Machine Learning-Based System for Weather Prediction and Air Quality Index Estimation

This paper presents a study on "Machine Learning for Weather Prediction and Air Quality Index Estimation," aimed at enhancing weather forecasting and air quality monitoring. Integrating historical weather data with real-time atmospheric measurements from the OpenWeather API, the study utilizes the Random Forest Machine Learning algorithm to construct predictive models. Backend operations are managed by a Django application on AWS EC2, supported by Nginx as a reverse proxy. The frontend, a ReactJS-based web app hosted on AWS S3 and distributed via CloudFront, offers an intuitive interface. Additionally, a dedicated mobile app extends the system’s reach, delivering real-time updates on weather conditions and air quality. This comprehensive approach empowers users with precise insights for informed decision-making and environmental awareness.