Forecasting Air Pollution Levels Using Machine Learning Algorithms

This work addresses the growing concern over environmental health by examining the application of machine learning algorithms to predict air pollution levels. The suggested model analyzes historical air quality data and meteorological variables using a variety of techniques, such as Linear Regression, Random Forest, and Gradient Boosting Machines (GBM). Through the integration of several factors, including temperature, humidity, wind speed, and emission sources, the model is able to accurately anticipate pollution levels. To improve model performance, data preprocessing methods including missing value imputation and standardization are used. The findings show that machine learning is capable of accurately predicting air pollution, which offers important information for public health and environmental policy decisions.