Development of Machine Learning-Based Predictive Models for Air Quality Analysis and Prediction

Authors

  • Shah A Dept. of CSE, East West Institute of Technology, Visvesvaraya Technological University, Bangalore, INDIA
  • PrayashRimal P Dept. of CSE, East West Institute of Technology, Visvesvaraya Technological University, Bangalore, INDIA
  • Singh D Dept. of CSE, East West Institute of Technology, Visvesvaraya Technological University, Bangalore, INDIA
  • Jagadeesh B N Dept. of CSE, East West Institute of Technology, Visvesvaraya Technological University, Bangalore, INDIA

Keywords:

AQI(Air Quality Index),, Data Cleaning,, Softmax Function

Abstract

One of the biggest environmental problems right now is air pollution. Air quality is needed to be consistently monitored and assessed to ensure better living conditions. The U.S. Environmental Protection Agency (EPA) uses the air quality index (AQI) to standardize the air quality. However, AQI requires precise and accurate sensor readings and complex calculations, making it not feasible for portable air quality monitoring devices. The aim of this paper is to find an alternative way of monitoring and characterizing air quality through the use of integrated gas sensors and building predictive models using machine learning algorithms that can be used to obtain data-driven solutions to mitigate the risk of air pollution.

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Published

2025-11-26

How to Cite

[1]
A. Shah, P. PrayashRimal, D. Singh, and B. N. Jagadeesh, “Development of Machine Learning-Based Predictive Models for Air Quality Analysis and Prediction”, Int. J. Comp. Sci. Eng., vol. 7, no. 15, pp. 32–35, Nov. 2025.

Issue

Vol. 7 No. 15 (2019): IJCSE Special Issue May Edition

Section

Research Article

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