Crime Analysis Using Machine Learning

Abstract

Crime eradication and prevention have been a major setback of most developed countries. This paper deals with the analysis of criminal data record from the kaggle which belongs to the San Francisco crime dataset. We are finding the model with best accuracy, and performance of all the models is tested by us. Here, the implementation of multiple approaches from machine learning and its comparative analysis is done with the help of the data. We are finding which model has best accuracy and performance of all the models. It is shown that Linear SVC has achieved the best results of all the models considered. The inclusion of these methodologies to the investigation broadens the search and lessens the risks for the cops.