diff --git a/Chapter 02/code/naive_bayes.py b/Chapter 02/code/naive_bayes.py
index 3268e49..58eced1 100644
--- a/Chapter 02/code/naive_bayes.py	
+++ b/Chapter 02/code/naive_bayes.py	
@@ -1,7 +1,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.naive_bayes import GaussianNB 
-from sklearn import cross_validation
+from sklearn import model_selection
 
 from utilities import visualize_classifier
 
@@ -32,7 +32,7 @@
 # Cross validation 
 
 # Split data into training and test data 
-X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2, random_state=3)
+X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=3)
 classifier_new = GaussianNB()
 classifier_new.fit(X_train, y_train)
 y_test_pred = classifier_new.predict(X_test)
@@ -48,19 +48,19 @@
 # Scoring functions
 
 num_folds = 3
-accuracy_values = cross_validation.cross_val_score(classifier, 
+accuracy_values = model_selection.cross_val_score(classifier, 
         X, y, scoring='accuracy', cv=num_folds)
 print("Accuracy: " + str(round(100*accuracy_values.mean(), 2)) + "%")
 
-precision_values = cross_validation.cross_val_score(classifier, 
+precision_values = model_selection.cross_val_score(classifier, 
         X, y, scoring='precision_weighted', cv=num_folds)
 print("Precision: " + str(round(100*precision_values.mean(), 2)) + "%")
 
-recall_values = cross_validation.cross_val_score(classifier, 
+recall_values = model_selection.cross_val_score(classifier, 
         X, y, scoring='recall_weighted', cv=num_folds)
 print("Recall: " + str(round(100*recall_values.mean(), 2)) + "%")
 
-f1_values = cross_validation.cross_val_score(classifier, 
+f1_values = model_selection.cross_val_score(classifier, 
         X, y, scoring='f1_weighted', cv=num_folds)
 print("F1: " + str(round(100*f1_values.mean(), 2)) + "%")