Crime Type and Occurrence Prediction Using Machine Learning Algorithm

Abstract

Crime forecasting is essential for improving public safety and maximizing law enforcement resources. This paper describes a machine learning-based framework for forecasting both the type and incidence of crimes in cities. Using historical crime records, socio-economic conditions, and spatial patterns, we apply an array of machine learning algorithms, such as decision trees, random forests, and neural networks, to learn and forecast crime events. Our research explores the efficiency of feature selection methods to improve prediction accuracy and discovers influential predictors of crime occurrence. The model, as proposed, not only predicts crime types (e.g., theft, assault, burglary) but also calculates the probability of occurrence in specific locations at varied times. We validate the model's performance via comparative analysis against conventional statistical models, noting its promise in proactive crime prevention resource allocation, and policy-making. The findings indicate that machine learning can provide useful insights for short-term crime trend analysis as well as long-term urban planning.