Machine Learning-Driven Crime Risk Modelling and Prediction

Abstract

Effective law enforcement and public safety strategies are essential for accurate crime analysis, and forecasting. This situation is a global challenge, especially in the region where diverse socioeconomic factors can influence complex crime patterns. This study presents a novel machine learning-based approach to address this critical issue. By leveraging publicly available historical crime data and relevant socio-demographic variables, we develop a predictive model to identify crimes in the area with high rates. The methodology involves data preprocessing, feature selection, model training, and validation using advanced machine learning techniques. Our proposed method attains a prediction accuracy of 0.52 over other competitive methods. These empirical results demonstrate the efficacy of the approach in forecasting crime hotspots, providing actionable insights for law enforcement agencies and policymakers. This research contributes to enhancing proactive measures for crime prevention and resource allocation in regions with diverse social economic factors.