• Shazia Tazeen
• Samala Harshith Kumar
• Edla Yeshwanth

One of the most important instruments for evaluating air quality based on pollutant concentrations is the Air Quality Index (AQI). This approach divides the air quality into six different categories, from "Good" to "Severe," each of which has a range of AQI values, accompanying symptoms, possible health risks, and suggested preventative measures. "Severe" denotes extremely dangerous pollution with major health hazards, whereas "Good" denotes great air quality with no symptoms at all. Knowing the AQI gives people the power to choose outdoor activities wisely and take suitable precautions to protect their health. This system is an important resource for increasing awareness of and reducing the harmful effects of air pollution on human health. Additionally, the data is essentially split into the North and South regions. The random forest regressor demonstrated a great predictive performance with an accuracy score of 0.89 in the North, as seen by its Mean Squared Error of 1436.99 and R-squared value of 0.89. The random forest regressor demonstrated an accuracy score of 0.91 in the South, with a Mean Squared Error of 3020.29 and an R-squared value of 0.87.

Air pollution, AQI, linear regression, prediction, and random forest regressor

"AIR QUALITY INDEX ANALYZER USING MACHINE LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.b140-b144, December-2023, Available :http://www.jetir.org/papers/JETIR2312117.pdf

"AIR QUALITY INDEX ANALYZER USING MACHINE LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppb140-b144, December-2023, Available at : http://www.jetir.org/papers/JETIR2312117.pdf