Predict payment defaults using SVM with Python
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Learn how to predict payment defaults using Support Vector Machines (SVM) in Python. Understand how to preprocess financial data, train SVM models, and assess their effectiveness in predicting customer behavior in the finance industry.
At a Glance
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.
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
- 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.
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