Abstract: In this research been applied predictive analytics techniques, and a KNN model has been generated to evaluate the pre- diction percentage. The KNN (K-Nearest Neighbors) algorithm was applied to predict the probability of dropout in each stu- dent and its accuracy was evaluated using a confusion matrix. Also, information was collected on the 12 relevant academic vari- ables for the study, including academic performance, the number of subjects taken, the number of electives, and selection, To pre- dict dropout, the number of neighbors was set to 4,...
Keywords: dropout, higher education, machine learning, data mining, predictive patterns
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