nano-bot01/Support-Vector-Machine

Support Vector Machine functioning

Support-Vector-Machine

Support Vector Machine implementation on IRIS Dataset

Dependencies

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets

Create an Instance of SVM and Fit out the data.

Data is not scaled so as to be able to plot the support vectors

svc = svm.SVC(kernel ='linear', C = 1).fit(X, y)

create a mesh to plot

x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
h = (x_max / x_min)/100
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
		np.arange(y_min, y_max, h))

Plot the data for Proper Visual Representation

plt.figure(figsize=(16,9))
plt.subplot(1, 1, 1)
Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap = plt.cm.Paired, alpha = 0.8)

plt.scatter(X[:, 0], X[:, 1], c = y, cmap = plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.title('SVC with linear kernel')

Output the Plot

plt.show()