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Credit Risk Modeling in R

Intermédiaire

Updated 12/2024

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

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RFinance appliquée4 heures16 vidéos52 exercices4,000 XP47,069Déclaration de réalisation

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Essayer DataCamp for Business

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Description du cours

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.

Conditions préalables

Intermediate R for Finance

1

Introduction and data preprocessing

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Introduction and data structure

Exploring the credit data

Interpreting a CrossTable()

Histograms and outliers

Missing data and coarse classification

Deleting missing data

Replacing missing data

Keeping missing data

Data splitting and confusion matrices

Splitting the data set

Creating a confusion matrix

2

Logistic regression

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Logistic regression: introduction

Basic logistic regression

Interpreting the odds for a categorical variable

Multiple variables in a logistic regression model

Interpreting significance levels

Logistic regression: predicting the probability of default

Predicting the probability of default

Making more discriminative models

Evaluating the logistic regression model result

Specifying a cut-off

Comparing two cut-offs

Wrap-up and remarks

Comparing link functions for a given cut-off

3

Decision trees

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What is a decision tree?

Computing the gain for a tree

Changing one Gini...

Building decision trees using the rpart()-package

Undersampling the training set

Changing the prior probabilities

Including a loss matrix

Pruning the decision tree

Pruning the tree with changed prior probabilities

Pruning the tree with the loss matrix

Other tree options and the construction of confusion matrices

One final tree using more options

Confusion matrices and accuracy of our final trees

Optimizing the accuracy

4

Evaluating a credit risk model

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Finding the right cut-off: the strategy curve

Computing a bad rate given a fixed acceptance rate

The strategy table and strategy curve

To tree or not to tree?

The ROC-curve

ROC-curves for comparison of logistic regression models

ROC-curves for comparison of tree-based models

Input selection based on the AUC

Another round of pruning based on AUC

Best of four

Further model reduction?

Course wrap-up

Credit Risk Modeling in R

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