Campus placement is a crucial event for students and educational institutions in today's competitive job market. Predicting placement outcomes and potential salaries can help students prepare better and institutions improve their placement strategies. This research presents a machine learning system using Random Forest classifiers to predict both placement probability and expected salary based on various student features. The proposed system achieves 93.97% accuracy in placement prediction and a 98.7% R² score in salary prediction. The trained models are deployed using a Flask-based web application with an interactive skill assessment module that provides personalized recommendations and learning paths for students. The system demonstrates strong potential for assisting students in improving employability and helping institutions enhance placement success rates.
G.Anjaiah et al. (Mon,) studied this question.