This course is part of these tracks:
Lore Dirick
Data Scientist at DataCamp
Lore is a data scientist at DataCamp. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. At DataCamp, she is in charge of building out the Applied Finance curriculum.
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Course Description
This hands-on-course with real-life credit data will teach you how to model credit risk by using logistic regression and decision trees in R.
Modeling credit risk for both personal and company loans is of major importance for banks. The probability that a debtor will default is a key component in getting to a measure for credit risk. While introducing other models in this course as well, we focus on two model types that are often used in the credit scoring context; logistic regression and decision trees. We will teach you how to use them in this particular context, and how these models are evaluated by banks.
If you'd like to continue exploring after the course, the data set used at the start of the course can be downloaded here. The preprocessed data set from chapter 2 onwards can be found here.
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1
Introduction and data preprocessing
FreeThis 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.
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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.
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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.
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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.
