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login page where admin , faculty, student can login
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it is the admin page
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this faculty page
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this is student page
Inspiration
What it does
Inspiration
Managing academic activities like attendance, timetables, and student performance is often manual and inefficient. We wanted to build a system that not only automates these processes but also adds intelligence using machine learning to help institutions make better decisions.
What we built
We developed Acadex, a full-stack academic management system that handles attendance, courses, exams, and timetable management. In addition, we integrated a machine learning model to predict student risk levels based on performance data, and built an automated timetable generation system.
How we built it
Frontend: React (Vite) with Tailwind CSS for a clean UI
Backend: Spring Boot with REST APIs and JWT authentication
Database: MySQL
ML Integration: Python (Flask) with Logistic Regression model
Backend communicates with the ML service via APIs to fetch predictions
What we learned
Integration of multiple technologies (React + Spring Boot + ML)
Building scalable REST APIs
Handling real-world constraints like timetable conflicts
Deploying and connecting a machine learning service with a backend system
Challenges we faced
Synchronizing frontend, backend, and ML services
Handling timetable conflicts and incomplete slot filling
Managing real-time data consistency
Ensuring fallback when ML service is unavailable
What makes our project special
Combines automation + machine learning
Real-world use case (college management)
Smart features like:
Risk prediction
Auto timetable generation
Optimized seating system
Built With
- flask
- frontend:-react-(vite)
- javascript-(jsx)
- joblib-apis:-rest-apis-for-frontend-backend-communication-and-ml-integration-tools-&-build:-maven
- node.js
- scikit-learn-(logistic-regression)
- spring-boot
- spring-data-jpa-(hibernate)-database:-mysql-machine-learning:-python
- spring-security-(jwt)
- tailwind-css-backend:-java
