Artificial intelligence and machine learning for crime data analysis

Artificial intelligence and machine learning for crime data analysis

Djidji Etienne Ahikpa

Parul Institute of Computer Application Parul University

Vadodara, Gujarat, India etienneahikpa@gmail.com

Vijya Tulsani Associate Professor,

Parul Institute of Computer Application Parul Univeristy,

Vadodara, Gujarat, India

Vijya.tulsani42087@paruluniversity.ac.in

Abstract— The classification of an act as a crime within the realm of criminal law is contingent upon the severity of the offense, encompassing intentional actions causing harm to oneself, others, or property damage. The escalating prevalence and diversity of an unlawful activities underscore the urgency in establishing efficient enforcement agencies. Conventional crime-solving methods, characterized by their tardiness and inefficacy, are no longer proficient in addressing the contemporary surge in criminal activities. Consequently, there is an opportunity to alleviate the workload of police personnel and contribute to crime prevention by developing reliable methods for anticipating crimes before they occur. To achieve this, the implementation of Machine Learning (ML) technologies is suggested. This study presents finding from several cases employing such strategies, simulating further interest in exploration. The shift in crime detection and prevention strategies is fundamentally rooted in observations made by authorities. The primary focus of this research is to demonstrate how machine learning can enhance the speed and accuracy of crime detection, prevention, and resolution. The overarching goal is to enable law enforcement and other authorities to swiftly and accurately detect, prevent, and solve crimes. This research underscores the transformative potential of artificial intelligence and machine learning in reshaping law agencies.

Keywords—artificial intelligence, machine learning, crime data, decision tree, random forest