Machine Learning-Based Prediction Analysis of Unlawful Activities to Aid Law Enforcement

Abstract

One of our society's most significant and pervasive issues is crime. Numerous crimes are perpetrated often each day. The development of policing strategies and the implementation of crime prevention and control depend greatly on crime prediction. The most popular prediction technique right now is machine learning. Little research, however, has rigorously contrasted various machine learning approaches for crime prediction. The dataset in this instance consists of the date and the annual crime rate for the corresponding years. The crime rate used in this project is only based on robberies. Utilising historical data, the authors employ the linear and random forest regression algorithms to estimate future crime rates. The algorithm receives the date as input, and the result is the total number of crimes that year.