Visualize the Working of the Support Vector Machine

# required imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from ipywidgets import interact

# from sklearn.datasets.samples_generator import make_blobs # older versions
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=50, centers=2,random_state=121,cluster_std=0.90) 
# try different values of cluster_std e.g. 0.60, 0.70, 0.80 ...etc and 
# observe the margins in the interactive plots 
plt.figure(figsize=(16,8))
ax = plt.gca()
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlabel('X1')
ax.set_ylabel('X2')
# plotting data as a scatter plot
plt.scatter(X[:, 0], X[:, 1], c=y, s=100, cmap=plt.cm.Set1, edgecolors='black', linewidth=2);