• Guntaka Sathvika
• Parchuri Poojitha
• Kandru Rakesh
• Lokesh Tadepalli
• Dr.Konda Chaitanya

Predicting air quality is essential to managing public health and the environment. In order to forecast air quality characteristics, this study suggests a supervised machine learning method that uses Random Forest Regression algorithm. By utilizing a dataset that includes a variety of pollutants, this model seeks to predict metrics related to air quality with a high degree of accuracy and consistency. The usefulness of Random Forest Regression in capturing intricate correlations between input factors and air quality indicators is determined through rigorous testing and assessment. The model's output displays encouraging performance indicators, such as strong generalization skills across various temporal and spatial contexts and high prediction accuracy. Through the development of useful tools that help the public, environmental organizations, and policymakers better understand and manage air pollution, this research advances the field of air quality monitoring systems.

Air Quality Prediction, Supervised Machine Learning, Random Forest Regression algorithm.

"AIR QUALITY PREDICTION USING RANDOM FOREST REGRESSION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.d76-d84, June-2024, Available :http://www.jetir.org/papers/JETIR2406312.pdf

"AIR QUALITY PREDICTION USING RANDOM FOREST REGRESSION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppd76-d84, June-2024, Available at : http://www.jetir.org/papers/JETIR2406312.pdf