At a Glance

Name: Predict payment defaults using SVM with Python
Price: Free CAD
Rating: 4.7 (9 reviews)
Author: lucy_xu, fateme_akbari, kang_wang

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.

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.

Offered By: IBMSkillsNetwork

Predict payment defaults using SVM with Python

At a Glance

A look at the project ahead

What you'll need