Prediction of active debt in the State of Pernambuco, Brazil Application of techniques the data mining

Abstract

Application of data mining (DM) techniques to optimize the process of collection of Active Debt (AD) of the State of Pernambuco, Brazil. We apply the following data mining techniques: Decision Tree (DT), Logistic regression (LR), Nayve bayes (NB), Support vector machine (SVM), also applied to the Random Forest technique which is considered an essemble method. We observed that the RF technique obtained better results than all the techniques of classification, reaching higher values in all metrics analyzed. We note that the creation of a data mining model to choose which debts can succeed in the collection process can bring benefits to the pernambuco government. With the application of RF technique, we obtained indexes above 85% in the evaluation of the metrics.