Air Quality Prediction Using Machine Learning Algorithm

Days gone by air pollution is rapidly increasing, pollution occurs due to human activities, industrialization and burning of fossil fuels. The air is polluted by dangerous gases present in the atmosphere; the pollutants are carbon dioxide (CO2), carbon monoxide (CO), Sulphur dioxide (SO2), nitrogen dioxide (NO2), etc. The pollutants of which Particulate Matter (PM 2.5) consists of suspended particles with a diameter of less than 2.5 micrometers are considered harmful. Air pollution is a major issue that impacts humans and causes health problems including heart disease, lung cancer, and respiratory diseases such as emphysema and asthma. previous techniques such as probability, statistics, traditional methods, etc. These methods are too complex to predict. Machine learning (ML) is the best way to predict air quality. In this paper, we need to implement models that record information about air pollutant concentrations. We compare machine learning algorithms like linear regression (LR), random forest (RF), k-nearest neighbor (KNN), and decision tree regression (DT) to predict the air quality index (AQI).