Machine Learning for Students Employability Prediction

Aniss Moumen, Imane El Bakkouri, Hamza Kadimi, Abir Zahi, Ihsane Sardi, Mohammed Saad Tebaa, Ziyad Bousserrhine, Hanae Baraka

2021

Abstract

Nowadays, students' employability is a major concern for the institutions, and predicting their employability can help take timely actions to increase the institutional placement ratio. Data mining techniques such as classification is best suited for predicting the employability of students. Knowing weaknesses before appearing can help students work in areas that they need to improve to best match the company's skillset. Moreover, predict student employability can help educational staff in elaborating curriculum programs. This paper presents a systematic and exploratory literature review on Machine learning algorithms for students employability from Scopus Database.

Paper Citation

in Harvard Style

Moumen A., El Bakkouri I., Kadimi H., Zahi A., Sardi I., Tebaa M., Bousserrhine Z. and Baraka H. (2021). Machine Learning for Students Employability Prediction. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 274-278. DOI: 10.5220/0010732400003101

in Bibtex Style

@conference{bml21,
author={Aniss Moumen and Imane El Bakkouri and Hamza Kadimi and Abir Zahi and Ihsane Sardi and Mohammed Saad Tebaa and Ziyad Bousserrhine and Hanae Baraka},
title={Machine Learning for Students Employability Prediction},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={274-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010732400003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Machine Learning for Students Employability Prediction
SN - 978-989-758-559-3
AU - Moumen A.
AU - El Bakkouri I.
AU - Kadimi H.
AU - Zahi A.
AU - Sardi I.
AU - Tebaa M.
AU - Bousserrhine Z.
AU - Baraka H.
PY - 2021
SP - 274
EP - 278
DO - 10.5220/0010732400003101