Today, information is spread quickly throughout communities by means of simple messaging, group chats, and social media platforms. Because of the ease of use that these services provide, misinformation has become a common trend. The term ‘fake news’ has emerged as being a way to refer to all information shared in a manner that is meant to mislead a reader into thinking something is a true statement when it is not. Combating fake news has become a major topic, and many are attempting to find a way of detecting when something is real or made up. In this paper, we look at a database of news articles that have been classified as either real or fake and apply machine learning to automatically determine if something is deliberately misleading. Algorithms have been developed to make judgements, classify articles in a database and judge new articles based on learned knowledge. This model combines multiple factors that may raise or lower confidence in the article being legitimate or illegitimate and provides a single confidence metric. This paper presents the development of these algorithms for assessing articles. It discusses the efficacy of using this approach and compares it to other classification approaches. It then presents the results of using the system to classify numerous presented articles and discusses the sufficiency of system accuracy for multiple applications. Finally, it discusses next steps in the fake news detection project and how these algorithms fit within them.