MindMetrics is an advanced AI system developed by our team to predict depression, anxiety, and stress levels in students using machine learning. Our comprehensive solution helps educational institutions identify at-risk students early and provide data-driven interventions.
🔹 Why MindMetrics?
Early detection of stress, anxiety, and depression.
Data-driven insights for counselors and educators.
Customizable surveys for different student groups.
Secure, privacy-focused design.
🔗 Click Here to Explore It Visulaly
📱 Optimized for all screen sizes — mobile, tablet, and desktop
🔹 Key Advantages:
- Triple Prediction Model: Simultaneously assesses depression, anxiety, and stress
- Early Intervention: Identifies warning signs before crises occur
- Personalized Insights: Tailored recommendations based on severity levels
- Privacy-First: Secure data handling with anonymized reporting
| Feature | Description | Technology |
|---|---|---|
| Depression Detection | Predicts mild/moderate/severe levels | Multioutput Regression (AdaBoost) (73.86% accuracy) |
| Anxiety Analysis | Identifies low/medium/high anxiety | Multioutput Regression (GradientBoosting) (73.43% accuracy) |
| Stress Evaluation | Measures academic/personal/social stress | Multioutput Regression (XGB) (73.36% accuracy) |
| Interactive Dashboard | Real-time visualization of mental health trends | Django + Bootstrap |
✅ Multi-factor analysis (academics, sleep, social life, Family involvement)
✅ Personalized student profiles
✅ Semester-over-semester trend tracking
✅ Secure data encryption
| Role | Members | Contributions |
|---|---|---|
| ML Engineers | Sreyash, Debanjan, Bhaskar | Developed prediction models |
| Backend Devs | Sudip, Debprasad | Built API & database |
| Frontend Team | Debprasad, Sudip | Created dashboard UI |
| Data Analysts | Bhaskar, Debanjan | Processed datasets |
- Frontend: Django, Bootstarp
- Backend: Python
- ML Models: Scikit-learn, TensorFlow
- Database: PostgreSQL
- Deployment: Render, Cloudinary, NeonDB
- Data Collection: Anonymous surveys (PHQ-9, GAD-7, PSS adapted)
- Feature Analysis:
- Academic performance
- Social engagement
- Sleep patterns
- Self-reported moods
- ML Prediction: Three specialized models working in tandem
- Visualization: Interactive dashboard with risk indicators
- Upload student data (CSV/Excel)
- Schedule regular assessments
- Monitor dashboard alerts
# Clone repository
git clone https://github.com/Debprasad77/MindMetrics.git
# Set up environment
cd MindMetrics
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txt
# Run system
python manage.py runserver🔒 All predictions are anonymized
📊 Transparent model explanations available
🛡️ GDPR-compliant data practices
📧 Email: debprasad7047@gmail.com
🌐 Website: mind-metrics-v1.onrender.com
💙 "Supporting student well-being through ethical AI"