A Review on Placement Prediction and Analysis Using Machine Learning

Pranay Rapartiwar, Sanket Agade, Ashwini Mirge, Janvi Wakde and Sumit Muddalkar

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 1, 416-421

Abstract: The rate at which the students are placed in jobs are considered an essential aspect of measuring the efficacy of a particular institution in imparting proper training to its students, which leads to placement in jobs. Many a learner takes due interest in studying the placement rate when deciding which institution to choose as a place to educate themselves at the collegiate level in colleges or universities. Enhancing the placement opportunities of the students in jobs is considered a major goal of almost all academic institutions, and this review paper is a study dedicated to laying a foundation in the aspect of campus placement prediction using machine learning as a predictive tool. By delving into the subject of machine learning as a predictive tool, this paper serves as a directional path that stimulates future research after pointing out the existing shortcomes in this type of prediction in raising awareness in a basic way regarding the accuracy of the efficiency of a machine learning-based campus placement prediction system.

Keywords: Campus Placement; Machine Learning; Ensemble Techniques; Feature Selection; Accuracy; Predictive Performance (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i1:id:1412

DOI: 10.32628/IJSRST2613167

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