Credit card 1

 str(credit)

## 'data.frame':    1000 obs. of  21 variables:
##  $ Creditability                    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Account.Balance                  : int  1 1 2 1 1 1 1 1 4 2 ...
##  $ Duration.of.Credit..month.       : int  18 9 12 12 12 10 8 6 18 24 ...
##  $ Payment.Status.of.Previous.Credit: int  4 4 2 4 4 4 4 4 4 2 ...
##  $ Purpose                          : int  2 0 9 0 0 0 0 0 3 3 ...
##  $ Credit.Amount                    : int  1049 2799 841 2122 2171 2241 3398 1361 1098 3758 ...
##  $ Value.Savings.Stocks             : int  1 1 2 1 1 1 1 1 1 3 ...
##  $ Length.of.current.employment     : int  2 3 4 3 3 2 4 2 1 1 ...
##  $ Instalment.per.cent              : int  4 2 2 3 4 1 1 2 4 1 ...
##  $ Sex...Marital.Status             : int  2 3 2 3 3 3 3 3 2 2 ...
##  $ Guarantors                       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Duration.in.Current.address      : int  4 2 4 2 4 3 4 4 4 4 ...
##  $ Most.valuable.available.asset    : int  2 1 1 1 2 1 1 1 3 4 ...
##  $ Age..years.                      : int  21 36 23 39 38 48 39 40 65 23 ...
##  $ Concurrent.Credits               : int  3 3 3 3 1 3 3 3 3 3 ...
##  $ Type.of.apartment                : int  1 1 1 1 2 1 2 2 2 1 ...
##  $ No.of.Credits.at.this.Bank       : int  1 2 1 2 2 2 2 1 2 1 ...
##  $ Occupation                       : int  3 3 2 2 2 2 2 2 1 1 ...
##  $ No.of.dependents                 : int  1 2 1 2 1 2 1 2 1 1 ...
##  $ Telephone                        : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Foreign.Worker                   : int  1 1 1 2 2 2 2 2 1 1 ...

 F=c(1,2,4,5,7,8,9,10,11,12,13,15,16,17,18,19,20)
 for(i in F) credit[,i]=as.factor(credit[,i])

 F=c(1,2,4,5,7,8,9,10,11,12,13,15,16,17,18,19,20)
 for(i in F) credit[,i]=as.factor(credit[,i])

 i_test=sample(1:nrow(credit),size=333)
 i_calibration=(1:nrow(credit))[-i_test]

 LogisticModel <- glm(Creditability ~ Account.Balance + Payment.Status.of.Previous.Credit + Purpose + 
Length.of.current.employment + 
Sex...Marital.Status, family=binomial, 
data = credit[i_calibration,])

 fitLog <- predict(LogisticModel,type="response",
                  newdata=credit[i_test,])
 library(ROCR)

## Warning: package 'ROCR' was built under R version 4.1.1

 pred = prediction( fitLog, credit$Creditability[i_test])
 perf <- performance(pred, "tpr", "fpr")
 plot(perf)

 AUCLog1=performance(pred, measure = "auc")@y.values[[1]]
 cat("AUC: ",AUCLog1,"n")

## AUC:  0.7105969 n

 fitLog <- predict(LogisticModel,type="response",
                   newdata=credit[i_test,])
 library(ROCR)
 pred = prediction( fitLog, credit$Creditability[i_test])
 perf <- performance(pred, "tpr", "fpr")
 plot(perf)

 AUCLog1=performance(pred, measure = "auc")@y.values[[1]]
 cat("AUC: ",AUCLog1,"n")

## AUC:  0.7105969 n

 LogisticModel <- glm(Creditability ~ ., 
 family=binomial, 
  data = credit[i_calibration,])

 library(rpart)
 ArbreModel <- rpart(Creditability ~ ., 
  data = credit[i_calibration,])

 library(rpart.plot)
 prp(ArbreModel,type=2,extra=1)

 fitArbre <- predict(ArbreModel,
                     newdata=credit[i_test,],
                     type="prob")[,2]
 pred = prediction( fitArbre, credit$Creditability[i_test])
 perf <- performance(pred, "tpr", "fpr")
 plot(perf)

 AUCArbre=performance(pred, measure = "auc")@y.values[[1]]
 cat("AUC: ",AUCArbre,"n")

## AUC:  0.708597 n

 library(randomForest)

## randomForest 4.6-14

## Type rfNews() to see new features/changes/bug fixes.

 RF <- randomForest(Creditability ~ .,
 data = credit[i_calibration,])
 fitForet <- predict(RF,
                     newdata=credit[i_test,],
                     type="prob")[,2]
 pred = prediction( fitForet, credit$Creditability[i_test])
 perf <- performance(pred, "tpr", "fpr")
 plot(perf)

 AUCRF=performance(pred, measure = "auc")@y.values[[1]]
 cat("AUC: ",AUCRF,"n")

## AUC:  0.7825905 n

 AUC=function(i){
   set.seed(i)
   i_test=sample(1:nrow(credit),size=333)
   i_calibration=(1:nrow(credit))[-i_test]
   LogisticModel <- glm(Creditability ~ ., 
    family=binomial, 
    data = credit[i_calibration,])
   summary(LogisticModel)
   fitLog <- predict(LogisticModel,type="response",
                     newdata=credit[i_test,])
   library(ROCR)
   pred = prediction( fitLog, credit$Creditability[i_test])
   AUCLog2=performance(pred, measure = "auc")@y.values[[1]] 
   RF <- randomForest(Creditability ~ .,
   data = credit[i_calibration,])
   fitForet <- predict(RF,
                       newdata=credit[i_test,],
                       type="prob")[,2]
   pred = prediction( fitForet, credit$Creditability[i_test])
   AUCRF=performance(pred, measure = "auc")@y.values[[1]]
   return(c(AUCLog2,AUCRF))
 }
 A=Vectorize(AUC)(1:200)
 plot(t(A))