Analyzing Air Quality in Gurugram using Machine Learning

Challenge Background

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.

The Problem

Machine learning-based analysis of air quality data can provide valuable insights into the patterns, trends, and underlying factors contributing to air pollution in Gurugram. By leveraging machine learning algorithms, it is possible to develop predictive models, identify sources of pollution, and assess the effectiveness of control measures. This information can then be used to inform policy decisions and develop targeted interventions to reduce air pollution in Gurugram and other cities in India and create awareness among the general public about the severity of air pollution in the city and its effects on their health and well-being. 

Goal of the Project

The goals of this project are: 

• Identifying possible data sources
• Relevant Data Collection
• Data Cleaning and Preprocessing
• Developing AQI calculation strategy
• Evaluating trends and patterns in AQI and other parameters
• Applying machine learning modeling
• Model training and evaluation
• Deploying model as an API using FastAPI

Project Timeline

What you'll learn

Data Collection, Custom AQI calculation strategy, Data Analysis, Feature Selection and Engineering, Machine Learning, API development, MLOps