INTELLIGENT AIR QUALITY FORECASTING SYSTEM USING MACHINE LEARNING FOR SMART CITIES

The proposed Air Quality Index (AQI) prediction system presents an effective and data-driven approach for estimating air pollution levels using machine learning techniques. By utilizing pollutant parameters such as PM2.5, PM10, NO₂, SO₂, and CO, the Linear Regression model is able to predict AQI with satisfactory accuracy. The system follows a structured process including data preprocessing, model training, and deployment through a Flask-based web application, ensuring efficient and user-friendly interaction.   The results demonstrate that the model can provide reliable predictions with minimal computational complexity. Compared to traditional monitoring systems, the proposed approach offers faster prediction and better accessibility. Although the current implementation uses historical data, the system can be further enhanced by integrating real-time sensor data and advanced machine learning models.   Overall, the project provides a scalable and practical solution for environmental monitoring and can support better decision-making for public health and pollution control.

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