Crime Rate Prediction Framework using Machine Learning Algorithm: A Review

Abstract

The rapid growth of urban populations and the increasing complexity of criminal activities have intensified the need for intelligent and data-driven crime prediction systems. Traditional crime analysis methods, which rely primarily on statistical techniques and manual interpretation, often fail to capture complex patterns in large-scale and heterogeneous datasets. Machine learning (ML) algorithms offer a promising solution by enabling automated analysis, pattern discovery, and accurate prediction of crime rates. This review paper presents a comprehensive examination of machine learning–based frameworks for crime rate prediction. It systematically analyzes commonly used data sources, preprocessing techniques, feature engineering methods, and learning algorithms, including supervised, unsupervised, and deep learning models. Furthermore, performance evaluation metrics, existing challenges such as data imbalance, ethical concerns, and model interpretability, as well as emerging research trends, are discussed. The study aims to provide researchers and practitioners with a structured understanding of current crime prediction approaches and to highlight future directions for developing reliable, transparent, and effective crime rate prediction systems.