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पाठ्यक्रम

Credit Risk Modeling in R

मध्यमकौशल स्तर

अपडेट किया गया 11/2023

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

मुफ़्त में पाठ्यक्रम शुरू करें

RApplied Finance4 घंटे16 वीडियो52 अभ्यास4,000 XP48,288उपलब्धि का प्रमाण पत्र

अपना निःशुल्क खाता बनाएँ

या

जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।

हजारों कंपनियों के शिक्षार्थियों द्वारा पसंद किया गया

2 या अधिक लोगों को प्रशिक्षण दे रहे हैं?

DataCamp for Business आज़माएं

पाठ्यक्रम विवरण

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.

पूर्व आवश्यकताएं

Intermediate R for Finance

1

Introduction and data preprocessing

This 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.

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

अध्याय शुरू करें

2

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.

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

अध्याय शुरू करें

3

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.

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

अध्याय शुरू करें

4

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.

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

अध्याय शुरू करें

Credit Risk Modeling in R

पाठ्यक्रम
पूर्ण

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व्यवसाय के लिए

2 या अधिक लोगों को प्रशिक्षण दे रहे हैं?

अपनी टीम को सभी सुविधाओं सहित पूर्ण DataCamp प्लेटफॉर्म तक पहुंच प्रदान करें।

निम्नलिखित ट्रैक में

Applied Finance in R

Quantitative Analyst in R

प्रशिक्षक

Lore Dirick

Director of Data Science Education at Flatiron School

पाठ्यक्रम संसाधन

Loan Data Chapter 1डेटासेट

Loan Data Chapter 2, 3 and 4डेटासेट

19 मिलियन से अधिक शिक्षार्थियों के साथ जुड़ें और आज ही Credit Risk Modeling in R शुरू करें!

अपना निःशुल्क खाता बनाएँ

या

जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।

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