Abstract
School dropout is one of the great challenges for the educational system. Educational data mining seeks to study and contribute with results that aim to hidden problems and find possible solutions. Considering its importance, this work aims to use two nonparametric techniques, Quantile Regression and Support Vector Regression, to predict the results of school dropout in the Brazilian scenario. The development of the work followed the phases of CRISP-DM. The evaluation metric of the models is the mean of the absolute error. The results show more significant results for Support Vector Regression.
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CITATION STYLE
do Nascimento, R. L. S., das Neves Junior, R. B., de Almeida Neto, M. A., & de Araújo Fagundes, R. A. (2018). Educational data mining: An application of regressors in predicting school dropout. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10935 LNAI, pp. 246–257). Springer Verlag. https://doi.org/10.1007/978-3-319-96133-0_19
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