A machine learning-based air purification and accessibility-focused dashboard that predicts ozone output, classifies safety levels, and estimates indoor air purification efficiency — built to support individuals with respiratory conditions like asthma or COPD.
This project combines IoT-inspired data with software intelligence to improve indoor air quality using AI-enabled analysis. It features a multi-module ML model deployed as a Streamlit web app, optimized for accessibility and ease of use.
- Python
- Streamlit
- scikit-learn
- NumPy
- Pandas
- Predict ozone output (g/hr) using regression
- Classify air safety levels for respiratory health
- Estimate purification efficiency based on input factors
- Clean, accessible UI with real-time ML inference
- Hosted online via Streamlit Cloud
- Regression model for ozone output
- Classification model for air safety
- Efficiency estimation based on calculated metrics
ozone-generator-ml/
├── ozone_predictor.py
├── classifier.py
├── efficiency_model.py
├── app.py
├── data.csv
└── requirements.txt
To run locally:
pip install -r requirements.txt
streamlit run app.py- Integrating multiple ML models into a single interactive tool
- Designing with accessibility-first UI principles
- Applying AI to social-impact problems like air quality and public health
This project was published in the IRJMETS Journal.
📄 Link to publication
MIT License
👤 Author: Hriday Goyal
🔗 GitHub