Analysis of prognosticate Omicron Using SVM & LASSO

Abstract

Although ML forecasting algorithms frequently use techniques that involve more complex features and predictive methods, their goal is the same as traditional methods: to improve forecast accuracy while minimizing the loss function. To cope with forecasting challenges, a number of prediction approaches are routinely utilized. This study demonstrates how machine learning algorithm could predict how many individuals got infested by Omicron, virus which is presently being taken as possible risk to humanity. Four common forecasting prototypes were used to predict the harmful components of omicron: linear regression (LR), SVM, LASSO & ES. Using these algorithms, system calculates amount of recently infested people, death count, & recovered patient count. In terms of predicting new confirmed cases, mortality rates, and rates of recovery, ES is efficiently accompanied by LASSO, LR, and SVM models. In addition, the system uses symptoms to detect and diagnose Omicron disease.