Skip to main content

Credit Risk Modeling in Python

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

Start Course for Free
4 Hours15 Videos57 Exercises11,751 Learners4850 XPApplied Finance Track

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

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


    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.

    Play Chapter Now
    Understanding credit risk
    50 xp
    Explore the credit data
    100 xp
    Crosstab and pivot tables
    100 xp
    Outliers in credit data
    50 xp
    Finding outliers with cross tables
    100 xp
    Visualizing credit outliers
    100 xp
    Risk with missing data in loan data
    50 xp
    Replacing missing credit data
    100 xp
    Removing missing data
    100 xp
    Missing data intuition
    50 xp
  2. 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.

    Play Chapter Now

In the following tracks

Applied Finance


ruannevdwaltRuanne Van Der Waltmona-kayMona Khalil
Michael Crabtree Headshot

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.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA