Air Pollution Prediction Using Data Science Process and ML

Forecasting Air pollution through Data Science and Machine Learning involves the study of historical air quality records to predict future pollution levels. It consists of data collection, preprocessing, feature selection, and the application of predictive models such as regression, neural nets, and decision trees. These models utilize real-time and previously recorded environmental data to determine pollution trends and possible risk areas. Such predictions aid decision makers in taking pre-emptive actions to enhance air quality management. This, along with supporting models, completes the goal of monitoring the environment and responding to pollution threats timely.