From real-time risk classification to detailed feature-importance breakdowns, SPP gives academic institutions the intelligence to act — before it's too late.
Automatically classifies each student as Excellent, Average, or At Risk using a weighted multi-factor scoring model grounded in educational research.
Institutional-level analytics with live donut charts, bar comparisons, and summary statistics — updated the moment student data changes.
Every prediction comes with a breakdown showing which factors — CGPA, attendance, backlogs — contributed most to a student's score.
Students and teachers receive specific, personalised recommendations — not just a score, but a clear path to improvement.
Separate dashboards for students, teachers, and administrators. Each role sees exactly what they need — nothing more, nothing less.
Export complete student records and prediction results to CSV with a single click. Ready for integration with institutional reporting systems.
Role-based authentication with local credential storage — admin, teacher, and student accounts with full access control and dynamic credential management.
Track how a student's predicted score evolves over time. Every save creates a timestamped record, revealing trajectories and trends.
Administrators can manage all student records, teacher accounts, and system-wide data — including add, edit, delete, and re-predict operations.
Students or teachers input CGPA, SGPA, attendance percentage, active backlogs, assignment completions, projects, and extracurricular involvement.
A research-backed model applies weighted coefficients to each normalised factor — CGPA at 30%, SGPA at 25%, attendance at 20%, and so on.
The aggregate score is mapped to one of three tiers — Excellent (7–10), Average (4–7), or At Risk (0–4) — with a logistic confidence estimate.
Feature importance bars reveal which factors drive the score. Personalised recommendations guide students toward measurable improvement.
SPP adapts to who is using it. Each role has a tailored interface with precisely the capabilities they need.
Full system control — manage all student records, accounts, and platform-wide data with complete add, edit, and delete access.
Department-level oversight — run predictions, monitor student populations, and intervene early for students showing at-risk signals.
A personal academic companion — students enter their own data, receive instant predictions, and track their improvement over time.
The backend exposes a clean REST API built with Flask and SQLite,
orchestrated with JWT authentication. The frontend communicates via
fetch() and stores session state locally.
Credentials are managed locally with role-based access control for enterprise-grade security.