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Credit Risk Modeling in R

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  • 16 Videos
  • 52 Exercises
  • 4 hours 
  • 17,564 Participants
  • 4000 XP


Lore Dirick
Lore Dirick

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.

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.