Comparative Analysis of Machine Learning Models for Prediction of Air Quality Index

the air quality and its impact on public health and environment is a very crucial point in today’s world. In this paper, we are exploring the use of machine learning (ML) models to predict Air Quality Index (AQI). Here we are using historical air quality data of Delhi city to build the model. Exploratory data analysis (EDA) applied on the dataset before providing as input to learning model. The objective is to compare the performance of ML models random forest and XGBoost for the predicting of air quality index for Delhi city in India. We are focusing in accurately prediction of AQI, so that preventive or proactive actions will be taken to control air pollution. Ultimately it will be helpful for public health management. Along with this, we are bringing attention towards future research on the implementation of air quality monitoring system using recent technologies.