An Estimation of COVID-19 Confirmed, Death, and Recovered Cases Across India Using Novel Ridge Regularization and Comparing with Support Vector Machine

Abstract

COVID-19 is caused by Coronavirus, which leads to mild to moderate symptoms like cough and sneezing. It causes severe acute respiratory syndrome. India has the third highest number of COVID-19 confirmed cases in the world. The COVID-19 analysis was about the estimation of confirmed, death, and recovered cases across India. The aim of the study was to introduce the Novel Ridge Regularization model for effective prediction of COVID-19 cases, therefore reducing the overfitting of data. Materials and Methods: In this study, two groups were used for classification namely Ridge regularization with sample size of 110 and SVM (Support Vector Machine) technique with sample size of 110, similarly the dataset size of 65,896 was used for this experiment. Result: Based on the experiment, it was observed that the ridge regularization has got Least RMSE values than the SVM model with significance p=0.032. Conclusion: Ridge regularization model provides a better approach for analyzing COVID-19 cases than the SVM model.