Senior Innovation Showcase
Active Final Year Projects Showcase
Explore verified, ongoing final year projects advancing across all academic batches. Filter by emerging tech stacks, discover architectures, and explore student innovation.
137
Active Projects
1
Academic Batches
24+
Tech Stacks
15
Project Domains
FA23 (2026)
Active
Predictive Academic Risk Analytics & Presence Verification Engine Using Decision Tree & Offline QR
The project solves the lack of intelligent academic monitoring in traditional attendance systems by providing automated presence verification, predictive student risk analysis, and real-time academic insights.
| Project Information | Status | Batch | Technologies | Action |
|---|---|---|---|---|
|
Predictive Academic Risk Analytics & Presence Verification Engine Using Decision Tree & Offline QR
The project solves the lack of intelligent academic monitoring in traditional attendance s...
|
Active | FA23 (2026) |
JavaScript
TypeScript
Python
+11
|
Details |
No Matching Projects Found
No senior projects matched your filter query. Try searching with different keywords, removing specific technologies, or selecting another academic batch.
Active Project
FA23 (2026)
Predictive Academic Risk Analytics & Presence Verification Engine Using Decision Tree & Offline QR
Complete Technology Stack & Tools
JavaScript
TypeScript
Python
Java
React
Next.js
Express.js
Firebase
Tailwind CSS
Material UI
Node.js
scikit-learn
Pandas
NumPy
Problem Statement & Scope
The project solves the lack of intelligent academic monitoring in traditional attendance systems by providing automated presence verification, predictive student risk analysis, and real-time academic insights.
Target Users & Audience
University Students
Faculty Members / Instructors
Department Administration
Faculty Members / Instructors
Department Administration
Functional Requirements
The system should issue each enrolled student a unique encrypted personal QR code permanently linked to their registration number, serving as their sole verified identity token for all attendance sessions throughout the semester.
The system should provide faculty members with a Faculty-Side Entry Scanner Portal via an installable PWA, enabling instant QR-based student check-ins at classroom entry, with a manual override option available for late arrivals before final session closure.
The system should automatically close the attendance session after the faculty ends the scan window, recording all unscanned students as absent, and immediately locking the record to prevent unauthorized modifications.
The system should enforce a Pre-Authorized Role-Based Access Control mechanism where all system accounts including Super Admin and Faculty are provisioned exclusively by a system administrator, completely eliminating public self-registration and enforcing role-specific module access.
The system should include a custom-built Chrome Extension that connects directly to the Firebase database and automatically populates attendance records on the official university portal, eliminating all duplicate manual data entry for faculty members.
The system should provide faculty members with a Marks Entry Module enabling them to record and update quiz scores and assignment marks for each enrolled student, which are displayed on the student's personal academic dashboard and included in downloadable performance reports.
The system should present each student with a personal analytics dashboard displaying their real-time attendance percentage, session-by-session presence timeline, and individual quiz and assignment marks, with an automated Red-Alert notification and email alert triggered to both the student and faculty member when attendance falls below the 75% threshold
The system should train and execute a Decision Tree Classifier using historical attendance records and academic performance data to generate a future absenteeism Risk Score for each student, displayed on the Faculty Analytics Dashboard for early intervention.
The system should generate downloadable PDF and Excel reports containing complete attendance records and marks data, available on-demand or on a scheduled basis with filters by student, course, section, and date range, while automatically archiving all finalized records to Google Drive for long-term academic audit compliance.
The system should provide faculty members with a Faculty-Side Entry Scanner Portal via an installable PWA, enabling instant QR-based student check-ins at classroom entry, with a manual override option available for late arrivals before final session closure.
The system should automatically close the attendance session after the faculty ends the scan window, recording all unscanned students as absent, and immediately locking the record to prevent unauthorized modifications.
The system should enforce a Pre-Authorized Role-Based Access Control mechanism where all system accounts including Super Admin and Faculty are provisioned exclusively by a system administrator, completely eliminating public self-registration and enforcing role-specific module access.
The system should include a custom-built Chrome Extension that connects directly to the Firebase database and automatically populates attendance records on the official university portal, eliminating all duplicate manual data entry for faculty members.
The system should provide faculty members with a Marks Entry Module enabling them to record and update quiz scores and assignment marks for each enrolled student, which are displayed on the student's personal academic dashboard and included in downloadable performance reports.
The system should present each student with a personal analytics dashboard displaying their real-time attendance percentage, session-by-session presence timeline, and individual quiz and assignment marks, with an automated Red-Alert notification and email alert triggered to both the student and faculty member when attendance falls below the 75% threshold
The system should train and execute a Decision Tree Classifier using historical attendance records and academic performance data to generate a future absenteeism Risk Score for each student, displayed on the Faculty Analytics Dashboard for early intervention.
The system should generate downloadable PDF and Excel reports containing complete attendance records and marks data, available on-demand or on a scheduled basis with filters by student, course, section, and date range, while automatically archiving all finalized records to Google Drive for long-term academic audit compliance.
Key Functional Highlights
- 1. QR-Based Presence Verification
- Each student is assigned a unique QR code linked to their registration ID.
- Faculty uses a scanner (PWA-based) to mark attendance instantly.
- 2. AI-Based Predictive Risk Analytics
- Uses Decision Tree Machine Learning model.
- Predicts students at risk based on attendance patterns and academic performance.
- Generates risk levels (Low / Medium / High).
- 3. Real-Time Academic Dashboard
- Live attendance percentage tracking.
- Student-wise academic progress view.
- Faculty analytics dashboard with class summaries and insights.
- 4. Progressive Web Application (PWA)
- Installable web app for faculty and students.
- Works on mobile and desktop.
- Provides smooth real-time experience.
- 5. Offline QR Attendance System
- Attendance can be marked without internet.
- Data is stored locally and synced when connection is restored.
- 6. Role-Based Access Control (RBAC)
- Separate access for Admin, Faculty, and Students.
- Secure login system using Firebase Authentication.
- 7. Academic Performance Tracking
- Faculty can enter quiz and assignment marks.
- Students can view combined attendance + marks report.
- 8. Automated Alerts System
- Alerts when attendance drops below 75%.
- Notifications sent to students and faculty for early intervention.
- 9. Report Generation
- Generates downloadable PDF and Excel reports.
- Filters available by student, course, and date range.
- 10. Chrome Extension Integration
- Automatically transfers attendance data to official university portal.
- Eliminates duplicate manual entry for faculty.
Non-Functional Specifications
Security
The system shall implement secure authentication using Firebase Authentication.
Role-Based Access Control (RBAC) shall be enforced for Admin, Faculty, and Student roles.
Attendance records shall be protected from unauthorized modification after session closure.
Data transmission and storage shall ensure confidentiality and integrity of academic records.
Performance
The system shall process QR-based attendance marking in real-time with minimal delay.
The dashboard shall load analytics and reports efficiently even for large datasets.
Machine learning predictions shall be generated within acceptable response time for faculty usage.
Availability
The system shall be accessible 24/7 through web and PWA interfaces.
Offline functionality shall be available for attendance marking and later synchronization.
The system shall recover gracefully from network interruptions without data loss.
Scalability
The system shall support multiple departments, courses, and large numbers of students.
The architecture shall allow future expansion such as additional AI models or modules.
Usability
The system shall provide a user-friendly and responsive interface for both students and faculty.
The dashboard shall present analytics and insights in a clear and understandable format.
Minimal training shall be required for faculty to operate the system.
Reliability
The system shall ensure accurate attendance recording without duplication or data inconsistency.
Data synchronization shall maintain consistency between offline and online states.
The system shall minimize downtime during academic operations.
Maintainability
The system shall be modular to allow easy updates and feature enhancements.
Codebase shall be structured for debugging, testing, and future improvements.
New ML models or features can be integrated without redesigning the entire system.
Data Integrity
All attendance and academic records shall remain consistent and tamper-proof after submission.
Firestore shall be used to ensure reliable real-time data storage and synchronization.
The system shall implement secure authentication using Firebase Authentication.
Role-Based Access Control (RBAC) shall be enforced for Admin, Faculty, and Student roles.
Attendance records shall be protected from unauthorized modification after session closure.
Data transmission and storage shall ensure confidentiality and integrity of academic records.
Performance
The system shall process QR-based attendance marking in real-time with minimal delay.
The dashboard shall load analytics and reports efficiently even for large datasets.
Machine learning predictions shall be generated within acceptable response time for faculty usage.
Availability
The system shall be accessible 24/7 through web and PWA interfaces.
Offline functionality shall be available for attendance marking and later synchronization.
The system shall recover gracefully from network interruptions without data loss.
Scalability
The system shall support multiple departments, courses, and large numbers of students.
The architecture shall allow future expansion such as additional AI models or modules.
Usability
The system shall provide a user-friendly and responsive interface for both students and faculty.
The dashboard shall present analytics and insights in a clear and understandable format.
Minimal training shall be required for faculty to operate the system.
Reliability
The system shall ensure accurate attendance recording without duplication or data inconsistency.
Data synchronization shall maintain consistency between offline and online states.
The system shall minimize downtime during academic operations.
Maintainability
The system shall be modular to allow easy updates and feature enhancements.
Codebase shall be structured for debugging, testing, and future improvements.
New ML models or features can be integrated without redesigning the entire system.
Data Integrity
All attendance and academic records shall remain consistent and tamper-proof after submission.
Firestore shall be used to ensure reliable real-time data storage and synchronization.