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Credit Risk Modeling in R

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$12

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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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

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Intermediate R for Finance

What You Will Learn

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This chapter begins with a general introduction to credit risk models

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

Course Instructors

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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...

Course Reviews

Average Rating Based on 3 reviews

5.0

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Course Cover

$12

$6

49% OFF

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