1. After installing the essential packages, let's construct some training labels:

import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
a = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
b = np.array([1, 1, 1, 2, 2, 2])

2. Initiate the classifier:

classification = linear_model.LogisticRegression(solver='liblinear', C=100)
classification.fit(a, b)

3. Sketch datapoints and margins:

def plot_classification(classification, 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.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)

  # 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, a, b)

The command to execute logistic regression is shown in the following screenshot: