Paper Contents
Abstract
ABSTRACTCrime prediction and analysis has proven critical in helping people stay safe and assist law enforcement agencies. The emergence of big data and machine learning means that predictive models can detect previously unnoticed trends in crime data to assist in predicting the occurrence of a crime. In this project, machine learning algorithms, namely Random Forest, Decision Tree and Support Vector machine are applied to a crime dataset in order to obtain both prediction and analysis.The system has a web-based frontend developed with ReactJS and Vite, designed with Tailwind CSS, and provides real-time tracking and analysis of crime trends. It has been experimentally tested that the accuracy of Random Forest is highest of the models tested. The system indicates that ML-enabled systems could potentially help law enforcement to make decisions and allocate resources.INTRODUCTIONOne of the major issues of society, defining the conditions of economic growth and population safety, is crime. Most criminal activity that is investigated in the old system much relies on manual enquiries and utilization of previous trends that in the majority of cases are not a help in providing forecasts of forthcoming activities that may occur as far as crime is concerned. The development of the Artificial Intelligence (AI) concept and the field of the Machine Learning (ML) made it possible to analyze the data on crime properly and extract the hidden associations.The goal of the present study is to develop a crime prediction and analysis system based on the concept of machine learning algorithm that will be applied to analyzing and classifying the crime patterns. An adequate project also incorporates the effective visualization and interactive analytical interface through a friendly web interface.
Copyright
Copyright © 2025 Chaitra P N. This is an open access article distributed under the Creative Commons Attribution License.
