TY - JOUR ID - TI - The Use of Predictive Analyzes for University Dropout Cases AU - Hachem Harouni Alaoui 1 AU - Elkaber Hachem1 AU - Cherif Ziti1 AU - Mustapha Bassiri2, 3 PY - 2021 VL - IS - (Special Issue) International Conference on Communication, Management and Information Technology IC SP - 44 EP - 51 JO - Iraqi Journal of Science المجلة العراقية للعلوم SN - 00672904 23121637 AB - We will also derive practical solutions using predictive analytics. And this would include application making predictions with real world example from University of Faculty of Chariaa of Fez. As soon as student enrolled to the university, they will certainly encounter many difficulties and problems which discourage their motivation towards their courses and which pushes them to leave their university.The aim of our article is to manage an investigation of the issue of dropping out their studies. This investigation actively integrates the benefits ofmachine learning. Hence, we will concentrate on two fundamental strategies which are KNN, which depends on the idea of likeness among data; and the famous strategy SVM, which can break the issues of classification.Thanks to predictive analytics, we can come up concrete solutions to decrease this issue. Therefore, our case study was specifically limited to University of Chariaa-Fez, Morocco.

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