• Ms.Anjana Rosamma
• Dr. Shashidhar Kini K

This project presents a data-driven web application developed using the MERN stack to analyze student performance. It collects and processes academic scores, attendance records, socio-economic data, and behavioral attributes. The system uses machine learning models and statistical methods to uncover patterns and predict student success. Interactive dashboards offer real-time insights to educators and policymakers. The platform emphasizes personalized learning and data-informed interventions. It ensures secure, scalable, and efficient performance monitoring. By identifying key influencers of academic success, the system aids in targeted decision-making. The integration of predictive analytics allows early detection of at-risk students. Educators can allocate resources effectively and plan remedial strategies. The project also promotes continuous tracking of learning outcomes. Overall, the solution demonstrates the potential of analytics in transforming educational practices.

Fake job classification, machine learning, NLP, XGBoost, EMSCAD, text mining

"Student Performance Analytics A Phase 1 Report on Student Performance Analytics Using MERN Stack", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1180-1183, July-2025, Available :http://www.jetir.org/papers/JETIRGX06228.pdf

"Student Performance Analytics A Phase 1 Report on Student Performance Analytics Using MERN Stack", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1180-1183, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06228.pdf