A Study on Variable Selections and Prediction for Covid-19 and Delta Dataset Using Machine Learning Approaches

Abstract

Ongoing initiatives to combat COVID-19 prioritize widespread vaccination, ongoing research, and implementing public health measures to mitigate disease transmission and its impact. Staying informed about the most current guidance provided by local health authorities and the World Health Organization (WHO) is crucial for safeguarding your well-being and that of your community. Data mining entails exploring patterns, trends, relationships, and valuable insights within extensive datasets by employing various techniques and algorithms. This process aims to extract useful information from structured and unstructured data sources. This paper considers COVID-19-related dataset like state name, state code, district, confirmed, Active, deceased, recovered, delta confirmed, delta deceased, delta recovered. The machine learning approaches are used to analyze and predict the dataset using linear regression, multilayer perceptron, SMOreg, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.