Article’s

Campus placement prediction plays a crucial role in enhancing student career opportunities and institutional performance. This paper presents a Smart Placement Prediction System that leverages Machine Learning techniques to predict student employability outcomes. The system utilizes Logistic Regression to classify students based on academic performance, technical skills, and extracurricular activities. A full-stack web application is developed with role-based access for students, administrators, and recruiters. The dataset is preprocessed and used to train the model, achieving high prediction accuracy. The system provides insights into key placement factors and helps institutions take proactive measures to improve placement rates. The results demonstrate the effectiveness of machine learning in automating and improving the placement prediction process..