Develop a “Credit Risk Scoring System” with R
First of all, you need to decide how you evaluate "good" vs "bad", which means picking a metric. From the top of my head you have three choices:


accuracy: simplest, but potentially misleading (if you have few defaults and predict all 0s, you still get high accuracy)
logloss: penalize assigning a high def probability to good quality credit and vice versa
ROC: sort the credit from best to worst.


Once you've made your pick, make sure your model can generate predictions in the right format (i.e. output probabilities and not just 0/1) and the go through the training / validation / test paradigm. This should get you started.