This repo contains the code base for the Perceptron Learning Algorithm.

The problem details can be found here.

The algorithm is very sensitive to the random seed selected.

The weights that should give testing_accuracy = 100% are w = [-149.0,2.0,2.0]. The corresponding decision bounday is:

I set the random seed to 0 and obtained the following results:

• learned weights = [-222.0,2.0,4.0]
• training accuracy = 100%
• testing accuracy = 80%

Below, a 2D visualization of the learned decision boundary and test data:

I used two libraries: numpy and matplotlib for visualization.