Girish Kumar

Work place: School of Computer Application, Lovely Professional University-Phagwara, Punjab, 144001, India

E-mail: girish.21706@lpu.co.in

Website:

Research Interests:

Biography

Girish Kumar holds a B.Sc. (Computer Science) Degree and PGDCA, MIT from GNDU and is a Research Scholar currently working as an Assistant Professor at Lovely Professional University. He has more than 21 years of teaching experience. He has four patents to his credit and has published more than 20 research papers in different national as well as international conferences and journals. He has authored four books published by reputed national and international publishers. He is also a Certified Academic Associate by IBM for DB2. He is an active member of IAENG- International Association of Engineers.

Author Articles
Predicting College Students’ Placements Based on Academic Performance Using Machine Learning Approaches

By Mukesh Kumar Nidhi Walia Sushil Bansal Girish Kumar Korhan Cengiz

DOI: https://doi.org/10.5815/ijmecs.2023.06.01, Pub. Date: 8 Dec. 2023

Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students' placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students' placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of this study could be beneficial to educational institutions, students, and career counsellors.

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