Authors: Anant Samrat, Amar Deep Gupta, Shubham Dadwal, Adarsh Samrat,, Mayank, Priya Kumari
Abstract: Crime is a significant challenge in modern society, necessitating effective prevention strategies. Machine learning (ML) offers promising solutions for crime analysis and prediction. This study explores algorithms like Naive Bayes, SVM, Linear Regression, Decision Trees, Bagging, Stacking, and Random Forest for accurate crime prediction. The proposed Naive Bayes-based model achieved 99.9% classification accuracy on test data, outperforming previous models. By integrating empirical data and criminological insights, this approach effectively forecasts crimes, reducing crime and deterring criminal activities.
DOI: https://doi.org/10.5281/zenodo.16409884
