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

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Mukesh Kumar 1,* Nidhi Walia 2 Sushil Bansal 2 Girish Kumar 3 Korhan Cengiz 4

1. School of Computer Application, Lovely Professional University, Phagwara, Punjab, India

2. Maharaja Agrasen University, Solan, Himachal Pradesh, India

3. School of Computer Application, Lovely Professional University-Phagwara, Punjab, 144001, India

4. Department of Information Technologies, Faculty of Informatics and Management, University of Hradec Kralove, Kralove, 50003, Czech Republic

* Corresponding author.


Received: 17 Apr. 2023 / Revised: 15 May 2023 / Accepted: 20 Jun. 2023 / Published: 8 Dec. 2023

Index Terms

Machine Learning Techniques, Logistic Regression, Gaussian Naïve Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbor


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.

Cite This Paper

Mukesh Kumar, Nidhi Walia, Sushil Bansal, Girish Kumar, Korhan Cengiz, "Predicting College Students’ Placements Based on Academic Performance Using Machine Learning Approaches", International Journal of Modern Education and Computer Science(IJMECS), Vol.15, No.6, pp. 1-13, 2023. DOI:10.5815/ijmecs.2023.06.01


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