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Lore Dirick
Lore Dirick

Data Scientist

Lore is a data scientist with expertise in applied finance. 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.

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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 other models will be introduced in this course as well, you will learn about two model types that are often used in the credit scoring context; logistic regression and decision trees. You will learn how to use them in this particular context, and how these models are evaluated by banks.

  1. 1

    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.

  2. 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.

  3. 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.

  4. 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.