Credit Risk Modeling in R

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

This course will teach you how to model credit risks using logistic regression and decision trees in R.

The role of banks is crucial in assessing credit risk for company and personal loans. The probability of a debtor defaulting is a key component in determining credit risk. Although you will learn many other models in this course, the only two that will be used for credit scoring are logistic regression and decision trees. These models will be discussed in the context of how banks evaluate them.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Intermediate R for Finance

What You Will Learn

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

In this chapter, you will learn how to apply logistic regression models on credit data in R

This chapter begins with a general introduction to credit risk models

Course Content

Expand all sections

Module 1: Introduction and data preprocessing

Module 2: Logistic regression

Module 3: Decision trees

Module 4: Evaluating a credit risk model

Course Instructors

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 workin...