AQI Predictor
A machine learning solution that accurately forecasts Air Quality Index (AQI) based on historical data and environmental factors.
AQI Predictor Demo Access
This project's code and demo are protected for client confidentiality. I'd be happy to showcase similar solutions tailored to your specific needs.
Project Overview
Created an intelligent AQI prediction system that leverages machine learning algorithms to forecast air quality levels with high accuracy. The solution ingests data from multiple sources including weather patterns, pollution measurements, and traffic density to generate reliable predictions for up to 72 hours in advance.
Development Challenges
Preprocessing and normalizing diverse data from multiple sources presented significant challenges. Fine-tuning the machine learning model to account for complex environmental interactions and seasonal variations required extensive experimentation and validation.
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Technologies Used
- Python
- TensorFlow
- Scikit-learn
- Pandas
- Flask
- React
- streamlit
- RESTful API
- Time Series Analysis
Outcomes & Results
The AQI prediction system achieved 87% accuracy in forecasting air quality levels, enabling local authorities and health organizations to issue timely advisories and helping vulnerable populations plan outdoor activities more effectively.
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AQI Predictor Demo Access
This machine learning is protected due to client confidentiality and intellectual property considerations.
This project was developed for a client with specific confidentiality requirements. The code repository and live demo are kept private to protect:
- Proprietary algorithms and business logic
- Custom implementation strategies
- Client data and specific requirements
- Competitive advantages and unique features
While I can't provide direct access to this specific project, I'm happy to discuss how I've implemented similar solutions and how we could adapt this approach for your needs.
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