@article{Dr. D.J. Samatha Naidu_R. Aruna_2022, title={Study of Air Quality Detection using Machine Learning Techniques}, volume={2}, url={https://ijsar.net/index.php/ijsar/article/view/71}, DOI={10.54756/IJSAR.2022.V2.i8.1}, abstractNote={<p><em>Over the past few decades, </em><em>due to<strong> </strong></em><em>human activities, industrialization, and urbanization, air </em><em>pollution </em><em>has become a life-threatening factor in many countries around the world. Air, </em><em>an important<strong> </strong></em><em>natural resource, has been compromised in terms of quality by economic activities. </em><em>pollution<strong> </strong></em><em>is a severe problem in areas where population density is high such as metropolitan cities. Various </em><em>sorts of </em><em>emissions caused by people’s actions, </em><em>like<strong> </strong></em><em>transportation, power, and fuel use, are affecting air quality. Considerable research has been </em><em>dedicated to<strong> </strong></em><em>predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. We forecast air quality by using machine learning to predict the air quality index of a given area. The air quality index is a dataset for </em><em>a</em> <em>typical</em><strong><em> </em></strong><em>measure used to indicate the pollutant (SO<sub>2</sub> NO<sub>2</sub>, RSPM, SPM, and more) levels over a period. The ML models like a Decision tree and Random Forest Classifier </em><em>are</em><strong><em> </em></strong><em>implemented and compared to show better accuracy.</em></p>}, number={8}, journal={International Journal of Scientific and Academic Research (IJSAR), eISSN: 2583-0279}, author={Dr. D.J. Samatha Naidu and R. Aruna}, year={2022}, month={Sep.}, pages={1–8} }