Abstract

Crime hotspot locations identification is a very important endeavor to help ensure public safety. Been able to effectively identify these locations will help provide useful information to law enforcement bodies to help minimize criminal activities. This proposed work will analyze a five-year crime data from the Cincinnati Police Department using clustering algorithms such K-means, DBSCAN, Hierarchical algorithms, and classification machine learning algorithms such as Random Forest, SVM, Logistic Regression, KNN, and Naïve Bayes, on the same dataset. The results from both these two approaches will be compared to determine which approach provides the best result.