ML-Based: Placement Prediction Application

Abstract

This research paper examines machine learning models in predicting student placement outcomes in technical education. Given the increasing focus on employability in higher education, institutions need strong predictive models to improve placement readiness. We perform a stringent comparison of four sophisticated machine learning methods—Random Forest, XGBoost, Logistic Regression with Regularisation, and Support Vector Machines with RBF Kernel—on a complete dataset involving academic, technical, and behavioral metrics. Our approach requires feature engineering methods and advanced hyperparameter tuning to achieve the best predictive performance. Results reflect that the ensemble techniques consistently outperform traditional algorithms, with 92.3% accuracy achieved by XGBoost and more favorable recall measures. We further introduce a feature importance analysis tool that identifies the dominant determinants of placement success. The study proposes actionable suggestions to academic administrators and placement cells to help them develop intervention mechanisms specific to their requirements, filling the industry-academia needs gap.