Credit Risk Modeling in R
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Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Course Description
What You’ll Learn
Introduction and data preprocessing
This chapter begins with a general introduction to credit risk models. We’ll explore a real-life data set, then preprocess the data set such that it’s in the appropriate format before applying the credit risk models.
Decision trees
Classification trees are another popular method in the world of credit risk modeling. In this chapter, you will learn how to build classification trees using credit data in R.
Logistic regression
Logistic regression is still a widely used method in credit risk modeling. In this chapter, you will learn how to apply logistic regression models on credit data in R.
Evaluating a credit risk model
In this chapter, you’ll learn how you can evaluate and compare the results obtained through several credit risk models.
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