diff --git a/Part 3 - Classification/Section 15 - K-Nearest Neighbors (K-NN)/K_Nearest_Neighbors/classification_template.py b/Part 3 - Classification/Section 15 - K-Nearest Neighbors (K-NN)/K_Nearest_Neighbors/classification_template.py
index 86bf97f..7ffd04a 100644
--- a/Part 3 - Classification/Section 15 - K-Nearest Neighbors (K-NN)/K_Nearest_Neighbors/classification_template.py	
+++ b/Part 3 - Classification/Section 15 - K-Nearest Neighbors (K-NN)/K_Nearest_Neighbors/classification_template.py	
@@ -1,16 +1,14 @@
-# Classification template
 
-# Importing the libraries
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 
-# Importing the dataset
+
 dataset = pd.read_csv('Social_Network_Ads.csv')
 X = dataset.iloc[:, [2, 3]].values
 y = dataset.iloc[:, 4].values
 
-# Splitting the dataset into the Training set and Test set
+
 from sklearn.cross_validation import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
 
@@ -19,14 +17,8 @@
 sc = StandardScaler()
 X_train = sc.fit_transform(X_train)
 X_test = sc.transform(X_test)
-
-# Fitting classifier to the Training set
-# Create your classifier here
-
-# Predicting the Test set results
 y_pred = classifier.predict(X_test)
 
-# Making the Confusion Matrix
 from sklearn.metrics import confusion_matrix
 cm = confusion_matrix(y_test, y_pred)
 
@@ -64,4 +56,4 @@
 plt.xlabel('Age')
 plt.ylabel('Estimated Salary')
 plt.legend()
-plt.show()
\ No newline at end of file
+plt.show()