Machine Learning Algorithm for Predicting Crime Type and Occurrence

Crime is a widespread problem that upsets social harmony and presents serious difficulties for law enforcement. Effective crime prevention and resource allocation depend on an understanding of crime trends and the ability to forecast future events. In order to evaluate criminal behaviour and forecast the kinds, locations, and dates of crimes, this study makes use of Kaggle's open-source crime dataset. Using Naïve Bayes classification, the suggested method outperforms current models with an accuracy of 93.07%. The findings show how machine learning might improve methods for predicting and preventing crimes.