Empirical Analysis on Crime Prediction using Machine Learning
Ritesh Kumar, N.P. Saravanan, K. Nirmala Devi, P. Jayanthi, N V Krishnamoorthy, S. Karthi · 2023
Criminal offence or Crime is an detrimental act that not only affects an individuals but affects an community. In the current scenario crimes sequences happens every second in various places, in various crime patterns, as the results the crime rate is increasing each growing places. It is very much essential to determine the different factors for the possible occurrence of crimes and find the optimized ways to reduce the crime. Crimes can be compartmented into multiple factors like Rape, Murder, Assault and Theft and so on. Data were gathered from National Crime Records Bureau. This proposed work focuses to apply various machine learning algorithms such as Naïve bayes, Random forest, Decision tree and Support vector machine and Logistic regression for reducing of crime rates in India. Performance of the proposed models compared with various measures like precision, recall, accuracy, and fl score along with the confusing matrix. Also considers the error value for various models with the help of mean absolute error technique. As a result, the Naïve bayes model gives the best results of 98.94% comparing with other.