The Use of Predictive Analyzes for University Dropout Cases

  • Iraqi Journal of Science
  • Hachem Harouni Alaoui Mathematics & Computer Department, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
  • Elkaber Hachem Mathematics & Computer Department, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
  • Cherif Ziti Mathematics & Computer Department, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
  • Mustapha Bassiri LISTA Laboratory Faculty of sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah Unversity, Fès, Morocco / Laboratory of Education and Training Sciences. Normal Superior School (ENS), Hassan II University, Casablanca. Morocco.
Keywords: dynamic programming, KNN, machine learning, predictive analytics, SVM

Abstract

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.

Published
2021-01-13
How to Cite
of Science, I. J., Hachem Harouni Alaoui, Elkaber Hachem, Cherif Ziti, & Mustapha Bassiri. (2021). The Use of Predictive Analyzes for University Dropout Cases. Iraqi Journal of Science, 44-51. https://doi.org/10.24996/ijs.2021.SI.1.7