Covid-19 Future Forecasting Using Supervised Machine Learning Models

Mukesh S Rao Dadge, Karthik Kumar, Jitendra Kumar and Deepanshu

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 3, 149-156

Abstract: In order to enhance decision-making on the future course of action, machine learning (ML) based forecasting methods have demonstrated its relevance to foresee in unexpected outcomes. Many application fields that required the detection and prioritization of negative aspects for a threat have long employed ML models. To deal with forecasting issues, a variety of prediction techniques are frequently utilized. This work shows how ML models can predict the amount of forthcoming COVID-19 patients who will be afflicted, which is now thought to pose a threat to humanity. Our suggested technique combines a number of approaches in an effort to improve the explore operation's cooperativeness. We create the Covid-19 application in this effort can be able to predict outcomes from Total Confirmed cases, Fatalities and Recoveries.

Keywords: Covid-19; Total Confirmed Cases; Fatalities; Recoveries. (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2390335
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