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  Published Paper Details:

  Paper Title

AIR QUALITY PREDICTION BASED ON MACHINE LEARNING

  Authors

  Shailesh Munge,  Sagar Kharche,  Piyush Joshi,  Kirti Bathe,  Santosh Waghmode

  Keywords

Air Pollution, Decision Tree, Linear Regression, Machine Learning, Random Forest, Supervised Learning, SVM.

  Abstract


One major basic right is clean air that is integral to the concept of citizenship and it's while not a doubt, the responsibility of every subject to try to do his/her half to stay the air clean. Air quality prognostication has been looked into because the key answer of early warning and management of pollution. During this paper, we tend to propose an Associate in nursing air quality prediction system supported by a machine learning framework known as the sunshine GBM model, to predict air quality. This model, trained victimization lightweight GBM classifier, take meteorology knowledge jointly of sources for predicting the air quality thereby increasing the prediction accuracy by creating full use of obtainable abstraction data. the prevailing air quality observance stations and satellite meteorologic knowledge offer period air quality observance info that is employed to predict the trend of air pollutants within the future. The projected system was found to administer Associate in nursing accuracy of ninety-two

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2107446

  Paper ID - 210229

  Page Number(s) - e210-e213

  Pubished in - Volume 9 | Issue 7 | July 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Shailesh Munge,  Sagar Kharche,  Piyush Joshi,  Kirti Bathe,  Santosh Waghmode,   "AIR QUALITY PREDICTION BASED ON MACHINE LEARNING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 7, pp.e210-e213, July 2021, Available at :http://www.ijcrt.org/papers/IJCRT2107446.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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