Crime data analysis using machine learning techniques has emerged as a critical area of research and application due to its potential to revolutionize crime prevention and law enforcement strategies. With the proliferation of data sources and advancements in machine learning algorithms, this study delves into the utilization of machine learning in crime data analysis. The paper provides a comprehensive overview of the existing landscape of crime data analysis, discussing traditional methods and the latest machine learning approaches. Furthermore, it sheds light on the integration of various data sources and discusses the challenges associated with ensuring data quality, privacy, and real-time analysis. By exploring the potential applications of clustering, classification, anomaly detection, and prediction techniques, this paper illustrates how machine learning algorithms can aid in hotspot identification, crime pattern recognition, and proactive law enforcement. Finally, it discusses the future prospects of integrating multimodal data and real-time analysis for a more holistic and timely understanding of criminal activities. The insights presented in this paper aim to encourage further research in the field and promote the effective use of machine learning in combating crime and enhancing public safety.

Crime data analysis, machine learning, predictive modeling, anomaly detection, law enforcement.