Analysis of COVID-19 in World Dataset Using Machine Learning Models

COVID-19 had a global impact, affecting countries and regions to varying extents. While I can offer general information up to that date, it's essential to bear in mind that the situation is continually changing. For the most current and trustworthy updates, please consult authoritative sources. Machine learning, a branch of artificial intelligence, harnesses statistical methods to empower computers to acquire knowledge and render decisions without explicit programming. It operates on the principle that computers can gain insights from data, identify patterns, and exercise judgment with minimal human intervention. This paper considers covid -19 in world-related dataset like country, continent, total_confirmed, total_deaths, total_recovered, active_cases, serious_or_critical, total_cases_per_1m_population, total_deaths_per_1m_population, total_tests, total_tests_per_1m_population, population. The machine learning approaches which is used to analysis 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.