Ensemble Voting Classifier-based Machine Learning Model for Predictive Modeling of Campus Student Placements

Campus placements are a significant aspiration for college students, as they reflect both the quality of the educational institution and the performance of its students. Securing a placement during campus interviews can greatly impact a student's career trajectory, making it essential for institutions to effectively forecast students' potential for success in these interviews. In this context, machine learning, coupled with knowledge discovery processes, has emerged as a valuable tool for predicting student performance in placement scenarios. This paper proposes an ensemble model based on a voting classifier that integrates the BayesNet and J48 algorithms. This innovative approach aims to classify student academic data and predict their placement opportunities with high accuracy. The ensemble model leverages the strengths of both classifiers to enhance the overall predictive performance. This model produced 91% of accuracy in the placement prediction. J48 and BayesNet classifiers are combined by probability average-based combination rule in the ensemble voting model.