(1) Use Python to generate a 2D (x; E R) linearly separable data set with 50 data points. The data set should have approximately half positive and half negative instances. Create a scatter plot to visualize the data set. Split the data set into training set (60%) and test set (40%). (2) Use Python (not sklearn package) to create the Batch Perceptron training algorithm and use the training data set in a.(1) to train a Perceptron model. Plot the error function curve when the training process converges. Create a plot that shows the training instances and the learnt decision boundary.