diff --git a/Part 3 - Classification/Section 20 - Random Forest Classification/classification_template.py b/Part 3 - Classification/Section 20 - Random Forest Classification/classification_template.py
index 86bf97f..d1ae3ec 100644
--- a/Part 3 - Classification/Section 20 - Random Forest Classification/classification_template.py	
+++ b/Part 3 - Classification/Section 20 - Random Forest Classification/classification_template.py	
@@ -12,7 +12,7 @@
 
 # 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)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
 
 # Feature Scaling
 from sklearn.preprocessing import StandardScaler
@@ -33,21 +33,7 @@
 # Visualising the Training set results
 from matplotlib.colors import ListedColormap
 X_set, y_set = X_train, y_train
-X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
-                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
-plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
-             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
-plt.xlim(X1.min(), X1.max())
-plt.ylim(X2.min(), X2.max())
-for i, j in enumerate(np.unique(y_set)):
-    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
-                c = ListedColormap(('red', 'green'))(i), label = j)
-plt.title('Classifier (Training set)')
-plt.xlabel('Age')
-plt.ylabel('Estimated Salary')
-plt.legend()
-plt.show()
-
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X
 # Visualising the Test set results
 from matplotlib.colors import ListedColormap
 X_set, y_set = X_test, y_test
@@ -64,4 +50,4 @@
 plt.xlabel('Age')
 plt.ylabel('Estimated Salary')
 plt.legend()
-plt.show()
\ No newline at end of file
+plt.show()