Loans are not always repaid in full, and there are defaulters. In this case, it becomes important for the bank to identify potential defaulters based on the available information. Here, we adapt the GC dataset from the RSADBE package to properly reflect the labels of the factor variable. The transformed dataset is available as GC2.RData in the data folder. The GC dataset itself is mainly an adaptation of the version available at https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data). Here, we have 1,000 observations, and 20 covariate/independent variables such as the status of existing checking account, duration, and so forth. The final status of whether the loan was completely paid or not is available in the good_bad column. We will partition the data into training and testing parts, and create the formula too:

> library(RSADBE)
> load("../Data/GC2.RData")
> table(GC2$good_bad)
 bad good 
 300  700 
> set.seed(12345)
> Train_Test <- sample(c("Train","Test"),nrow(GC2),replace = TRUE,prob=c(0.7,0.3))
> head(Train_Test)
[1] "Test"  "Test"  "Test"  "Test"  "Train" "Train"
> GC2_Train <- GC2[Train_Test=="Train",]
> GC2_TestX <- within(GC2[Train_Test=="Test",],rm(good_bad))
> GC2_TestY <- GC2[Train_Test=="Test","good_bad"]
> GC2_Formula <- as.formula("good_bad~.")