Análise e predição nas votações de leis federais na Câmara dos Deputados

Abstract

This study aims to analyze machine learning algorithms and deep learning for the task of predictability of approval of bills. It follows a post-positivist approach, adopting the quali-quantitative paradigm as a methodology. In the search for results, experiments were carried out using the available data on the Portal of the Chamber of Deputies, following the steps of bibliographic review, definition of experimentation environment, descriptive analysis and prediction. It was also sought to do a descriptive analysis and to predict possible outcomes in the voting process of legislative proposals focusing on bills that have been voted.