Fake news has become an increasing problem due to the rising use of the Internet and social media. It is important to be able to distinguish sources of fake and misleading news articles to ensure that misinformation does not sow discord, erode trust in credible sources, and negatively impact our personal and societal well-being. Moreover, in an age where many people only skim headlines without delving into the full articles, the ability to discern fake news from headlines alone becomes even more crucial. To detect and classify fake news, we implement and compare five machine learning models–naive Bayes, logistic regression, decision tree, random forest, and support vector machine–on two different datasets: a benchmark dataset and a dataset with full articles and headlines. We utilize measures such as term frequency-inverse document frequency and sentiment scores, as predictors in our models. We find that naive Bayes consistently performs best on both datasets with accuracies of 64.40% and 92.56%, respectively.