Bank Note Fraud Detection with SVMs in Python with Scikit-Learn

David Landup
Cássia Sampaio


Can you tell the difference between a real and a fraud bank note? Probably!
Can you do it for 1000 bank notes? Probably! But it takes time.

This doesn't have to be a computer vision problem - in this Guided Project, you'll learn the intuition and theory behind Support Vector Machines (SVMs) and use them on a tabular dataset to determine whether a bank note is fraudulent or not. We'll be using Python, Scikit-Learn, Pandas and Seaborn, building from exploratory data analysis to training and evaluating a model.

What is a Guided Project?

Turn Theory Into Practice

All great learning resources, books and courses teach you the holistic basics, or even intermediate concepts, and advise you to practice after that. As soon as you boot up your own project - the environment suddenly isn't as pristine as in the courses and books! Things go wrong, and it's oftentimes hard to pinpoint even why they do go wrong.

StackAbuse Guided Projects are there to bridge the gap between theory and actual work. We'll respect your knowledge and intelligence, and assume you know the theory. Time to put it into practice.

When applicable, Guided Projects come with downloadable, reusable scripts that you can refer back to whenever required in your new day-to-day work.

Last Updated: Dec 2022

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