🎓 EduCalm: Predicting Student Stress for Better Well-being

Developed by Harsh Gahlawat

EduCalm is a smart stress prediction system designed to assist educational institutions and health professionals in identifying students experiencing academic or health-related stress. Using a combination of academic performance and personal well-being indicators, this project leverages Machine Learning and Next.js to create a real-time web app for early intervention and support.

---

🌍 Real-World Relevance

With rising mental health issues among students, EduCalm serves as a bridge between data and action. Institutions can:

• Identify at-risk students early.
• Tailor support services and counseling.
• Inform policies for academic workload and wellness.

---

🚀 Tech Stack

• Frontend: Next.js, Tailwind CSS
• Backend: Node.js/Express (inside /backend)
• ML Model: Python, scikit-learn
• Data Source: Academic records, lifestyle surveys, health indicators

---

📂 Project Structure

├── backend/           # Python model & API
├── public/            # Static files
├── screenshots/       # App screenshots
├── src/app/           # Frontend pages/components
├── README.md
├── package.json

---

🔍 Features

• 🧠 Machine Learning model trained on academic and health data
• 🌐 Clean UI for live stress prediction
• 📈 Visualizations for individual stress metrics
• 🔐 Privacy-focused input (no personally identifiable data stored)
• ⚙️ Easily extendable for new institutions or datasets

---

📊 Input Features for Prediction

• 📚 GPA / Academic Scores
• 🕓 Study hours per day
• 😴 Sleep duration
• 🍎 Health issues (yes/no)
• 🧑‍🤝‍🧑 Family support
• 📱 Screen time
• 😓 Reported stress level (for supervised training)

---

🛠️ Getting Started

First, run the development server:

npm run dev
# or
yarn dev

Open http://localhost:3000 to view the app.

---

🤖 Machine Learning Model

• Algorithm: Random Forest Classifier

• Accuracy: ~84% on test data

• Target: Binary stress prediction (0 = Not stressed, 1 = Stressed)

• Preprocessing includes:

  • Label encoding
  • Feature scaling
  • Null handling

---

✅ Future Improvements

• 🔁 Real-time feedback loop for counselors
• 📉 Time-series analysis for stress trends
• 📲 Mobile version for better accessibility
• 🏥 Integration with wellness centers

---

🤝 Acknowledgments

Inspired by the vision of creating emotionally aware academic environments. Special thanks to peers, mentors, and the open-source community.

---

📢 Call to Action

Want to contribute? Help us:

• Add new datasets
• Train with larger, more diverse data
• Improve prediction accuracy

---

📬 Contact

Built by Harsh Gahlawat Let's build stress-aware campuses together 💚