Crimes result in property damage, injuries, and even loss of life. Being able to predict when and where crimes will occur would mitigate damage and improve community sentiment. By using historical crime data, the goal of this project is to build machine learning models and compare their accuracy then draft a recommendation that presents the best model to implement and steps to decrease crime activity. This project uses crime activity data collected in Chicago, IL as a case study.