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Predict credit defaults with random forest using Python

IntermediateGuided Project

Build a predictive model using Python, pandas, and scikit-learn's random forest algorithm for financial risk management. This hands-on project covers data preprocessing, model fitting, and performance evaluation. Learn hyperparameter tuning to enhance model robustness. Perfect for data science enthusiasts and financial analysts, this 30-minute project transforms your data into actionable insights for predicting credit defaults, showcasing the real-world power of machine learning in banking.

4.6 (66 Reviews)

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 466

Skills You Will Learn

  • Random forest, Python, Data Science, Machine Learning, Pandas, Scikit-learn

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • December 14, 2025
About this Guided Project

Predict credit defaults with random forest using Python


In today's financial landscape, managing risk is more crucial than ever. Understanding and predicting credit defaults can save financial institutions millions of dollars and streamline decision-making processes. This guided project is designed to take you through the intricacies of financial risk management using advanced machine learning techniques. By constructing a predictive model with Python, pandas, and scikit-learn's random forest algorithm, you'll gain invaluable insights and skills. This hands-on experience not only deepens your understanding of data preprocessing, model fitting, and performance evaluation but also enhances your ability to implement hyperparameter tuning techniques. Perfect for data science enthusiasts and financial analysts, this 30-minute tutorial transforms your data into actionable insights, showcasing the real-world power of machine learning in the banking sector.


What you'll learn

After you complete the project, you will:

  • Master the fundamentals of financial risk management through predictive modeling.
  • Learn how to implement the random forest algorithm using Python and the scikit-learn library.
  • Develop skills in data preprocessing to ensure that your data is clean and suitable for analysis.
  • Gain practical experience in evaluating model performance.
  • Understand hyperparameter tuning techniques to enhance model robustness and reliability.

What you'll need

Before starting this guided project, you should have:

  • Basic knowledge of Python programming
  • Familiarity with pandas for data manipulation
  • An understanding of basic machine learning concepts

Instructors

Ricky Shi

Data Scientist at IBM

Ricky Shi is a Data Scientist at IBM, specializing in deep learning, computer vision, and Large Language Models. He applies advanced machine learning and generative AI techniques to solve complex challenges across various sectors. As an enthusiastic mentor, Ricky is committed to helping colleagues and peers master technical intricacies and drive innovation.

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Karina Kervin

Senior Data Scientist

Karina Kervin is a Senior Data Scientist at IBM, where she applies data science to managing IBM’s data. Karina enjoys the creative problem-solving process of finding the nuggets of insight in data and applying those insights to problems while considering the impact on the larger community. She has applied this focus to multiple areas, such as retail and human resources.

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Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

Wojciech "Victor" Fulmyk is a Data Scientist and AI Engineer on IBM’s Skills Network team, where he focuses on helping learners build expertise in data science, artificial intelligence, and machine learning. He is also a Kaggle competition expert, currently ranked in the top 3% globally among competition participants. An economist by training, he applies his knowledge of statistics and econometrics to bring a distinctive perspective to AI and ML—one that considers both technical depth and broader socioeconomic implications.

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Kang Wang

Data Scientist

I was a Data Scientist in the IBM. I also hold a PhD from the University of Waterloo.

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