Level of Budget Execution According to the Professional Profile of Regional Governors Applying Machine Learning Models

Morales Rocha J.L.; Coyla Zela M.A.; Vargas Torres N.I.; Ramos Rojas J.T.; Mamani D.Q.; Huanca Frias J.O. · International Journal of Advanced Computer Science and Applications (2020) · DOI: 10.14569/ijacsa.2020.0111139

Resumen

Machine Learning is a discipline of artificial intelligence that implements computer systems capable of learning complex patterns automatically and predicting future behaviors. The objective was to implement a Machine Learning model that allows to identify, classify and predict the influence of the professional training of the governors in the execution of the public spending of the regional governments of Peru. Of the 14 indicators of academic training, professional experience and university studies were selected as significant indicators that contribute to the execution of public spending by the 25 governors of Peru. For the prediction of the execution of the public spending of the regional governors, a supervised learning algorithm was implemented. The mean square error for the Machine Learning regression model was 4.20 and the coefficient of determination was 0.726, which indicates that the execution of public spending by regional governments is explained with 72.6% by the professional experience and university studies of the governors. The regional governors of Peru with university studies and professional experience achieve better results in the execution of public spending in the regional governments of Peru.

Instituciones: Universidad Nacional de Moquegua; Universidad Nacional de Juliaca