AirGuard: Accessible Air Quality Tracker

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  Home Screen / Dashboard View

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  Output / Prediction

Inspiration

People with asthma, COPD, or other respiratory conditions often struggle to monitor air quality in real-time. For those with visual impairments or limited tech access, this challenge can be life-threatening.
AirGuard was built to offer a simple, accessible solution.

What It Does

AirGuard is a live dashboard that:

• Fetches real-time air quality and ozone data
  
• Uses color-coded safety levels for quick interpretation
  
• Offers ML-based predictions for air purification efficiency
  
• Is mobile-friendly and designed with accessibility-first principles

How I Built It

• UI: Streamlit (deployed on Streamlit Cloud)
  
• Backend: Python scripts for data handling
  
• ML: scikit-learn-based model to predict ozone output and efficiency
  
• Design: High-contrast visuals and a simplified layout
  
• Future-ready: Plans for speech and vibration-based alerts

Challenges I Ran Into

• Balancing functionality with accessibility
  
• Aligning ML predictions to real-world health use cases
  
• Finding and integrating reliable AQI/Ozone data sources

Accomplishments That I'm Proud Of

• Developed a fast, clean UI for non-technical users
  
• Live deployed app with a custom ML model
  
• Created a socially impactful, accessible tool
  
• Integrated AI with real-world environmental concerns

What I Learned

• How to align AI/ML with social good
  
• Accessibility principles in product design
  
• The real-world value of simple, inclusive interfaces

What's Next

• Add text-to-speech alerts for visually impaired users
  
• Integrate mobile notifications (Twilio, IFTTT)
  
• Translate the dashboard into Hindi and regional languages for rural use

Built With

• github
• python
• scikit-learn
• streamlit