Alumni-Connect: Digital Platform for Centralized Alumni Data Management and Engagement

Alumni engagement and career guidance for students remain critical challenges in technical education institutions. Traditional alumni networks lack structured mechanisms for meaningful student-alumni connections, systematic career mentorship, and data-driven placement prediction. This paper presents Alumni-Connect, an AI-powered engagement platform that integrates machine learning-based mentor matching, career path prediction, and intelligent recommendation systems to bridge the gap between students and alumni professionals. The proposed system employs an XGBoost-based mentor matching algorithm to identify optimal student-alumni pairs with 91% accuracy based on skill overlap, career interests, and expertise domains. A transparent, weighted-feature career prediction model estimates individual placement probability and salary ranges with domain-specific calibration. The platform utilizes TF-IDF vectorization and cosine similarity for intelligent job and mentor recommendations, achieving 89% recommendation relevance in evaluation studies. The system supports role-based access for students, alumni, counsellors, HODs, principals, and administrators, with comprehensive analytics dashboards for institutional performance monitoring. Multi-modal profile management enables rich professional representations including certifications, internships, project portfolios, and social links validated through roll number standardization. Evaluation results demonstrate significant improvements in mentor matching accuracy, recommendation quality, and overall engagement compared to traditional directory-based networking systems.