Tipificação de ocorrências policiais utilizando machine learning

Abstract

Public safety is one of the main pillars for society that directly influence the citizens quality of life. Lately this area has been receiving a great focus on the investment issue, as Ballesteros (2014) says his article, much of this value has been destined to process automation. In this context, technology enters to support various routine activities, making them more efficient through better management of their resources. With that, it was suggested in this assignment the use of machine learning algorithms, to automatically typify police occurrences. It was used a base of data with police records and from it some samples were taken for validation. For this tests were selected the algorithms C4.5, CART, KNN, SVM, Rede Neural, Ripper and Random Forest. The results gotten, indicated that the C4.5 and the Ripper had the best accuracy, becoming until 99% in some tests. In a test, the application of the coefficient of correlation of Matthews and of F1 score, reached 0.89 and 0.94 in that order.