In Order to Accurately Predict the Patients of COVID-19 Newely Formed Cases by Using SVM.

In this paper, we have shown you how to find covid-19 recovered cases, death cases and new cases using different types of machine learning algorithms. Machine learning based forecasting mechanism have proved their significance to anticipate in perioperative outcomes to improve the decision making on the future course of action. The ML modules have long been used in many application domains which needed the identification and prioritization of adverse factor for a threat. Several prediction methods are being popularly used to handle forecasting problems. This study demonstrate the capability if ML models to forecast the number of upcoming patient affected by COVID-19 which is presently considered as a potential threat to mankind. In particular four standard forecasting models such as linear regression, least absolute shrinking and selection operator, support vector machine and exponential smoothing have been used in this study to forecast the threatening factor of COVID-19. Three types of prediction are made by each of the models such as the number of newly infected cases the number of deaths, and the number of recoveries in the next 10 days.