Credit Risk Modeling in R

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

4 Hours16 Videos52 Exercises42,989 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

Free

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.

Introduction and data structure
50 xp
Exploring the credit data
100 xp
Interpreting a CrossTable()
50 xp
Histograms and outliers
50 xp
Histograms
100 xp
Outliers
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
2. 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. 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. 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.

In the following tracks

Applied FinanceQuantitative Analyst

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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Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

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