An investigative analysis of Brazilian Judicial Data using Machine Learning Techniques
Judicial Sentences, Machine Learning, Supervised, Unsupervised, Data analytics, Datamining
Brazilian Courts have been working in virtualization of judicial processes since this century's rise, leading to a revolution in relations, services and labor force. A huge volume of data has been produced and computational techniques have been an intimate ally to keeping business processes under control and delivering services as juridical clients expect. However, there has never been any discussion about the use of intelligent solutions for this end as well as any issues related with automatic predicting and decision making using historical data in context. One of the problems that has already come to light is the bias in judicial datasets around the world. Thus, this work will focus on evaluating, applying and understanding resources with the end of better using machine learning techniques when working on judicial systems, and, therefore, raising the discussion related to secondary issues. We aim to present objective experiments over data using clustering algorithms and supervised traditional classifications techniques.