diff --git a/machine_learning/logistic_regression.py b/machine_learning/logistic_regression.py
index de0cfd54d..71952e792 100644
--- a/machine_learning/logistic_regression.py
+++ b/machine_learning/logistic_regression.py
@@ -1,98 +1,101 @@
-#!/usr/bin/env python
-# coding: utf-8
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
 
-# # Logistic Regression from scratch
+## Logistic Regression from scratch
 
 # In[62]:
 
+# In[63]:
+
+# importing all the required libraries
 
 ''' Implementing logistic regression for classification problem 
      Helpful resources : 1.Coursera ML course    2.https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac'''
 
-
-# In[63]:
-
-
-#importing all the required libraries
 import numpy as np
 import matplotlib.pyplot as plt
-get_ipython().run_line_magic('matplotlib', 'inline')
+
+# get_ipython().run_line_magic('matplotlib', 'inline')
+
 from sklearn import datasets
 
 
 # In[67]:
 
+# sigmoid function or logistic function is used as a hypothesis function in classification problems
 
-#sigmoid function or logistic function is used as a hypothesis function in classification problems
 def sigmoid_function(z):
-    return 1/(1+np.exp(-z))
+    return 1 / (1 + np.exp(-z))
 
 
-def cost_function(h,y):
-    return (-y*np.log(h)-(1-y)*np.log(1-h)).mean()
+def cost_function(h, y):
+    return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
+
 
 # here alpha is the learning rate, X is the feature matrix,y is the target matrix
-def logistic_reg(alpha,X,y,max_iterations=70000):
-    converged=False
-    iterations=0
-    theta=np.zeros(X.shape[1])
-    
-    
+
+def logistic_reg(
+    alpha,
+    X,
+    y,
+    max_iterations=70000,
+    ):
+    converged = False
+    iterations = 0
+    theta = np.zeros(X.shape[1])
+
     while not converged:
-        z=np.dot(X,theta)
-        h=sigmoid_function(z)
-        gradient = np.dot(X.T,(h-y))/y.size
-        theta=theta-(alpha)*gradient
-        
-        z=np.dot(X,theta)
-        h=sigmoid_function(z)
-        J=cost_function(h,y)
-       
-     
-        
-        iterations+=1   #update iterations
-        
-        
-        if iterations== max_iterations:
-            print("Maximum iterations exceeded!")
-            print("Minimal cost function J=",J)
-            converged=True
-            
+        z = np.dot(X, theta)
+        h = sigmoid_function(z)
+        gradient = np.dot(X.T, h - y) / y.size
+        theta = theta - alpha * gradient
+
+        z = np.dot(X, theta)
+        h = sigmoid_function(z)
+        J = cost_function(h, y)
+
+        iterations += 1  # update iterations
+
+        if iterations == max_iterations:
+            print ('Maximum iterations exceeded!')
+            print ('Minimal cost function J=', J)
+            converged = True
+
     return theta
 
 
-
-        
-    
-    
-
-
 # In[68]:
 
-
-if __name__=='__main__':
-    iris=datasets.load_iris()
+if __name__ == '__main__':
+    iris = datasets.load_iris()
     X = iris.data[:, :2]
     y = (iris.target != 0) * 1
-    
-    alpha=0.1
-    theta=logistic_reg(alpha,X,y,max_iterations=70000)
-    print(theta)
+
+    alpha = 0.1
+    theta = logistic_reg(alpha, X, y, max_iterations=70000)
+    print (theta)
+
+
     def predict_prob(X):
-        return sigmoid_function(np.dot(X,theta))              # predicting the value of probability from the logistic regression algorithm
-        
-    
+        return sigmoid_function(np.dot(X, theta))  # predicting the value of probability from the logistic regression algorithm
+
+
     plt.figure(figsize=(10, 6))
     plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='b', label='0')
     plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='r', label='1')
-    x1_min, x1_max = X[:,0].min(), X[:,0].max(),
-    x2_min, x2_max = X[:,1].min(), X[:,1].max(),
-    xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
+    (x1_min, x1_max) = (X[:, 0].min(), X[:, 0].max())
+    (x2_min, x2_max) = (X[:, 1].min(), X[:, 1].max())
+    (xx1, xx2) = np.meshgrid(np.linspace(x1_min, x1_max),
+                             np.linspace(x2_min, x2_max))
     grid = np.c_[xx1.ravel(), xx2.ravel()]
     probs = predict_prob(grid).reshape(xx1.shape)
-    plt.contour(xx1, xx2, probs, [0.5], linewidths=1, colors='black');
-
-    plt.legend();
-    
-    
+    plt.contour(
+        xx1,
+        xx2,
+        probs,
+        [0.5],
+        linewidths=1,
+        colors='black',
+        )
 
+    plt.legend()