A machine learning approach for analyzing the impact of Covid-19 crisis on people’s livelihood using novel regularized ridge regression and compared with SVM algorithm

The aim of the study was to introduce the Novel Ridge Regularization model for effective prediction of COVID-19 cases and its impact on people’s livelihood therefore by reducing the overfitting of data. In this study two groups were used for classification namely Novel Ridge regularization with sample size of 110 and SVM (Support Vector Machine) technique with sample size of 110, [8] similarly the dataset size of 1024 was used for this experiment. 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. Ridge Regularization model provides a better approach for analyzing COVID-19 impact on people’s livelihood than SVM model.