Students' Orientation Using Machine Learning and Big Data


  • Farouk Ouatik sultana moulay sliman university
  • Mohammed Erritali sultana moulay sliman university
  • Fahd Ouatik cadi ayyad university
  • Mostafa Jourhmane sultana moulay sliman university



Big data, Classification, Naïve Bayes, SVM, Random Forest Tree, Neural Net-work


Students' orientation in public institutions and choosing their academic paths or their appropriate specialization is important to students to continue their studies Easily in their school career. Therefore, we decided to make the student's orientation process automatic and individual, relying on an information system that works on Big Data technology, that enables us to process the information collected for each student (Student's points and number of absences in each subject and also their tendencies). Then we used the algorithms of machine learning, that enable us to give the appropriate specialization to each student. In this paper, we compared the accuracy and execution time of the following algorithms (Naïve Bayes, SVM, Random Forest Tree and Neural Network), where we found that Naïve Bayes is the best for this system.


Author Biographies

Farouk Ouatik, sultana moulay sliman university

 computer sciences

Mohammed Erritali, sultana moulay sliman university

computer zciences 

Fahd Ouatik, cadi ayyad university

physic departement 

Mostafa Jourhmane, sultana moulay sliman university







How to Cite

Ouatik, F., Erritali, M., Ouatik, F., & Jourhmane, M. (2021). Students’ Orientation Using Machine Learning and Big Data. International Journal of Online and Biomedical Engineering (iJOE), 17(01), pp. 111–119.



Short Papers