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
- 29,219 Participants
- 4,000 XP
Create Your Free Account
By continuing you accept the Terms of Use and Privacy Policy, that your data will be stored outside of the EU, and that you are 16 years or older.
Loved by learners at thousands of top companies:
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
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics @ USAA
Lore Dirick
Manager of Data Science Curriculum 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 the Manager of Data Science Curriculum at Flatiron School, a coding bootcamp in NYC.
See More