Air Pollution Classification Using Machine Learning

Air pollution is a serious concern, posing risks not only to our health but also to the environment we rely on. To tackle this issue, our project aims to develop a user-friendly system that leverages machine learning to classify air quality based on critical indicators, including PM2.5, PM10, NO2, SO2, CO, and O3. We plan to enhance our model’s effectiveness through careful data preprocessing and feature engineering. We will assess various algorithms—such as Decision Trees, Random Forests, and SVM—focusing on metrics like accuracy and the F1 score to ensure reliability. The end product will provide real-time pollution forecasts via an intuitive interface, empowering individuals and communities to take timely action. Looking ahead, we intend to incorporate even more variables and roll out the system in practical environments, highlighting the vital role of machine learning in monitoring and safeguarding our air quality.