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Credit Risk Modeling in R

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

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4 Hours16 Videos52 Exercises42,972 Learners4000 XPApplied Finance TrackQuantitative Analyst Track

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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.

    Play Chapter Now
    Introduction and data structure
    50 xp
    Exploring the credit data
    100 xp
    Interpreting a CrossTable()
    50 xp
    Histograms and outliers
    50 xp
    100 xp
    100 xp
    Missing data and coarse classification
    50 xp
    Deleting missing data
    100 xp
    Replacing missing data
    100 xp
    Keeping missing data
    100 xp
    Data splitting and confusion matrices
    50 xp
    Splitting the data set
    100 xp
    Creating a confusion matrix
    100 xp

In the following tracks

Applied FinanceQuantitative Analyst
Lore Dirick Headshot

Lore Dirick

Director of Data Science Education at Flatiron School

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. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is and is now Director of Data Science Education at Flatiron School, a coding school with branches in 8 cities and online programs.
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