University dropout is a problem related to the student, as a direct responsible, and with the university institution, knowing the possibilities of attrition is relevant for the institution. In this paper, it is proposed to use classification models to find patterns and predict possible dropouts in university students. An application has been implemented that uses information provided by the university and that generates classification models from different algorithms (neural networks, ID3, C4.5), and uses the most significant attributes within the available information. The performance of these models was compared to define the one that provided the best results and that will be used to classify the students. The results show that the algorithm of C4.5 presented improvements in performance with respect to the neural network and the ID3 and that the relation of credits approved by a student related to the credits that he should have taken is the most significant variable in the construction of the model, followed by the qualifications, while the modality of the admission exam through which the student entered the university did not turn out to be significant, since it does not appear in the generated decision tree.
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