An Analysis of Machine Learning Algorithms for AQI Prediction

Air quality is one of the most important factors which impact health of humans and environmental conditions. The Air Quality Index (AQI) is a metric that measures the air pollution level in a specific area. Accurately predicting AQI levels can help individuals and policymakers to make informed decisions regarding air quality management. In this study, we aim to forecast the Air Quality Index (AQI) using different machine learning models based on several pollutants such as PM2.5, PM10, NO2, SO2, CO, and O3. Our study demonstrates that machine learning algorithms can effectively predict AQI levels, providing a valuable tool for policymakers and individuals to manage air quality. We collected AQI data from several air quality monitoring stations in different cities. We used various machine learning models such as linear regression, decision tree, random forest, and support vector regression to predict AQI values. The results show that machine learning models can be used to forecast AQI values with high accuracy.