Predicting Student Placement Using Machine Learning

Abstract

The Smart Placement Prediction System is a machine learning–based model that helps colleges predicts which students are most likely to get placed in campus recruitment. Traditional methods of manual data checking by the Training and Placement Officer (TPO) are time-consuming and often inaccurate. This system uses students’ academic scores, project details, technical skills, communication ability, and aptitude results to predict placement outcomes. Machine learning algorithms such as Logistic Regression, Decision Tree, and Random Forest are used to train the model on previous placement data. The system can also generate eligibility reports, placement trends, and improvement suggestions for students who are not eligible. The proposed approach reduces manual effort, improves accuracy, and supports data-driven decision-making for both students and TPOs.