Machine Learning–Based Analysis Of Air Quality Parameters

Air pollution has become one of the most pressing global health and environmental issues, with both outdoor and indoor exposures leading to millions of preventable deaths annually. Concerns about indoor air quality have intensified because people now spend most of their time indoors. Inadequate ventilation combined with emissions from building materials and human activities can often lead to higher pollutant concentrations indoors than outdoors. Traditional monitoring systems, while accurate, are often expensive and difficult to maintain for continuous indoor deployment, limiting their practical use.This situation has sparked interest in the use of Machine Learning (ML) techniques, which can handle extensive datasets, uncover hidden relationships among environmental variables, and produce reliable forecasts to support decision-making in air quality management. In this research, a Machine Learning-based Air Quality Monitoring System was created to forecast ventilation status utilizing a publicly accessible dataset from Kaggle. The dataset encompasses essential environmental factors, including temperature, humidity, carbon dioxide (CO₂), particulate matter (PM2.5 and PM10), total volatile organic compounds (TVOC), carbon monoxide (CO), light intensity, motion detection, and occupancy count.