Analysis of training accompaniment needs through prediction models assisted by machine learning

The purpose of this study is to analyze tendencies in the needs of students in accompaniment in a perspective of prediction of the measures to be taken during the training. This approach consists in measuring, with adapted prediction models, the tendencies of accompaniment needs in three areas of competence of the training: competencies practices, written competencies and oral competencies. To this end, the accuracy of the models in these three areas of competence must be verified in order to classify their prediction parameters. In a first step we used data modeling of machine learning with data partitioning, 70% learning, 30% testing of all data. Then we compared the predictive models (SVM, Neural Network, Bayasian Network, CART, CHAID, C5) using the global precision index. This allowed us to select the best model based on its accuracy performance in the three areas of expertise already mentioned.