1.4 Data visualization

In [5]:
plt.figure(figsize=(12,8))
plt.scatter(X_train[:,0],X_train[:,1], color = 'b', label = 'class X_train')
plt.scatter(X_test[:,0],X_test[:,1], color = 'r', label = 'class X_test')
plt.xlabel('feature1')
plt.ylabel('feature2')
plt.legend()
plt.axis('equal')
plt.show()

1.5 Checking the shape of the input data

In [6]:
print('x_train:\t{}' .format(X_train.shape))
print('y_train:\t{}' .format(y_train.shape))
print('x_test:\t\t{}'.format(X_test.shape))
print('y_test:\t\t{}'.format(y_test.shape))

x_train:	(1340, 2)
y_train:	(1340,)
x_test:		(660, 2)
y_test:		(660,)