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Authors: Aniss Moumen 1 ; Imane El Bakkouri 1 ; Hamza Kadimi 1 ; Abir Zahi 1 ; Ihsane Sardi 1 ; Mohammed Saad Tebaa 1 ; Ziyad Bousserrhine 1 and Hanae Baraka 2

Affiliations: 1 National School of Applied Sciences of Kenitra ; 2 National School of Applied Sciences of Berrechid

Keyword(s): Employability, Machine learning, Students, review

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.

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Paper citation in several formats:
Moumen, A.; El Bakkouri, I.; Kadimi, H.; Zahi, A.; Sardi, I.; Tebaa, M.; Bousserrhine, Z. and Baraka, H. (2022). Machine Learning for Students Employability Prediction. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - BML; ISBN 978-989-758-559-3, SciTePress, pages 274-278. DOI: 10.5220/0010732400003101

@conference{bml22,
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 - BML},
year={2022},
pages={274-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010732400003101},
isbn={978-989-758-559-3},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - 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 - 2022
SP - 274
EP - 278
DO - 10.5220/0010732400003101
PB - SciTePress

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