Predictive Modelling of Air Quality Index (AQI) Across Diverse Cities
  and States of India using Machine Learning: Investigating the Influence of
  Punjab's Stubble Burning on AQI Variability
Kamaljeet Kaur Sidhu Habeeb Balogun Kazeem Oluwakemi Oseni
Air pollution is a common and serious problem nowadays and it cannot be
ignored as it has harmful impacts on human health. To address this issue
proactively, people should be aware of their surroundings, which means the
environment where they survive. With this motive, this research has predicted
the AQI based on different air pollutant concentrations in the atmosphere. The
dataset used for this research has been taken from the official website of
CPCB. The dataset has the air pollutant concentration from 22 different
monitoring stations in different cities of Delhi, Haryana, and Punjab. This
data is checked for null values and outliers. But, the most important thing to
note is the correct understanding and imputation of such values rather than
ignoring or doing wrong imputation. The time series data has been used in this
research which is tested for stationarity using The Dickey-Fuller test. Further
different ML models like CatBoost, XGBoost, Random Forest, SVM regressor, time
series model SARIMAX, and deep learning model LSTM have been used to predict
AQI. For the performance evaluation of different models, I used MSE, RMSE, MAE,
and R2. It is observed that Random Forest performed better as compared to other
models.