Use of supervised machine learning algorithms for state wise COVID-19 forecasting

Abstract

The Covid Techniques for predicting have demonstrated their value in anticipating perioperative outcomes for the objective of improve future decision-making activities. The designs have existed for a long time. Utilized in a large number of possible uses where unfavorable variables for a danger required be identifying and prioritizing. To take care of forecasting challenges, a large number of prediction approaches are widely utilized. This study demonstrates the model’s ability to estimate how many patients will be COVID-19 is a virus that affects people. Currently assumed to be a possibility danger to the human race. In this case, study; four conventional forecasting models were put to use to foresee the hazardous elements of COVID-19: LR, least LASSO, SVM, and ES.Each of the models makes 3 sorts of forecasts for the next ten days: the no. of freshly infected cases, the no.of newly infected cases, the no. of newly infected cases, the no. of newly fatalities, and the no. of recoveries. The study’s findings show that using these strategies in the present situation COVID-19 pandemic scenario is a promising mechanism.