Predicting Student Performance Analysis for Campus Recruitment Using Machine Learning Techniques

Campus recruitment screening is traditionally based on static eligibility criteria such as CGPA and arrears, which may not accurately reflect students' employability.This paper presents a Machine Learning (ML) based screening model that analyzes student performance data to predict campus drive eligibility.Various supervised ML algorithms including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbors are applied to a student performance dataset.The experimental results show that ensemble-based ML models provide higher prediction accuracy and better decision support for placement officers.The proposed system ensures fair, efficient, and data-driven student screening for campus recruitment.