Predicting Student Placement Outcomes with Bi-GRU: A Deep Learning Approach
DOI:
https://doi.org/10.48047/9z4wjs67Keywords:
Student Placement Data (SPD), prediction, Bi-directional Gated Recurrent Unit (BiGRU) algorithm and Improved Cuckoo Search Optimization (ICSO) algorithm.Abstract
For students, the shift from academia to the workforce is a pivotal time, and it is becoming more and more important to
be able predict their placement success. Placements are the main determinant of admission and establishment names. The
primary goal of this effort is to predict current students' placement prospects by analysing past student data from prior
years, which helps the institutions' placement rates rise. Based on the data of students who have been put in the past, this
system offers a Recommendation System (RS) that predicts whether the current student will be placed or not. If the
student is placed, the company is also predicted.
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References
Joy, L. C., & Raj, A. (2019, March). A review on student placement chance prediction. In 2019 5th International conference on advanced computing & communication systems (ICACCS) (pp. 542-545). IEEE.
Abed, T., Ajoodha, R., & Jadhav, A. (2020, January). A prediction model to improve student placement at a south african higher education institution. In 2020 International SAUPEC/RobMech/PRASA Conference (pp. 1-6). IEEE.
Maragatham, T., Yuvarani, P., Harishri, S., & Swetha, R. (2024, November). Student Placement Prediction using Deep Learning Techniques. In 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 620-626). IEEE.
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