Perceptron and its implementation in Python

The dataset that we consider for implementing Perceptron is the Iris flower dataset. This dataset contains 4 features that describe the flower and classify them as belonging to one of the 3 classes. We strip the last 50 rows of the dataset that belongs to the class'Iris-virginica' and use only 2 classes'Iris-setosa' and'Iris-versicolor' because these classes are linearly separable and the algorithm converges to a local minimum by eventually finding the optimal weights. Visualizing the dataset with 2 of the features, we can see that that dataset can be clearly separated by drawing a straight line between them. Our goal is to write an algorithm that finds that line and classifies all of these data points correctly.