Integrating Machine Learning in Air Quality Prediction Systems

Air pollution is described as the contains of dangerous substances in atmosphere that are emitted by natural sources or human activities and can harm humans, animals, and the environment. Sulfurous anhydride, carbon monoxide, Dioxide of nitrogen, trioxygen, carbon monoxide, and volatile organic compounds are some of the most common air contaminants. The development of tools for tracking and reducing air pollution is greatly supported by this analysis, which is important for both public health and environmental sustainability. To improve model performance, particular attention is paid to feature engineering and data pre-treatment methods. The study employs machine learning models, including Decision Tree Classifier, Logistic Regression, Naive Bayes, K-Nearest Neighbors, Random Forest, and SVM, to ascertain the most effective approach for accurate air quality forecasting. The key contribution of this work lies in its ability to provide a practical decision-making framework that can guide authorities in implementing focused interventions and optimizing resource allocation for pollution control. The hybrid approach utilized in this study surpasses traditional methods, offering a more precise tool for air quality forecasting. This model not only addresses the need for improved predictive performance but also acts as a useful tool for public health and environmental agencies seeking to minimize the harmful effects of air pollution.