Campus Placement and Salary Prediction: Leveraging Machine Learning for Enhanced Employability

In an era of fierce talent competition, optimizing campus placement and predicting graduate salaries is vital. This paper explores ‘Campus Placement and Salary Prediction’ using SVM, Random Forest, Logistic Regression, KNN, and Gradient Boosting. We analyze a comprehensive dataset with student info, academics, skills, internships, and placement outcomes to identify success factors. We employ a majority voting rule among these models and have created a user-friendly website for practical use in academic institutions. Our multifaceted research supports institutions in enhancing student employability and aligning academic goals with the evolving job market. Logistic Regression, one of the models employed has campus placement prediction, has an accuracy of 84%, whereas Gradient Boosting, one of the models used for salary estimation, has an accuracy of 78%.