Support Vector Machine Example - Machine Learning

This is code to practice concepts of Support Vector Machines

Overview

This code is implementing a Support Vector Machine with Hinge loss and gradient descent to find the optimal decision boundary between two classes of data.

The svm.py file is extensively documented with information about Support Vector Machines and the math that goes into them.

This implementation is using a pre-defined, small sample of meaningless data. It was used to learn the concepts of SVMs.

Dependencies

• Python
• numpy
• matplotlib

Usage

Just run python3 svm.py to see the results:

Plot of the Rate of classification errors during training

Plot of the Hyperplane generated by the SVM

Credits

Credits for this project go to this video on Youtube by Siraj Raval that explains Support Vector Machines.