Machine Learning Predictive Model for the Passive Transparency at the Brazilian Ministry of Mines and Energy

Abstract

This paper presents a case study based on the CRISP-DM Model and the use of Text Mining tools and techniques to automate the Passive Transparency process at the Brazilian Ministry of Mines and Energy. Thus, a Machine Learning Model is proposed to predict the class of the technical unit responsible for the data/information requested by citizens. Through the application of the algorithm LDA and TF-IDF it was possible to map the topics of the most relevant subjects for society. The stability of the model was tested from the comparative analysis between 5 known classification algorithms (Random Forest, Multinomial NB, Linear SVC, Logistic Regression, XGBoost and Gradient Boosting). XGBoost presented better performance and precision in multiclass learning outcomes.