Air pollution is a major environmental and public health issue in India, with Gurugram being one of the worst affected cities. Gurugram is a rapidly growing industrial and urban hub in the National Capital Region (NCR) of India, and is known for its high levels of air pollution caused by emissions from vehicular traffic, industries, construction activities, and other anthropogenic sources.
The Air Quality Index (AQI) is a measure of how polluted the air is and it reflects the concentration of major air pollutants, such as PM2.5, PM10, nitrogen oxides, and sulfur dioxide, among others. AQI ranges from 0 to 500, with higher values indicating more polluted air.
The need to analyse air quality in Gurugram using machine learning arises from the fact that air pollution is a major public health concern, and it has been linked to a range of health problems, such as respiratory and cardiovascular diseases, lung cancer, and stroke. In addition, air pollution also has adverse effects on the environment, such as acid rain, ozone depletion, and climate change. Therefore, it is essential to monitor and analyse air quality trends in Gurugram to better understand the causes of pollution, identify hotspots, and design effective strategies to reduce air pollution and protect public health and the environment.
Understanding the problem, Identifying data sources and collecting relevant data
Developing custom AQI calculation from available parameters, Data Preprocessing and Visualization
Developing and Evaluating Machine Learning model to predict AQI
Deploying the model as an API using FastAPI or Flask
Data Collection, Custom AQI calculation strategy, Data Analysis, Feature Selection and Engineering, Machine Learning, API development, MLOps