Overview / Usage

Objective of this model is to develop a credit score based on data. The data are from the book “Credit Scoring and Its Applications” by Thomas, Edelman, and Crook. They are available in the attached files public.xls and publicdict.xls.

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Methodology / Approach

The variable BAD is a binary variable indicating bad credit. The task of credit scoring is to develop a score by which we can predict whether an individual will end up with bad credit. Use logistic regression along with Lasso and RIdge regression to build a model that ‘best’ predicts bad credit. In doing so we have built many models and compared and contrast them

Technologies Used

I have used Logistic regression (logit function) to predict a customer default probability. I have also used L1(Lasso regression) and L2(Ridge regression) to identify significant variable. There was significant multicolinearity was present in data so using L1 regression, I identified those variables and chart them out. I also used backward elimination to remove some variable keeping R-sequre in mind as to check model performance

Repository

https://github.com/gaurish123/Credit_Score_Modeling/tree/master