Accurate Air Quality Prediction Through the Use of Statistical Models and Machine Learning Approach.

The practice of estimating the concentrations of air pollutants in a certain area over a predetermined time frame is known as air quality prediction. As is well known, air pollution harms both the environment and human health as a result of constantly rising urbanization. Predicting air quality is the only approach to stop the negative effects of air pollution. Consequently, the project's abstract emphasizes the significance of predicting air quality and how to do it using statistical models and machine learning algorithms. According to the technique, the project would use supervised learning, in which a machine learning model would be trained on previous data to forecast future air quality levels. To discover the highest performing regression method, regression techniques such as linear regression, decision trees, and random forest regression will be investigated. The predictions given by this model allows individuals to be aware of diseases caused by air pollution and at what level diseases are caused, as well as to improve the air quality in surrounding places. This model's predictions may give them insight into how to maintain the air clean and prevent certain diseases.