Understanding and Predicting the Legislative Process in the Chamber of Deputies of Brazil

Abstract

In this article, based on of open legislative data mining, we propose a methodology to create a model capable of indicating which characteristics have a positive or negative impact on the approval of a bill by the Chamber of Deputies. Added to the explanatory capacity, the model can also predict whether a bill will be approved or not. The model was submitted to experiments and analysis that measured and validated its explanatory and predictive capacity. In order to identify the most relevant characteristics we use an impact formula that calculates the relevance of the characteristics of the model in its final approval or archiving decision. In the end, the generated model contributed by clarifying characteristics relevant to the approval or not of the bill and achieved a good performance in its predictive capacity.