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Predict payment defaults using SVM with Python

BeginnerGuided Project

Explore Support Vector Machines (SVMs) with Python, a popular algorithm in classification tasks, with an application of machine learning in predictive modelling. Using a robust dataset featuring critical client attributes, we will predict whether or not a client will default on their payment the following month. Through hands-on exercises, learn how to classify data with SVMs, optimize your model with hyperparameter tuning, and reduce data dimensionality.

4.7 (12 Reviews)

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 107

Skills You Will Learn

  • Machine Learning, Python, sklearn, SVM

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • March 17, 2026
About this Guided Project
In this guided project, you will use SVM to predict client loan defaults given multiple attributes such as age, education, credit limit, and more. By learning SVM, you can acquire fundamental machine learning concepts and practical skills for data analysis. It's a valuable asset for problem-solving in real-world scenarios.

This hands-on project is based on the Classifying data using the SVM algorithm using Python tutorial. The guided project format combines the instructions of the tutorial with the environment to execute these instructions without the need to download, install, and configure tools.

A look at the project ahead

By completing this project, you will be able to:
  • Classify data using Support Vector Machines (SVMs) 
  • Optimize model with hyperparameter tuning
  • Reduce dimensionality with Principal Component Analysis

What you'll need

  • No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
  • Basic understanding of Python: Some basic understanding of Python will be beneficial.
  • Some understanding of statistical concepts: It's helpful to have some understanding of statistic concepts, particularly Linear Algebra and Classification.