1. Import the following packages:

from sklearn.naive_bayes import GaussianNB
import numpy as np
import matplotlib.pyplot as plt

2. Use the following data file, which includes comma-separated arithmetical data:

in_file = 'data_multivar.txt'
a = []
b = []
with open(in_file, 'r') as f:
  for line in f.readlines():
    data = [float(x) for x in line.split(',')]
    a.append(data[:-1])
    b.append(data[-1])
a = np.array(a)
b = np.array(b)

3. Construct a Naive Bayes classifier:

classification_gaussiannb = GaussianNB()
classification_gaussiannb.fit(a, b)
b_pred = classification_gaussiannb.predict(a)

4. Calculate the accuracy of Naive Bayes:

correctness = 100.0 * (b == b_pred).sum() / a.shape[0]
print "correctness of the classification =", round(correctness, 2), "%"

5. Plot the classifier result:

def plot_classification(classification_gaussiannb, a , b):
  a_min, a_max = min(a[:, 0]) - 1.0, max(a[:, 0]) + 1.0
  b_min, b_max = min(a[:, 1]) - 1.0, max(a[:, 1]) + 1.0
  step_size = 0.01
  a_values, b_values = np.meshgrid(np.arange(a_min, a_max,   step_size), np.arange(b_min, b_max, step_size))
  mesh_output1 = classification_gaussiannb.predict(np.c_[a_values.ravel(), b_values.ravel()])
  mesh_output2 = mesh_output1.reshape(a_values.shape)
  plt.figure()
  plt.pcolormesh(a_values, b_values, mesh_output2, cmap=plt.cm.gray)
  plt.scatter(a[:, 0], a[:, 1], c=b , s=80, edgecolors='black', linewidth=1,cmap=plt.cm.Paired)

6. Specify the boundaries of the figure:

plt.xlim(a_values.min(), a_values.max())
plt.ylim(b_values.min(), b_values.max())
# specify the ticks on the X and Y axes
plt.xticks((np.arange(int(min(a[:, 0])-1), int(max(a[:, 0])+1), 1.0)))
plt.yticks((np.arange(int(min(a[:, 1])-1), int(max(a[:, 1])+1), 1.0)))
plt.show()
plot_classification(classification_gaussiannb, a, b)

The accuracy obtained after executing a Naive Bayes classifier is shown in the following screenshot: