Abstract
The timely identification of students who do not wish to continue their high school studies is a problem that should be addressed urgently since it is possible to recognize those factors that are determining their decision, through the psychologists of the educational institutions can offer personalized advice to each student. To address this problem, a decision tree was developed using machine learning techniques to predict whether students are interested in continuing with their high school studies, a data set of student performance in secondary education was implemented. The data attributes include social demographic characteristics and are related to two schools, collected through school reports and questionnaires.
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