For markets and society to function, individuals and companies need access to credit.
Credit scoring algorithms, which make a guess at the probability of default (when the client will is not to pay back the credit), are the method banks use to determine whether or not a loan should be granted.
So, in order to determine this, we have a dataset composed of 10 explanatory variables (or columns) and 1 predictor variable which is SeriousDlqin2yrs. This predictor is a binary variable representing whether a person will experience financial distress, and consequently, will not able to pay back the credit.
Thus, our goal will be to predict the predictor variable using various machine learning algorithms implemented in R.