe 23293
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23293
## + perdict_default_woe 23293
## + predict_NonDefault_woe 23293
## + No.of.trades.opened.in.last.6.months_woe 23293
## + Marital.Status..at.the.time.of.application..xMarried_woe 23293
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23293
## + Marital.Status..at.the.time.of.application..xSingle_woe 23293
## + Type.of.residence.xOwned_woe 23293
## + No.of.PL.trades.opened.in.last.6.months_woe 23293
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23293
## + Education.xMasters_woe 23293
## + No.of.trades.opened.in.last.12.months_woe 23293
## + Education.xBachelor_woe 23293
## - No.of.months.in.current.company_woe 23305
## - Age_woe 23306
## - Avgas.CC.Utilization.in.last.12.months_woe 23317
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23320
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23321
## AIC
## - Education.xProfessional_woe 23328
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23329
## - Gender.xF_woe 23329
## - Outstanding.Balance_woe 23329
## - Profession.xSAL_woe 23329
## - Profession.xSE_PROF_woe 23329
## - Score_woe 23329
## - Profession.xSE_woe 23329
## - odds_woe 23329
## <none> 23329
## - No.of.PL.trades.opened.in.last.12.months_woe 23330
## - Income_woe 23330
## - Total.No.of.Trades_woe 23331
## + No.of.months.in.current.residence_woe 23331
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23331
## + Type.of.residence.xRented_woe 23331
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23331
## + Gender.xM_woe 23331
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23331
## + Education.xPhd_woe 23331
## + Presence.of.open.home.loan_woe 23331
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23331
## + perdict_default_woe 23331
## + predict_NonDefault_woe 23331
## + No.of.trades.opened.in.last.6.months_woe 23331
## + Marital.Status..at.the.time.of.application..xMarried_woe 23331
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23331
## + Marital.Status..at.the.time.of.application..xSingle_woe 23331
## + Type.of.residence.xOwned_woe 23331
## + No.of.PL.trades.opened.in.last.6.months_woe 23331
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23331
## + Education.xMasters_woe 23331
## + No.of.trades.opened.in.last.12.months_woe 23331
## + Education.xBachelor_woe 23331
## - No.of.months.in.current.company_woe 23339
## - Age_woe 23340
## - Avgas.CC.Utilization.in.last.12.months_woe 23351
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23354
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23355
##
## Step: AIC=23328.13
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Total.No.of.Trades_woe + Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.90.DPD.or.worse.in.last.12.months_woe + No.of.times.30.DPD.or.worse.in.last.12.months_woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Profession.xSAL_woe + Profession.xSE_woe + Profession.xSE_PROF_woe +
## odds_woe + Score_woe
##
## Df
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Score_woe 1
## - Profession.xSE_woe 1
## - odds_woe 1
## <none>
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## - Total.No.of.Trades_woe 1
## + Education.xProfessional_woe 1
## + No.of.months.in.current.residence_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + Education.xMasters_woe 1
## + Type.of.residence.xRented_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Gender.xM_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + Education.xBachelor_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + Type.of.residence.xOwned_woe 1
## + Education.xPhd_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23295
## - Gender.xF_woe 23295
## - Outstanding.Balance_woe 23296
## - Profession.xSAL_woe 23296
## - Profession.xSE_PROF_woe 23296
## - Score_woe 23296
## - Profession.xSE_woe 23296
## - odds_woe 23296
## <none> 23294
## - No.of.PL.trades.opened.in.last.12.months_woe 23296
## - Income_woe 23297
## - Total.No.of.Trades_woe 23297
## + Education.xProfessional_woe 23293
## + No.of.months.in.current.residence_woe 23294
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23294
## + Education.xMasters_woe 23294
## + Type.of.residence.xRented_woe 23294
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23294
## + Gender.xM_woe 23294
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23294
## + Presence.of.open.home.loan_woe 23294
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23294
## + Education.xBachelor_woe 23294
## + perdict_default_woe 23294
## + predict_NonDefault_woe 23294
## + Marital.Status..at.the.time.of.application..xMarried_woe 23294
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23294
## + No.of.trades.opened.in.last.6.months_woe 23294
## + Marital.Status..at.the.time.of.application..xSingle_woe 23294
## + Type.of.residence.xOwned_woe 23294
## + Education.xPhd_woe 23294
## + No.of.PL.trades.opened.in.last.6.months_woe 23294
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23294
## + No.of.trades.opened.in.last.12.months_woe 23294
## - No.of.months.in.current.company_woe 23306
## - Age_woe 23306
## - Avgas.CC.Utilization.in.last.12.months_woe 23317
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23320
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23322
## AIC
## - No.of.times.90.DPD.or.worse.in.last.12.months_woe 23327
## - Gender.xF_woe 23327
## - Outstanding.Balance_woe 23328
## - Profession.xSAL_woe 23328
## - Profession.xSE_PROF_woe 23328
## - Score_woe 23328
## - Profession.xSE_woe 23328
## - odds_woe 23328
## <none> 23328
## - No.of.PL.trades.opened.in.last.12.months_woe 23328
## - Income_woe 23329
## - Total.No.of.Trades_woe 23329
## + Education.xProfessional_woe 23329
## + No.of.months.in.current.residence_woe 23330
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23330
## + Education.xMasters_woe 23330
## + Type.of.residence.xRented_woe 23330
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23330
## + Gender.xM_woe 23330
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23330
## + Presence.of.open.home.loan_woe 23330
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23330
## + Education.xBachelor_woe 23330
## + perdict_default_woe 23330
## + predict_NonDefault_woe 23330
## + Marital.Status..at.the.time.of.application..xMarried_woe 23330
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23330
## + No.of.trades.opened.in.last.6.months_woe 23330
## + Marital.Status..at.the.time.of.application..xSingle_woe 23330
## + Type.of.residence.xOwned_woe 23330
## + Education.xPhd_woe 23330
## + No.of.PL.trades.opened.in.last.6.months_woe 23330
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23330
## + No.of.trades.opened.in.last.12.months_woe 23330
## - No.of.months.in.current.company_woe 23338
## - Age_woe 23338
## - Avgas.CC.Utilization.in.last.12.months_woe 23349
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23352
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23354
##
## Step: AIC=23327.21
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Total.No.of.Trades_woe + Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Gender.xF_woe +
## Profession.xSAL_woe + Profession.xSE_woe + Profession.xSE_PROF_woe +
## odds_woe + Score_woe
##
## Df
## - Gender.xF_woe 1
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - Score_woe 1
## <none>
## - odds_woe 1
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + Education.xProfessional_woe 1
## + No.of.months.in.current.residence_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + Education.xMasters_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Type.of.residence.xRented_woe 1
## + Gender.xM_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + Presence.of.open.home.loan_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Education.xBachelor_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + Type.of.residence.xOwned_woe 1
## + Education.xPhd_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Gender.xF_woe 23296
## - Outstanding.Balance_woe 23297
## - Profession.xSAL_woe 23297
## - Profession.xSE_PROF_woe 23297
## - Profession.xSE_woe 23297
## - Score_woe 23297
## <none> 23295
## - odds_woe 23297
## - No.of.PL.trades.opened.in.last.12.months_woe 23298
## - Income_woe 23298
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23294
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23294
## + Education.xProfessional_woe 23295
## + No.of.months.in.current.residence_woe 23295
## - Total.No.of.Trades_woe 23299
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23295
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23295
## + Education.xMasters_woe 23295
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23295
## + Type.of.residence.xRented_woe 23295
## + Gender.xM_woe 23295
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23295
## + Presence.of.open.home.loan_woe 23295
## + perdict_default_woe 23295
## + predict_NonDefault_woe 23295
## + Education.xBachelor_woe 23295
## + No.of.trades.opened.in.last.6.months_woe 23295
## + Marital.Status..at.the.time.of.application..xMarried_woe 23295
## + Marital.Status..at.the.time.of.application..xSingle_woe 23295
## + Type.of.residence.xOwned_woe 23295
## + Education.xPhd_woe 23295
## + No.of.PL.trades.opened.in.last.6.months_woe 23295
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23295
## + No.of.trades.opened.in.last.12.months_woe 23295
## - No.of.months.in.current.company_woe 23307
## - Age_woe 23308
## - Avgas.CC.Utilization.in.last.12.months_woe 23318
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23321
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23337
## AIC
## - Gender.xF_woe 23326
## - Outstanding.Balance_woe 23327
## - Profession.xSAL_woe 23327
## - Profession.xSE_PROF_woe 23327
## - Profession.xSE_woe 23327
## - Score_woe 23327
## <none> 23327
## - odds_woe 23327
## - No.of.PL.trades.opened.in.last.12.months_woe 23328
## - Income_woe 23328
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23328
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23328
## + Education.xProfessional_woe 23329
## + No.of.months.in.current.residence_woe 23329
## - Total.No.of.Trades_woe 23329
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23329
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23329
## + Education.xMasters_woe 23329
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23329
## + Type.of.residence.xRented_woe 23329
## + Gender.xM_woe 23329
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23329
## + Presence.of.open.home.loan_woe 23329
## + perdict_default_woe 23329
## + predict_NonDefault_woe 23329
## + Education.xBachelor_woe 23329
## + No.of.trades.opened.in.last.6.months_woe 23329
## + Marital.Status..at.the.time.of.application..xMarried_woe 23329
## + Marital.Status..at.the.time.of.application..xSingle_woe 23329
## + Type.of.residence.xOwned_woe 23329
## + Education.xPhd_woe 23329
## + No.of.PL.trades.opened.in.last.6.months_woe 23329
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23329
## + No.of.trades.opened.in.last.12.months_woe 23329
## - No.of.months.in.current.company_woe 23337
## - Age_woe 23338
## - Avgas.CC.Utilization.in.last.12.months_woe 23348
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23351
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23367
##
## Step: AIC=23326.39
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Total.No.of.Trades_woe + Outstanding.Balance_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Profession.xSAL_woe +
## Profession.xSE_woe + Profession.xSE_PROF_woe + odds_woe +
## Score_woe
##
## Df
## - Outstanding.Balance_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Score_woe 1
## - Profession.xSE_woe 1
## <none>
## - odds_woe 1
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Income_woe 1
## + Gender.xF_woe 1
## + Gender.xM_woe 1
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + Education.xProfessional_woe 1
## - Total.No.of.Trades_woe 1
## + No.of.months.in.current.residence_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + Education.xMasters_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Type.of.residence.xRented_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + Presence.of.open.home.loan_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Education.xBachelor_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + Type.of.residence.xOwned_woe 1
## + Education.xPhd_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Outstanding.Balance_woe 23298
## - Profession.xSAL_woe 23298
## - Profession.xSE_PROF_woe 23298
## - Score_woe 23298
## - Profession.xSE_woe 23298
## <none> 23296
## - odds_woe 23299
## - No.of.PL.trades.opened.in.last.12.months_woe 23299
## - Income_woe 23299
## + Gender.xF_woe 23295
## + Gender.xM_woe 23295
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23295
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23295
## + Education.xProfessional_woe 23296
## - Total.No.of.Trades_woe 23300
## + No.of.months.in.current.residence_woe 23296
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23296
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23296
## + Education.xMasters_woe 23296
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23296
## + Type.of.residence.xRented_woe 23296
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23296
## + Presence.of.open.home.loan_woe 23296
## + perdict_default_woe 23296
## + predict_NonDefault_woe 23296
## + Education.xBachelor_woe 23296
## + Marital.Status..at.the.time.of.application..xMarried_woe 23296
## + No.of.trades.opened.in.last.6.months_woe 23296
## + Marital.Status..at.the.time.of.application..xSingle_woe 23296
## + Type.of.residence.xOwned_woe 23296
## + Education.xPhd_woe 23296
## + No.of.PL.trades.opened.in.last.6.months_woe 23296
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23296
## + No.of.trades.opened.in.last.12.months_woe 23296
## - No.of.months.in.current.company_woe 23308
## - Age_woe 23309
## - Avgas.CC.Utilization.in.last.12.months_woe 23319
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23322
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23338
## AIC
## - Outstanding.Balance_woe 23326
## - Profession.xSAL_woe 23326
## - Profession.xSE_PROF_woe 23326
## - Score_woe 23326
## - Profession.xSE_woe 23326
## <none> 23326
## - odds_woe 23327
## - No.of.PL.trades.opened.in.last.12.months_woe 23327
## - Income_woe 23327
## + Gender.xF_woe 23327
## + Gender.xM_woe 23327
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23327
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23327
## + Education.xProfessional_woe 23328
## - Total.No.of.Trades_woe 23328
## + No.of.months.in.current.residence_woe 23328
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23328
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23328
## + Education.xMasters_woe 23328
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23328
## + Type.of.residence.xRented_woe 23328
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23328
## + Presence.of.open.home.loan_woe 23328
## + perdict_default_woe 23328
## + predict_NonDefault_woe 23328
## + Education.xBachelor_woe 23328
## + Marital.Status..at.the.time.of.application..xMarried_woe 23328
## + No.of.trades.opened.in.last.6.months_woe 23328
## + Marital.Status..at.the.time.of.application..xSingle_woe 23328
## + Type.of.residence.xOwned_woe 23328
## + Education.xPhd_woe 23328
## + No.of.PL.trades.opened.in.last.6.months_woe 23328
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23328
## + No.of.trades.opened.in.last.12.months_woe 23328
## - No.of.months.in.current.company_woe 23336
## - Age_woe 23337
## - Avgas.CC.Utilization.in.last.12.months_woe 23347
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23350
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23366
##
## Step: AIC=23325.92
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Total.No.of.Trades_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## No.of.PL.trades.opened.in.last.12.months_woe + Profession.xSAL_woe +
## Profession.xSE_woe + Profession.xSE_PROF_woe + odds_woe +
## Score_woe
##
## Df
## - No.of.PL.trades.opened.in.last.12.months_woe 1
## - Score_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## <none>
## - odds_woe 1
## + Outstanding.Balance_woe 1
## - Income_woe 1
## + Gender.xF_woe 1
## + Gender.xM_woe 1
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + No.of.months.in.current.residence_woe 1
## + Education.xProfessional_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## - Total.No.of.Trades_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Education.xMasters_woe 1
## + Type.of.residence.xRented_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + Education.xBachelor_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + Education.xPhd_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - No.of.PL.trades.opened.in.last.12.months_woe 23299
## - Score_woe 23300
## - Profession.xSAL_woe 23300
## - Profession.xSE_PROF_woe 23300
## - Profession.xSE_woe 23300
## <none> 23298
## - odds_woe 23300
## + Outstanding.Balance_woe 23296
## - Income_woe 23301
## + Gender.xF_woe 23297
## + Gender.xM_woe 23297
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23297
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23297
## + No.of.months.in.current.residence_woe 23297
## + Education.xProfessional_woe 23297
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23297
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23297
## - Total.No.of.Trades_woe 23302
## + Presence.of.open.home.loan_woe 23298
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23298
## + Education.xMasters_woe 23298
## + Type.of.residence.xRented_woe 23298
## + perdict_default_woe 23298
## + predict_NonDefault_woe 23298
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23298
## + Education.xBachelor_woe 23298
## + No.of.PL.trades.opened.in.last.6.months_woe 23298
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23298
## + Marital.Status..at.the.time.of.application..xMarried_woe 23298
## + Marital.Status..at.the.time.of.application..xSingle_woe 23298
## + Type.of.residence.xOwned_woe 23298
## + No.of.trades.opened.in.last.6.months_woe 23298
## + Education.xPhd_woe 23298
## + No.of.trades.opened.in.last.12.months_woe 23298
## - No.of.months.in.current.company_woe 23310
## - Age_woe 23310
## - Avgas.CC.Utilization.in.last.12.months_woe 23324
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23326
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23339
## AIC
## - No.of.PL.trades.opened.in.last.12.months_woe 23325
## - Score_woe 23326
## - Profession.xSAL_woe 23326
## - Profession.xSE_PROF_woe 23326
## - Profession.xSE_woe 23326
## <none> 23326
## - odds_woe 23326
## + Outstanding.Balance_woe 23326
## - Income_woe 23327
## + Gender.xF_woe 23327
## + Gender.xM_woe 23327
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23327
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23327
## + No.of.months.in.current.residence_woe 23327
## + Education.xProfessional_woe 23327
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23327
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23327
## - Total.No.of.Trades_woe 23328
## + Presence.of.open.home.loan_woe 23328
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23328
## + Education.xMasters_woe 23328
## + Type.of.residence.xRented_woe 23328
## + perdict_default_woe 23328
## + predict_NonDefault_woe 23328
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23328
## + Education.xBachelor_woe 23328
## + No.of.PL.trades.opened.in.last.6.months_woe 23328
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23328
## + Marital.Status..at.the.time.of.application..xMarried_woe 23328
## + Marital.Status..at.the.time.of.application..xSingle_woe 23328
## + Type.of.residence.xOwned_woe 23328
## + No.of.trades.opened.in.last.6.months_woe 23328
## + Education.xPhd_woe 23328
## + No.of.trades.opened.in.last.12.months_woe 23328
## - No.of.months.in.current.company_woe 23336
## - Age_woe 23336
## - Avgas.CC.Utilization.in.last.12.months_woe 23350
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23352
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23365
##
## Step: AIC=23325.17
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Total.No.of.Trades_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## Profession.xSAL_woe + Profession.xSE_woe + Profession.xSE_PROF_woe +
## odds_woe + Score_woe
##
## Df
## - Score_woe 1
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## - odds_woe 1
## <none>
## - Total.No.of.Trades_woe 1
## + No.of.PL.trades.opened.in.last.12.months_woe 1
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Income_woe 1
## + Gender.xF_woe 1
## + Gender.xM_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + No.of.months.in.current.residence_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + Education.xProfessional_woe 1
## + Outstanding.Balance_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Presence.of.open.home.loan_woe 1
## + Education.xMasters_woe 1
## + Type.of.residence.xRented_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + Education.xBachelor_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + Type.of.residence.xOwned_woe 1
## + Education.xPhd_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Score_woe 23301
## - Profession.xSAL_woe 23301
## - Profession.xSE_PROF_woe 23301
## - Profession.xSE_woe 23301
## - odds_woe 23301
## <none> 23299
## - Total.No.of.Trades_woe 23302
## + No.of.PL.trades.opened.in.last.12.months_woe 23298
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23298
## - Income_woe 23302
## + Gender.xF_woe 23298
## + Gender.xM_woe 23298
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23298
## + No.of.months.in.current.residence_woe 23298
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23298
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23298
## + Education.xProfessional_woe 23299
## + Outstanding.Balance_woe 23299
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23299
## + Presence.of.open.home.loan_woe 23299
## + Education.xMasters_woe 23299
## + Type.of.residence.xRented_woe 23299
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23299
## + No.of.trades.opened.in.last.12.months_woe 23299
## + No.of.PL.trades.opened.in.last.6.months_woe 23299
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23299
## + Education.xBachelor_woe 23299
## + No.of.trades.opened.in.last.6.months_woe 23299
## + perdict_default_woe 23299
## + predict_NonDefault_woe 23299
## + Marital.Status..at.the.time.of.application..xMarried_woe 23299
## + Marital.Status..at.the.time.of.application..xSingle_woe 23299
## + Type.of.residence.xOwned_woe 23299
## + Education.xPhd_woe 23299
## - No.of.months.in.current.company_woe 23311
## - Age_woe 23311
## - Avgas.CC.Utilization.in.last.12.months_woe 23326
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23327
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23347
## AIC
## - Score_woe 23325
## - Profession.xSAL_woe 23325
## - Profession.xSE_PROF_woe 23325
## - Profession.xSE_woe 23325
## - odds_woe 23325
## <none> 23325
## - Total.No.of.Trades_woe 23326
## + No.of.PL.trades.opened.in.last.12.months_woe 23326
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23326
## - Income_woe 23326
## + Gender.xF_woe 23326
## + Gender.xM_woe 23326
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23326
## + No.of.months.in.current.residence_woe 23326
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23326
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23326
## + Education.xProfessional_woe 23327
## + Outstanding.Balance_woe 23327
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23327
## + Presence.of.open.home.loan_woe 23327
## + Education.xMasters_woe 23327
## + Type.of.residence.xRented_woe 23327
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23327
## + No.of.trades.opened.in.last.12.months_woe 23327
## + No.of.PL.trades.opened.in.last.6.months_woe 23327
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23327
## + Education.xBachelor_woe 23327
## + No.of.trades.opened.in.last.6.months_woe 23327
## + perdict_default_woe 23327
## + predict_NonDefault_woe 23327
## + Marital.Status..at.the.time.of.application..xMarried_woe 23327
## + Marital.Status..at.the.time.of.application..xSingle_woe 23327
## + Type.of.residence.xOwned_woe 23327
## + Education.xPhd_woe 23327
## - No.of.months.in.current.company_woe 23335
## - Age_woe 23335
## - Avgas.CC.Utilization.in.last.12.months_woe 23350
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23351
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23371
##
## Step: AIC=23324.52
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Total.No.of.Trades_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## Profession.xSAL_woe + Profession.xSE_woe + Profession.xSE_PROF_woe +
## odds_woe
##
## Df
## - Profession.xSAL_woe 1
## - Profession.xSE_PROF_woe 1
## - Profession.xSE_woe 1
## <none>
## - Total.No.of.Trades_woe 1
## + Score_woe 1
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## + Gender.xF_woe 1
## - Income_woe 1
## + Gender.xM_woe 1
## + No.of.PL.trades.opened.in.last.12.months_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.months.in.current.residence_woe 1
## + Education.xProfessional_woe 1
## + Outstanding.Balance_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Education.xMasters_woe 1
## + Type.of.residence.xRented_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## + Education.xBachelor_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + Education.xPhd_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - odds_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Profession.xSAL_woe 23302
## - Profession.xSE_PROF_woe 23302
## - Profession.xSE_woe 23302
## <none> 23301
## - Total.No.of.Trades_woe 23303
## + Score_woe 23299
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23299
## + Gender.xF_woe 23299
## - Income_woe 23303
## + Gender.xM_woe 23299
## + No.of.PL.trades.opened.in.last.12.months_woe 23300
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23300
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23300
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23300
## + No.of.months.in.current.residence_woe 23300
## + Education.xProfessional_woe 23300
## + Outstanding.Balance_woe 23300
## + Presence.of.open.home.loan_woe 23300
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23300
## + Education.xMasters_woe 23300
## + Type.of.residence.xRented_woe 23300
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23300
## + No.of.trades.opened.in.last.12.months_woe 23300
## + Education.xBachelor_woe 23300
## + No.of.trades.opened.in.last.6.months_woe 23300
## + perdict_default_woe 23300
## + predict_NonDefault_woe 23300
## + Marital.Status..at.the.time.of.application..xMarried_woe 23300
## + Marital.Status..at.the.time.of.application..xSingle_woe 23300
## + Education.xPhd_woe 23301
## + Type.of.residence.xOwned_woe 23301
## + No.of.PL.trades.opened.in.last.6.months_woe 23301
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23301
## - No.of.months.in.current.company_woe 23312
## - Age_woe 23313
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23328
## - Avgas.CC.Utilization.in.last.12.months_woe 23328
## - odds_woe 23330
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23350
## AIC
## - Profession.xSAL_woe 23324
## - Profession.xSE_PROF_woe 23324
## - Profession.xSE_woe 23324
## <none> 23325
## - Total.No.of.Trades_woe 23325
## + Score_woe 23325
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23325
## + Gender.xF_woe 23325
## - Income_woe 23325
## + Gender.xM_woe 23325
## + No.of.PL.trades.opened.in.last.12.months_woe 23326
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23326
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23326
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23326
## + No.of.months.in.current.residence_woe 23326
## + Education.xProfessional_woe 23326
## + Outstanding.Balance_woe 23326
## + Presence.of.open.home.loan_woe 23326
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23326
## + Education.xMasters_woe 23326
## + Type.of.residence.xRented_woe 23326
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23326
## + No.of.trades.opened.in.last.12.months_woe 23326
## + Education.xBachelor_woe 23326
## + No.of.trades.opened.in.last.6.months_woe 23326
## + perdict_default_woe 23326
## + predict_NonDefault_woe 23326
## + Marital.Status..at.the.time.of.application..xMarried_woe 23326
## + Marital.Status..at.the.time.of.application..xSingle_woe 23326
## + Education.xPhd_woe 23327
## + Type.of.residence.xOwned_woe 23327
## + No.of.PL.trades.opened.in.last.6.months_woe 23327
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23327
## - No.of.months.in.current.company_woe 23334
## - Age_woe 23335
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23350
## - Avgas.CC.Utilization.in.last.12.months_woe 23350
## - odds_woe 23352
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23372
##
## Step: AIC=23324.07
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Total.No.of.Trades_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## Profession.xSE_woe + Profession.xSE_PROF_woe + odds_woe
##
## Df
## - Profession.xSE_PROF_woe 1
## <none>
## + Profession.xSAL_woe 1
## - Total.No.of.Trades_woe 1
## + Score_woe 1
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Income_woe 1
## + Gender.xF_woe 1
## + Gender.xM_woe 1
## + No.of.PL.trades.opened.in.last.12.months_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.months.in.current.residence_woe 1
## + Education.xProfessional_woe 1
## - Profession.xSE_woe 1
## + Outstanding.Balance_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Education.xMasters_woe 1
## + Type.of.residence.xRented_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## + Education.xBachelor_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + Education.xPhd_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - odds_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## - Profession.xSE_PROF_woe 23302
## <none> 23302
## + Profession.xSAL_woe 23301
## - Total.No.of.Trades_woe 23305
## + Score_woe 23301
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23301
## - Income_woe 23305
## + Gender.xF_woe 23301
## + Gender.xM_woe 23301
## + No.of.PL.trades.opened.in.last.12.months_woe 23301
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23301
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23301
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23301
## + No.of.months.in.current.residence_woe 23301
## + Education.xProfessional_woe 23301
## - Profession.xSE_woe 23306
## + Outstanding.Balance_woe 23302
## + Presence.of.open.home.loan_woe 23302
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23302
## + Education.xMasters_woe 23302
## + Type.of.residence.xRented_woe 23302
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23302
## + No.of.trades.opened.in.last.12.months_woe 23302
## + Education.xBachelor_woe 23302
## + No.of.trades.opened.in.last.6.months_woe 23302
## + perdict_default_woe 23302
## + predict_NonDefault_woe 23302
## + Marital.Status..at.the.time.of.application..xMarried_woe 23302
## + Marital.Status..at.the.time.of.application..xSingle_woe 23302
## + Education.xPhd_woe 23302
## + Type.of.residence.xOwned_woe 23302
## + No.of.PL.trades.opened.in.last.6.months_woe 23302
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23302
## - No.of.months.in.current.company_woe 23314
## - Age_woe 23314
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23329
## - Avgas.CC.Utilization.in.last.12.months_woe 23330
## - odds_woe 23331
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23351
## AIC
## - Profession.xSE_PROF_woe 23322
## <none> 23324
## + Profession.xSAL_woe 23325
## - Total.No.of.Trades_woe 23325
## + Score_woe 23325
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23325
## - Income_woe 23325
## + Gender.xF_woe 23325
## + Gender.xM_woe 23325
## + No.of.PL.trades.opened.in.last.12.months_woe 23325
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23325
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23325
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23325
## + No.of.months.in.current.residence_woe 23325
## + Education.xProfessional_woe 23325
## - Profession.xSE_woe 23326
## + Outstanding.Balance_woe 23326
## + Presence.of.open.home.loan_woe 23326
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23326
## + Education.xMasters_woe 23326
## + Type.of.residence.xRented_woe 23326
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23326
## + No.of.trades.opened.in.last.12.months_woe 23326
## + Education.xBachelor_woe 23326
## + No.of.trades.opened.in.last.6.months_woe 23326
## + perdict_default_woe 23326
## + predict_NonDefault_woe 23326
## + Marital.Status..at.the.time.of.application..xMarried_woe 23326
## + Marital.Status..at.the.time.of.application..xSingle_woe 23326
## + Education.xPhd_woe 23326
## + Type.of.residence.xOwned_woe 23326
## + No.of.PL.trades.opened.in.last.6.months_woe 23326
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23326
## - No.of.months.in.current.company_woe 23334
## - Age_woe 23334
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23349
## - Avgas.CC.Utilization.in.last.12.months_woe 23350
## - odds_woe 23351
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23371
##
## Step: AIC=23322.12
## Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Total.No.of.Trades_woe + Avgas.CC.Utilization.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## Profession.xSE_woe + odds_woe
##
## Df
## <none>
## - Total.No.of.Trades_woe 1
## + Score_woe 1
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 1
## - Income_woe 1
## + Gender.xF_woe 1
## + Gender.xM_woe 1
## + No.of.PL.trades.opened.in.last.12.months_woe 1
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 1
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 1
## + No.of.months.in.current.residence_woe 1
## + Education.xProfessional_woe 1
## - Profession.xSE_woe 1
## + Outstanding.Balance_woe 1
## + Presence.of.open.home.loan_woe 1
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 1
## + Education.xMasters_woe 1
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 1
## + Type.of.residence.xRented_woe 1
## + No.of.trades.opened.in.last.12.months_woe 1
## + Education.xBachelor_woe 1
## + No.of.trades.opened.in.last.6.months_woe 1
## + perdict_default_woe 1
## + predict_NonDefault_woe 1
## + Marital.Status..at.the.time.of.application..xMarried_woe 1
## + Marital.Status..at.the.time.of.application..xSingle_woe 1
## + Education.xPhd_woe 1
## + Type.of.residence.xOwned_woe 1
## + No.of.PL.trades.opened.in.last.6.months_woe 1
## + No.of.PL.trades.opened.in.last.6.months.1_woe 1
## + Profession.xSE_PROF_woe 1
## + Profession.xSAL_woe 1
## - No.of.months.in.current.company_woe 1
## - Age_woe 1
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1
## - Avgas.CC.Utilization.in.last.12.months_woe 1
## - odds_woe 1
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 1
## Deviance
## <none> 23302
## - Total.No.of.Trades_woe 23305
## + Score_woe 23301
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23301
## - Income_woe 23305
## + Gender.xF_woe 23301
## + Gender.xM_woe 23301
## + No.of.PL.trades.opened.in.last.12.months_woe 23301
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23301
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23301
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23301
## + No.of.months.in.current.residence_woe 23301
## + Education.xProfessional_woe 23301
## - Profession.xSE_woe 23306
## + Outstanding.Balance_woe 23302
## + Presence.of.open.home.loan_woe 23302
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23302
## + Education.xMasters_woe 23302
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23302
## + Type.of.residence.xRented_woe 23302
## + No.of.trades.opened.in.last.12.months_woe 23302
## + Education.xBachelor_woe 23302
## + No.of.trades.opened.in.last.6.months_woe 23302
## + perdict_default_woe 23302
## + predict_NonDefault_woe 23302
## + Marital.Status..at.the.time.of.application..xMarried_woe 23302
## + Marital.Status..at.the.time.of.application..xSingle_woe 23302
## + Education.xPhd_woe 23302
## + Type.of.residence.xOwned_woe 23302
## + No.of.PL.trades.opened.in.last.6.months_woe 23302
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23302
## + Profession.xSE_PROF_woe 23302
## + Profession.xSAL_woe 23302
## - No.of.months.in.current.company_woe 23314
## - Age_woe 23314
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23330
## - Avgas.CC.Utilization.in.last.12.months_woe 23330
## - odds_woe 23331
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23351
## AIC
## <none> 23322
## - Total.No.of.Trades_woe 23323
## + Score_woe 23323
## + No.of.times.90.DPD.or.worse.in.last.12.months_woe 23323
## - Income_woe 23323
## + Gender.xF_woe 23323
## + Gender.xM_woe 23323
## + No.of.PL.trades.opened.in.last.12.months_woe 23323
## + No.of.times.90.DPD.or.worse.in.last.6.months_woe 23323
## + No.of.times.60.DPD.or.worse.in.last.6.months_woe 23323
## + No.of.times.60.DPD.or.worse.in.last.12.months_woe 23323
## + No.of.months.in.current.residence_woe 23323
## + Education.xProfessional_woe 23323
## - Profession.xSE_woe 23324
## + Outstanding.Balance_woe 23324
## + Presence.of.open.home.loan_woe 23324
## + No.of.Inquiries.in.last.6.months..excluding.home...auto.loans._woe 23324
## + Education.xMasters_woe 23324
## + No.of.times.30.DPD.or.worse.in.last.6.months_woe 23324
## + Type.of.residence.xRented_woe 23324
## + No.of.trades.opened.in.last.12.months_woe 23324
## + Education.xBachelor_woe 23324
## + No.of.trades.opened.in.last.6.months_woe 23324
## + perdict_default_woe 23324
## + predict_NonDefault_woe 23324
## + Marital.Status..at.the.time.of.application..xMarried_woe 23324
## + Marital.Status..at.the.time.of.application..xSingle_woe 23324
## + Education.xPhd_woe 23324
## + Type.of.residence.xOwned_woe 23324
## + No.of.PL.trades.opened.in.last.6.months_woe 23324
## + No.of.PL.trades.opened.in.last.6.months.1_woe 23324
## + Profession.xSE_PROF_woe 23324
## + Profession.xSAL_woe 23324
## - No.of.months.in.current.company_woe 23332
## - Age_woe 23332
## - No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 23348
## - Avgas.CC.Utilization.in.last.12.months_woe 23348
## - odds_woe 23349
## - No.of.times.30.DPD.or.worse.in.last.12.months_woe 23369m_3 <- glm(Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
Avgas.CC.Utilization.in.last.12.months_woe + No.of.times.90.DPD.or.worse.in.last.6.months_woe +
No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.trades.opened.in.last.12.months_woe +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
Profession.xSE_woe
, family = "binomial", data = dt_woe)
summary(m_3)##
## Call:
## glm(formula = Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Avgas.CC.Utilization.in.last.12.months_woe + No.of.times.90.DPD.or.worse.in.last.6.months_woe +
## No.of.times.30.DPD.or.worse.in.last.6.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe +
## Profession.xSE_woe, family = "binomial", data = dt_woe)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5213 -0.3387 -0.2665 -0.1783 2.9962
##
## Coefficients:
## Estimate
## (Intercept) -3.06017
## Age_woe 0.80471
## Income_woe 0.21613
## No.of.months.in.current.company_woe 0.38178
## Avgas.CC.Utilization.in.last.12.months_woe 0.45118
## No.of.times.90.DPD.or.worse.in.last.6.months_woe 0.02616
## No.of.times.30.DPD.or.worse.in.last.6.months_woe 0.03600
## No.of.times.90.DPD.or.worse.in.last.12.months_woe 0.08703
## No.of.times.30.DPD.or.worse.in.last.12.months_woe 0.36668
## No.of.trades.opened.in.last.12.months_woe 0.21908
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 0.38852
## Profession.xSE_woe 0.76409
## Std. Error
## (Intercept) 0.02861
## Age_woe 0.23156
## Income_woe 0.09758
## No.of.months.in.current.company_woe 0.10881
## Avgas.CC.Utilization.in.last.12.months_woe 0.04892
## No.of.times.90.DPD.or.worse.in.last.6.months_woe 0.10933
## No.of.times.30.DPD.or.worse.in.last.6.months_woe 0.07136
## No.of.times.90.DPD.or.worse.in.last.12.months_woe 0.07160
## No.of.times.30.DPD.or.worse.in.last.12.months_woe 0.07563
## No.of.trades.opened.in.last.12.months_woe 0.06774
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 0.06905
## Profession.xSE_woe 0.39924
## z value
## (Intercept) -106.964
## Age_woe 3.475
## Income_woe 2.215
## No.of.months.in.current.company_woe 3.509
## Avgas.CC.Utilization.in.last.12.months_woe 9.223
## No.of.times.90.DPD.or.worse.in.last.6.months_woe 0.239
## No.of.times.30.DPD.or.worse.in.last.6.months_woe 0.504
## No.of.times.90.DPD.or.worse.in.last.12.months_woe 1.216
## No.of.times.30.DPD.or.worse.in.last.12.months_woe 4.849
## No.of.trades.opened.in.last.12.months_woe 3.234
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 5.626
## Profession.xSE_woe 1.914
## Pr(>|z|)
## (Intercept) < 2e-16
## Age_woe 0.00051
## Income_woe 0.02677
## No.of.months.in.current.company_woe 0.00045
## Avgas.CC.Utilization.in.last.12.months_woe < 2e-16
## No.of.times.90.DPD.or.worse.in.last.6.months_woe 0.81091
## No.of.times.30.DPD.or.worse.in.last.6.months_woe 0.61395
## No.of.times.90.DPD.or.worse.in.last.12.months_woe 0.22417
## No.of.times.30.DPD.or.worse.in.last.12.months_woe 1.24e-06
## No.of.trades.opened.in.last.12.months_woe 0.00122
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1.84e-08
## Profession.xSE_woe 0.05564
##
## (Intercept) ***
## Age_woe ***
## Income_woe *
## No.of.months.in.current.company_woe ***
## Avgas.CC.Utilization.in.last.12.months_woe ***
## No.of.times.90.DPD.or.worse.in.last.6.months_woe
## No.of.times.30.DPD.or.worse.in.last.6.months_woe
## No.of.times.90.DPD.or.worse.in.last.12.months_woe
## No.of.times.30.DPD.or.worse.in.last.12.months_woe ***
## No.of.trades.opened.in.last.12.months_woe **
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe ***
## Profession.xSE_woe .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 24427 on 69866 degrees of freedom
## Residual deviance: 23324 on 69855 degrees of freedom
## AIC: 23348
##
## Number of Fisher Scoring iterations: 6m_4 <- glm(Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
Avgas.CC.Utilization.in.last.12.months_woe +
No.of.times.90.DPD.or.worse.in.last.12.months_woe +
No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.trades.opened.in.last.12.months_woe +
No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe
, family = "binomial", data = dt_woe)
summary(m_4)##
## Call:
## glm(formula = Performance.Tag ~ Age_woe + Income_woe + No.of.months.in.current.company_woe +
## Avgas.CC.Utilization.in.last.12.months_woe + No.of.times.90.DPD.or.worse.in.last.12.months_woe +
## No.of.times.30.DPD.or.worse.in.last.12.months_woe + No.of.trades.opened.in.last.12.months_woe +
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe,
## family = "binomial", data = dt_woe)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5164 -0.3386 -0.2667 -0.1783 2.9904
##
## Coefficients:
## Estimate
## (Intercept) -3.06528
## Age_woe 0.80013
## Income_woe 0.21606
## No.of.months.in.current.company_woe 0.38194
## Avgas.CC.Utilization.in.last.12.months_woe 0.45052
## No.of.times.90.DPD.or.worse.in.last.12.months_woe 0.10235
## No.of.times.30.DPD.or.worse.in.last.12.months_woe 0.39584
## No.of.trades.opened.in.last.12.months_woe 0.21965
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 0.38890
## Std. Error
## (Intercept) 0.02275
## Age_woe 0.23153
## Income_woe 0.09758
## No.of.months.in.current.company_woe 0.10879
## Avgas.CC.Utilization.in.last.12.months_woe 0.04889
## No.of.times.90.DPD.or.worse.in.last.12.months_woe 0.06347
## No.of.times.30.DPD.or.worse.in.last.12.months_woe 0.05123
## No.of.trades.opened.in.last.12.months_woe 0.06775
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 0.06904
## z value
## (Intercept) -134.722
## Age_woe 3.456
## Income_woe 2.214
## No.of.months.in.current.company_woe 3.511
## Avgas.CC.Utilization.in.last.12.months_woe 9.216
## No.of.times.90.DPD.or.worse.in.last.12.months_woe 1.613
## No.of.times.30.DPD.or.worse.in.last.12.months_woe 7.727
## No.of.trades.opened.in.last.12.months_woe 3.242
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 5.633
## Pr(>|z|)
## (Intercept) < 2e-16
## Age_woe 0.000549
## Income_woe 0.026812
## No.of.months.in.current.company_woe 0.000447
## Avgas.CC.Utilization.in.last.12.months_woe < 2e-16
## No.of.times.90.DPD.or.worse.in.last.12.months_woe 0.106809
## No.of.times.30.DPD.or.worse.in.last.12.months_woe 1.11e-14
## No.of.trades.opened.in.last.12.months_woe 0.001186
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 1.77e-08
##
## (Intercept) ***
## Age_woe ***
## Income_woe *
## No.of.months.in.current.company_woe ***
## Avgas.CC.Utilization.in.last.12.months_woe ***
## No.of.times.90.DPD.or.worse.in.last.12.months_woe
## No.of.times.30.DPD.or.worse.in.last.12.months_woe ***
## No.of.trades.opened.in.last.12.months_woe **
## No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 24427 on 69866 degrees of freedom
## Residual deviance: 23328 on 69858 degrees of freedom
## AIC: 23346
##
## Number of Fisher Scoring iterations: 6vif(m_4)## variable gvif
## 1: Age_woe 1.004000
## 2: Income_woe 1.069368
## 3: No.of.months.in.current.company_woe 1.032944
## 4: Avgas.CC.Utilization.in.last.12.months_woe 1.661560
## 5: No.of.times.90.DPD.or.worse.in.last.12.months_woe 1.571021
## 6: No.of.times.30.DPD.or.worse.in.last.12.months_woe 1.797306
## 7: No.of.trades.opened.in.last.12.months_woe 2.608167
## 8: No.of.Inquiries.in.last.12.months..excluding.home...auto.loans._woe 2.440812score
card = scorecard(bins, m_4,points0 = 400,odds0 = 1/9,pdo = 20)credit score for only total score
library(scorecard)
final_df$score = scorecard_ply (final_df, card)summary(final_df$score)## score
## Min. :393.0
## 1st Qu.:418.0
## Median :431.0
## Mean :432.3
## 3rd Qu.:453.0
## Max. :464.0min = 393 , max = 464
Financial analysis
Without scorecard
approval_rate <-(nrow(merged_df) -nrow(rejected_applicants))/nrow(merged_df) *100
approval_rate## [1] 98.00118current approval rate : 98%
#Net Credit loss(without scorecard)
default_users_outstanding <- data_for_eda$Outstanding.Balance[which(data_for_eda$Performance.Tag==1)]
current_credit_loss<- sum(default_users_outstanding)
current_credit_loss## [1] 3711178158Current credit loss : 3711178158
With scorecard (Conservative cut -off)
New credit loss
default_users_ID <- master_data_backup$Application.ID [which(master_data_backup$Performance.Tag==1)]
t1<-final_df$Outstanding.Balance[which(final_df$Performance.Tag==1)]
t2<-final_df$Score[which(final_df$Performance.Tag==1)]
outstanding_ref <- cbind(default_users_ID,default_users_outstanding,scale(default_users_outstanding),t1,t2)
possible_defaults_with_more_than_419_score<-data.frame(subset(outstanding_ref,t2>419))
sum(possible_defaults_with_more_than_419_score$default_users_outstanding)## [1] 212951202New credit loss : 212951202
approval rate (with scorecard)
nrow(data.frame(subset(final_df,Score>419)))/nrow(final_df)## [1] 0.183377New approval rate : 18.33%
Although net credit loss is much lesser, Approval rate also goes down sharply. Which in turn could cause less sales. Hence in this Trade-off between sales and risk,we could afford to be less conservative in terms of score cut off. From score distribution plot we can observe that by setting cut off to 395 we can cover much higher sales without risking too much credit loss.
With scorecard (Balanced cut-off)
Credit loss
possible_defaults_with_more_than_395_score<-data.frame(subset(outstanding_ref,t2>395))
sum(possible_defaults_with_more_than_395_score$default_users_outstanding)## [1] 1712981767New credit loss : 1,71,29,81,767
Aprroval rate
nrow(data.frame(subset(final_df,Score>395)))/nrow(final_df)## [1] 0.707759New approval rate : 70.77%
This gives a more balanced result.