Student Placement Prediction Using Various Machine Learning Techniques

Authors

  • Milind Ruparel, Priya Swaminarayan

Keywords:

Student Placement, Categorical Encoding, Machine Learning, Feature Importance, Voting Classification.

Abstract

When “it comes to helping students achieve their goals, campus placement stands out as a crucial factor in every educational institution's consideration. Each student shares the common objective of graduating from college with a job offer in hand. To address this, a predictive model has been developed for this study, aimed at determining a student's likelihood of securing placement. The main objective of this research is to analyze historical data from the previous academic year, forecast placement opportunities for current students, and support efforts to increase the percentage of successful placements within institutions. The study also aims to propose a recommendation system that predicts whether an existing student will be placed. Four distinct machine learning classification algorithms have been utilized for this purpose: the K-Nearest Neighbors (KNN) algorithm, logistic regression algorithm, random forest algorithm, and Support Vector Machine (SVM) algorithm. These algorithms independently predict outcomes, and their efficiency is evaluated based on the dataset used. The ranking of efficiency is determined by the dataset's characteristics. This research contributes to identifying students with academic potential, enabling them to focus on and improve both their technical and social skills, thereby enhancing their chances of success in securing” placement.

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Published

24.03.2024

How to Cite

Priya Swaminarayan, M. R. . (2024). Student Placement Prediction Using Various Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 2107–2113. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5678

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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