ML-CPC: A Pathway for Machine Learning Based Campus Placement Classification

Abstract

The intense competition among students for a limited number of job opportunities poses a significant challenge to campus placements. There are various strategies that organizations can employ to tackle this issue. Primarily, it is essential to provide high-quality educational programs and opportunities for professional development that align with current market needs. This involves regularly updating the curriculum, integrating sector-relevant projects, and facilitating hands-on training experiences. Campus placements play a crucial role in evaluating an institution's caliber and ensuring the employability of its students. Institutions can enhance their placement records by implementing innovative solutions to challenges encountered in placements, such as intense competition and economic fluctuations. This requires a proactive strategy, collaboration with businesses, a focus on skill enhancement, and support for students' soft skills and professional development. By implementing these corrective measures, institutions can contribute to students' future success by better preparing them for the workforce. The primary objective of this paper is to conduct an exploratory analysis of the recruitment dataset. The application of supervised machine learning is employed to predict whether a student was placed, utilizing classification models. The proposed approaches and methods surpass all other machine learning models, achieving a recall value of 1, accuracy of 0.9524, precision of 0.8667, and an F1 score value of 0.9286 to address the ensemble architectural.