Chapter 1. Credit Risk Modeling

All the chapters in this book are practical applications. We will develop one application per chapter. We will understand about the application, and choose the proper dataset in order to develop the application. After analyzing the dataset, we will build the base-line approach for the particular application. Later on, we will develop a revised approach that resolves the shortcomings of the baseline approach. Finally, we will see how we can develop the best possible solution using the appropriate optimization strategy for the given application. During this development process, we will learn necessary key concepts about Machine Learning techniques. I would recommend my reader run the code which is given in this book. That will help you understand concepts really well.

In this chapter, we will look at one of the many interesting applications of predictive analysis. I have selected the finance domain to begin with, and we are going to build an algorithm that can predict loan defaults. This is one of the most widely used predictive analysis applications in the finance domain. Here, we will look at how to develop an optimal solution for predicting loan defaults. We will cover all of the elements that will help us build this application.

We will cover the following topics in this chapter:

• Introducing the problem statement

• Understanding the dataset

  • Understanding attributes of the dataset

  • Data analysis

• Features engineering for the baseline model

• Selecting an ML algorithm

• Training the baseline model

• Understanding the testing matrix

• Testing the baseline model

• Problems with the existing approach

• How to optimize the existing approach

  • Understanding key concepts to optimize the approach

  • Hyperparameter tuning

• Implementing the revised approach

  • Testing the revised approach

  • Understanding the problem with the revised approach

• The best approach

• Implementing the best approach

• Summary