International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Air pollution is a major global issue affecting the environment and human health. With the increasing concentration of harmful gases and particulate matter, monitoring and predicting air quality has become essential. This research aims to design and implement an Air Quality Prediction System using Python and Machine Learning (ML) techniques. The system predicts the Air Quality Index (AQI) based on environmental features such as PM2.5, PM10, CO, NO₂, SO₂, O₃, temperature, and humidity. Machine learning algorithms such as Linear Regression, Random Forest Regressor, and Support Vector Regressor (SVR) are applied to build predictive models. The model is trained and evaluated using Python libraries like pandas, numpy, scikit-learn, and matplotlib. Experimental results show that the Random Forest algorithm gives the best performance with higher accuracy and minimal prediction error. The proposed system can assist government agencies, researchers, and the public in understanding pollution trends and taking preventive measures to improve air quality.

Downloads: | DOI: 10.17148/IJARCCE.2025.141034