Machine Learning-Based Predictive Modeling for Recruitment and Selection in Campus Placement Processes

Abstract

This research paper delves into an investigation of candidate selection criteria within the context of College/University campus placements, employing various parameters through the application of Machine Learning Algorithms. As campus recruitment is most crucial part for every students who are seeking for job just after their studies as well for the company who wants to hire the correct candidate for the company. The study encompasses the utilization of classification models, including Random Forest, Decision Tree, Boosting, and Logistic Regression. By transforming company records into binary responses ("0" and "1"), the implemented models exhibit promising outcomes, achieving a test accuracy of approximately 99% in candidate selection.