Sushil Bansal

Work place: Maharaja Agrasen University, Solan, Himachal Pradesh, India



Research Interests: Software Security, Machine Learning


Sushil Kumar Bansal is a Professor within the Department of Computer Science & Engineering at Maharaja Agrasen University, Baddi ( Himachal Pradesh). He is having a vast Industrial and Teaching experience of 25 Years. He did his Bachelor of Technology in Computer Science and Engineering from Dr.BR Ambedkar Regional Engineering College , Jalandhar , Master of Technology in Computer Science and Engineering from NITTTR Chandigarh and PhD in Computer Science and Engineering from LPU Jalandhar. His research interests are in Software Security, Machine Learning and IOT. He has published many papers in refereed journals, conference proceedings and book chapters on his research areas.

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:, 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.

[...] Read more.
Other Articles