Predictive Analysis of Air Pollution using Machine Learning

Authors

  • Sunita Chalageri Department of Computer Science and Engineering, ACS College of Engineering, Bangalore, India
  • Prithvi Prakash Department of Computer Science and Engineering, ACS College of Engineering, Bangalore, India
  • Rakshitha Department of Computer Science and Engineering, ACS College of Engineering, Bangalore, India
  • Rachanaa Department of Computer Science and Engineering, ACS College of Engineering, Bangalore, India

DOI:

https://doi.org/10.34293/acsjse.v2i1.24

Keywords:

Air Pollution, Artificial Neural Network

Abstract

Where substances such as gases, particulates and biological molecules discharge hazardous or unsustainable quantities into the Earth's atmosphere, this is referred to as polluted air. It couldroot disease, allergy and smooth death in people; it may also impact on other living species, like animals and food crop, and harm the usual or constructed surroundings. Mutually human actions and normal processes can create air contamination. Air polution.

This study examines the limits of Linear Regression methods and the machine learning model's potential. Datasets are taken in the form of files from UCI, CSV (combination separated values) (University of California). Demonstrated through the comprehension of the explanatory variable in machine learning models that linear regression might help. This study reveals the character of the machine learning algorithms through research into different models' performance in connection with how they capture the link among air eminence and different variables.

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Published

2022-03-08

Issue

Section

Articles