A Machine Learning-based Centralized Placement Hub for Enhancing Campus Recruitment Efficiency

In Campus recruitment and student placement preparation represent critical components of higher education, particularly in engineering and technology sectors. Traditional placement preparation methodologies rely heavily on fragmented resources, ad hoc mentorship, and unstructured study approaches, leading to inconsistent performance outcomes. This research introduces a comprehensive Placement Hub platform that integrates systematic preparation roadmaps, centralized resources, progress tracking, and machine learning-based performance prediction. The system employs three classification algorithms— Using preparation criteria and academic achievement, logistic regression, decision trees, and random forests are used to forecast student placement outcomes. Performance evaluation utilizes accuracy metrics, confusion matrices, and ROC curve analysis under standardized conditions. Results demonstrate that Random Forest attained the greatest prediction accuracy of 97%, outperforming Decision Tree (95%) and Logistic Regression (93%). The Placement Hub platform enhanced student preparation efficiency by 68%, increased completion rates by 45%, and improved overall placement readiness scores by 52%. This research contributes a comprehensive framework for educational data mining in placement preparation, bridging the gap between administrative systems and student-centric predictive analytics.