The objective of the project is to study the seasonality and the meteorological reasons behind the air pollution in Delhi. Moreover, we have experimented with a few machine learning based models to forecast the trend observed in the Air quality index.

The dataset contains air quality data and AQI (Air Quality Index) at hourly and daily intervals of various stations across multiple cities in India. The data has been made publicly available by the Central Pollution Control Board which is the official portal of the Government of India. The compiled dataset can be found here.

Download the dataset and extract it out into the air-quality-dataset folder.

• air-quality-analysis.ipynb - Contains the code used for visualizing the dataset.
• fb-prophet-air-quality-forecasting.ipynb - FB Prophet model for forecasting the air quality index.
• sarima-model-AQI-forecasting.ipynb - SARIMA model for forecasting the air quality index.
• rnn-lstm-model-AQI-forecasting.ipynb - RNN-LSTM model for forecasting the air quality index.

Shrey Shah and Parth Gupta, under the guidance of Dr. Shamik Chakraborty