A Study on Smart Machine Learning (ML) Tools for Crime Detection and Prediction

• Published in: 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) pp. 1747 - 1752
• Main Authors: Singh, Shweta, Bhargava, Deepshikha, Singh, Pooja
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.05.2024
• Subjects: Crime; Criminal Prediction and Analysis; data mining; Juvenile delinquency; Law enforcement; Machine learning; Machine learning algorithms; Prevention and mitigation; Reviews; Surveys
• DOI: 10.1109/IC3SE62002.2024.10593591

Crime, a grave menace to society, not only disrupts the lives of individuals, whether they are victims or perpetrators but also reverberates throughout families and communities. Presently, law enforcement agencies face the daunting task of thwarting criminal activities. This study delves into various machine learning algorithms employed in the detection and prevention of crime. This research paper surveys recent literature examining the application of various Machine Learning algorithms in forecasting and mitigating future criminal activities. It investigates the computational challenges and opportunities presented by historical trends in predicting crime-the study endeavours to analyse and discuss the predominant parameters explored across multiple studies. Through a review of past literature, it becomes apparent that while numerous parameters have been utilized, the primary focus remains on crime patterns concerning time and crime hotspots. This paper conducts a comprehensive inquiry into Machine Learning techniques for uncovering and forecasting future crimes. It reveals that researchers predominantly rely on supervised ML approaches in their investigations, largely due to their superior accuracy.