Abstract
The objective of this article is to develop a method that integrates Machine Learning models to predict whether a student is at risk of dropping out or not, based on a set of data. First, we proceeded to collect, analyze and prepare a set of data, to make them usable by machine learning algorithms. Second, we tested this data on several algorithms such as Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, Support Vector Machine, Neural Networks, and K-Nearest Neighbours. Then, we exposed the evaluation and the deployment of these models. Finally, we have developed a web application that integrates these models, makes predictions, visualizes this data and models its performance.
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Ouabou, S., Idrissi, A., Daoudi, A., Bekri, M.A. (2023). School Dropout Prediction using Machine Learning Algorithms. In: Idrissi, A. (eds) Modern Artificial Intelligence and Data Science. Studies in Computational Intelligence, vol 1102. Springer, Cham. https://doi.org/10.1007/978-3-031-33309-5_12
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