AIR QUALITY PREDICTION USING MACHINE LEARNING

Abstract

The survival of mankind cannot be imagined without air. Daily industrial, transport, and domestic activities are
stirring hazardous pollutants in our environment. Monitoring and predicting air quality have become essentially
important in this era, especially in developing countries like India. Air quality prediction is essential for environmental
monitoring and public health management. In this study, we investigate the effectiveness of various machine learning
algorithms for predicting air quality levels. Our dataset includes key air quality indicators such as AQI (Air Quality Index),
CO, Ozone, NO, and PM concentrations. After preprocessing and data partitioning, we trained and evaluated four
machine learning models: Furthermore, we implemented a real-time prediction mechanism enabling users to input air
quality parameters for instant predictions, aiding in timely decision-making and intervention strategies. Our findings
underscore the efficacy of machine learning in air quality prediction, offering valuable insights for environmental agencies
and policymakers.