Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models

Abstract

The term” crime prevention” refers to a group of initiatives that work with people, communities, businesses, non-governmental organizations, and all levels of government to address the numerous social and environmental risk factors for crime, disorder, and victimization in communities. In this paper, the authors proposed various regression model for the prediction of communities and crime including decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The communities and crime dataset are used for training and evaluation the proposed model. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods.