Air Quality Prediction by Machine Learning

Authors

  • Ritik Sharma  Vishwakarma Institute of Technology, Pune, Maharashtra, India
  • Gaurav Shilimkar  Vishwakarma Institute of Technology, Pune, Maharashtra, India
  • Shivam Pisal  Vishwakarma Institute of Technology, Pune, Maharashtra, India

DOI:

https://doi.org/10.32628/IJSRST218396

Keywords:

Machine Learning, Internet of Things, AQI, Air Pollution

Abstract

The air quality observing framework estimates different air toxins in different areas to keep up great air quality. It is the consuming issue in the current situation. Air is defiled by the appearance of risky gases into the environment from the enterprises, vehicular outflows, and so forth These days, air contamination has arrived at basic levels and the air contamination level in many significant urban areas has crossed the air quality list esteem as set by the public authority. It significantly affects the soundness of the human. With the headway in innovation of ML, it is currently conceivable to anticipate the poisons dependent on the past information. In this paper we are presenting a gadget that can proceed with that can take present poisons and with the assistance of past toxins, we are running a calculation dependent on the ML to anticipate the future information of contaminations. The detected information is saved inside the Excel sheet for additional assessment. These sensors are utilized on the Arduino Uno stage to gather the contamination information.

References

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International Journal of Scientific Research in Science and Technology

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Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ritik Sharma, Gaurav Shilimkar, Shivam Pisal "Air Quality Prediction by Machine Learning" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 8, Issue 3, pp.486-492, May-June-2021. Available at doi : https://doi.org/10.32628/IJSRST218396