Interactive Course

Credit Risk Modeling in Python

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

  • 4 hours
  • 15 Videos
  • 57 Exercises
  • 1,603 Participants
  • 4,850 XP

Loved by learners at thousands of top companies:

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Course Description

If you've ever applied for a credit card or loan, you know that financial firms process your information before making a decision. This is because giving you a loan can have a serious financial impact on their business. But how do they make a decision? In this course, you will learn how to prepare credit application data. After that, you will apply machine learning and business rules to reduce risk and ensure profitability. You will use two data sets that emulate real credit applications while focusing on business value. Join me and learn the expected value of credit risk modeling!

  1. 1

    Exploring and Preparing Loan Data

    Free

    In this first chapter, we will discuss the concept of credit risk and define how it is calculated. Using cross tables and plots, we will explore a real-world data set. Before applying machine learning, we will process this data by finding and resolving problems.

  2. Gradient Boosted Trees Using XGBoost

    Decision trees are another standard credit risk model. We will go beyond decision trees by using the trendy XGBoost package in Python to create gradient boosted trees. After developing sophisticated models, we will stress test their performance and discuss column selection in unbalanced data.

  3. Logistic Regression for Defaults

    With the loan data fully prepared, we will discuss the logistic regression model which is a standard in risk modeling. We will understand the components of this model as well as how to score its performance. Once we've created predictions, we can explore the financial impact of utilizing this model.

  4. Model Evaluation and Implementation

    After developing and testing two powerful machine learning models, we use key performance metrics to compare them. Using advanced model selection techniques specifically for financial modeling, we will select one model. With that model, we will: develop a business strategy, estimate portfolio value, and minimize expected loss.

  1. 1

    Exploring and Preparing Loan Data

    Free

    In this first chapter, we will discuss the concept of credit risk and define how it is calculated. Using cross tables and plots, we will explore a real-world data set. Before applying machine learning, we will process this data by finding and resolving problems.

  2. Logistic Regression for Defaults

    With the loan data fully prepared, we will discuss the logistic regression model which is a standard in risk modeling. We will understand the components of this model as well as how to score its performance. Once we've created predictions, we can explore the financial impact of utilizing this model.

  3. Gradient Boosted Trees Using XGBoost

    Decision trees are another standard credit risk model. We will go beyond decision trees by using the trendy XGBoost package in Python to create gradient boosted trees. After developing sophisticated models, we will stress test their performance and discuss column selection in unbalanced data.

  4. Model Evaluation and Implementation

    After developing and testing two powerful machine learning models, we use key performance metrics to compare them. Using advanced model selection techniques specifically for financial modeling, we will select one model. With that model, we will: develop a business strategy, estimate portfolio value, and minimize expected loss.

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Lloyd's Banking Group

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Harvard Business School

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Decision Science Analytics @ USAA

Michael Crabtree
Michael Crabtree

Data Scientist

Michael is a cross-functional data scientist and big data engineer at Ford. He has created several high-value analytical and data products spanning domains such as manufacturing, purchasing, finance, product development, and marketing. Since graduating from the University of Louisville College of Business, he has completed over 65 online learning classes across five educational platforms. He is often found spreading fun and science throughout the offices at Ford.

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