Credit Risk Modeling in R
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
- 4 hours
- 16 Videos
- 52 Exercises
- 27,870 Participants
- 4,000 XP
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Lore Dirick
Senior Data Science Curriculum Writer 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 now a senior Data Science Curriculum Writer at Flatiron School, a coding bootcamp in NYC.
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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.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Course Outline
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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.
Course Instructor
Lore Dirick
Senior Data Science Curriculum Writer 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 now a senior Data Science Curriculum Writer at Flatiron School, a coding bootcamp in NYC.
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