Campus Training Management and Placement Eligibility Prediction

The Campus Training Management and Placement Eligibility Prediction system enhances student training and placement processes through advanced technologies and data-driven insights. This project addresses inefficiencies in student record management, training progress tracking, and placement prediction by integrating academic performance, acquired skills, and extracurricular involvement. Leveraging machine learning, the system employs classification algorithm Random Forest to predict student placement outcomes. Feature selection is based on academic records, technical expertise, and soft skills. Historical student data is used to train the predictor, improving accuracy and decision-making. Additionally, the system supports Training and Placement (TNP) cell operations by providing real-time analytics and insights. A React-based user interface enhances accessibility and usability. By implementing machine learning techniques, this system offers a scalable and reliable solution to optimize campus recruitment, bridging the gap between students and recruiters. Keywords— Campus Placement, Campus Training , Random Forest, Accuracy, Prediction.