Smart Air Quality Monitoring and Prediction System With Data-driven Insights

Air pollution has serious consequences for people's health, the environment, and the world's economy. Using critical environmental variables like temperature (T), humidity (H), visibility (VV), wind speed (V), maximum wind speed (VM), particulate matter (PM2.5), sea level pressure (SLP), and maximum and minimum temperatures, this study introduces a machine learning-based Air Quality Index (AQI) prediction system. The system utilizes a Flask-based API to facilitate user interaction by accepting these environmental inputs, processing them through a Random Forest Regressor model optimized using Randomized Search Cross-Validation. This approach ensures accurate and efficient predictions of AQI levels. Exploratory Data Analysis (EDA), including correlation heatmaps, density plots, and pairwise visualizations, is employed to uncover relationships and dependencies between features. The most important elements influencing AQI may be identified by feature importance analysis utilizing the Extra Trees Regressor. This allows for focused interventions to be implemented. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are robust performance measures that are used to analyze the model in order to guarantee accurate outcomes. The technology does more than just forecast pollution levels; it also provides concrete suggestions on how to improve air quality according to those levels. These recommendations include encouraging public transportation, enforcing stricter industrial emission controls, increasing urban greenery, and adopting renewable energy solutions. By integrating real-time data input through an API, advanced machine learning algorithms, and tailored improvement suggestions, this system equips policymakers and environmentalists with valuable insights for air pollution mitigation, contributing to sustainable urban development and improved public health outcomes.