Predicting and Analyzing Air Quality Using Machine Learning

Air pollution is a major environmental issue that has a direct effect on our health and the ecosystem. Being able to accurately predict and analyze air quality is crucial for taking the right steps to reduce its harmful impacts. This study aims to create a machine learning-based system that predicts and analyzes the air quality index (AQI) by using pollutant levels and real-time data. The system is built around three main features: (1) AQI prediction based on pollutant levels, where users can enter concentrations of PM2.5, PM10, SO2, NO2, CO, and O3 to get AQI values and see classifications like Good, Moderate, or Unhealthy. We tested several machine learning models, including Linear Regression, Decision Tree, XGBoost, and Random Forest, ultimately choosing Random Forest as the top performer because of its impressive accuracy. (2) Real-time AQI prediction through the OpenWeather API, which lets users pick a location and receive live AQI updates, along with classifications and notifications. (3) Future AQI forecasting for the next seven days using Long Short-Term Memory (LSTM) neural networks, where users can input a location to get a week-long prediction and classification of air quality trends. This system offers valuable insights into pollution levels, helping both individuals and authorities make informed decisions. By combining traditional machine learning algorithms with deep learning techniques and real-time data collection, this research presents a thorough approach to monitoring air quality, ensuring accuracy and timely alerts for better environmental management.