| Issue |
E3S Web Conf.
Volume 309, 2021
3rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|
|
|---|---|---|
| Article Number | 01218 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/e3sconf/202130901218 | |
| Published online | 07 October 2021 | |
Covid-19 Forecasting using Supervised Machine Learning Techniques – Survey
1 M.Tech Student, C.S.E, GRIET, Hyderabad, Telangana, India
2 Associate Professor, C.S.E, GRIET, Hyderabad, Telangana, India
* Corresponding author: plsruthi@gmail.com
COVID-19 is a global epidemic that has spread to over 170 nations. In practically all of the countries affected, the number of infected and death cases has been rising rapidly. Forecasting approaches can be implemented, resulting in the development of more effective strategies and the making of more informed judgments. These strategies examine historical data in order to make more accurate predictions about what will happen in the future. These forecasts could aid in preparing for potential risks and consequences. In order to create accurate findings, forecasting techniques are crucial. Forecasting strategies based on Big data analytics acquired from National databases (or) World Health Organization, as well as machine learning (or) data science techniques are classified in this study. This study shows the ability to predict the number of cases affected by COVID-19 as potential risk to mankind.
Key words: pandemic / COVID-19 / corona virus / exponential smoothing / R2 score adjusted / machine learning supervised
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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