Machine Learning for Homicide Crime Prediction Exploring Patterns in Models and Applications

Abstract

Homicide crime prediction is a critical area of focus for law enforcement agencies aiming to enhance public safety and efficiently allocate resources. This study develops and evaluates several machine learning models to predict homicide crimes using historical data from 2007 to 2022. The models assessed include Random Forest, Decision Tree, Logistic Regression, and Linear Regression. Performance evaluation reveals that the Decision Tree model achieved the highest accuracy at 99%, with a precision of 0.99, recall of 0.98, and F1 score of 0.99. Logistic Regression also performed well, with an accuracy of 95%, precision of 0.94, recall of 0.96, and F1 score of 0.95. These results demonstrate the potential of machine learning models in predicting homicide crimes. A web application was developed to make these predictions accessible and actionable for law enforcement agencies, aiding in resource allocation and crime prevention strategies.