diff --git a/machine_learning/decision_tree.py b/machine_learning/decision_tree.py
index 2ff53d89f..a6cbe1535 100644
--- a/machine_learning/decision_tree.py
+++ b/machine_learning/decision_tree.py
@@ -6,6 +6,7 @@ Output: The decision tree maps a real number input to a real number output.
 import numpy as np
 import doctest
 
+
 class DecisionTree:
     def __init__(self, depth=5, min_leaf_size=5):
         self.depth = depth
@@ -151,16 +152,17 @@ class TestDecisionTree:
 
         return float(squared_error_sum / labels.size)
 
+
 def main():
     """
-    In this demonstration first we are generating x which is a numpy array containing values starting 
-    from -1 to 1 with an interval of 0.005 i.e [-1,-0.995,....,0.995,1] this is what we are 
-    getting by applying arange function of numpy.Then the we are generating y by applying sin function 
-    on x which is an array containing values from -1 to 1 with difference of 0.005 i.e we are getting 
-    an array y which contains sin of each value of x. We then train a decision tree on the data set 
-    and use the decision tree to predict the label of 10 different test values. Here we should prefer 
-    calculating Root Mean Squared Error over Mean Sqaured error beacause RMSE should be used 
-    when you need to communicate your results in an understandable  way to end users or when 
+    In this demonstration first we are generating x which is a numpy array containing values starting
+    from -1 to 1 with an interval of 0.005 i.e [-1,-0.995,....,0.995,1] this is what we are
+    getting by applying arange function of numpy.Then the we are generating y by applying sin function
+    on x which is an array containing values from -1 to 1 with difference of 0.005 i.e we are getting
+    an array y which contains sin of each value of x. We then train a decision tree on the data set
+    and use the decision tree to predict the label of 10 different test values. Here we should prefer
+    calculating Root Mean Squared Error over Mean Sqaured error beacause RMSE should be used
+    when you need to communicate your results in an understandable  way to end users or when
     penalising outliers is less of a priority.Interpretation will be easy in this case.
     You can check out these https://stephenallwright.com/rmse-vs-mse/ to know the reason for this.
     """
@@ -169,13 +171,13 @@ def main():
 
     tree = DecisionTree(depth=10, min_leaf_size=10)
     tree.train(x, y)
-    
+
     test_cases = (np.random.rand(10) * 2) - 1
     predictions = np.array([tree.predict(x) for x in test_cases])
     mse = mean_squared_error(y_true, y_pred)
-    
+
     mse_error = np.mean((predictions - test_cases) ** 2)
-    
+
     """RMSE error"""
     avg_error = np.sqrt(avg_error)
 
@@ -186,5 +188,5 @@ def main():
 
 if __name__ == "__main__":
     main()
-    
+
     doctest.testmod(name="mean_squarred_error", verbose=True)