Analysis and Prognosis of Covid-19 using Machine Learning Algorithms and Visualization using Tableau

ABSTRACT

This paper uses prognosticative machine learning models that predict corona positives and deaths as a result of the crisis, and the recovery rate from the pandemic. This method aids in diagnosing the contours of an individual's presumption in data transmission based on medical knowledge and calculates the unfolding virus's socioeconomic impact. It examines the Covid-19's spread technique with the help of machine learning models. It also identifies the approaching prophecy and recessive presumption of the crisis at the same time, and as a result, this applicable analysis aids similar countries in making decisions. This paper also considers the global prevalence of the plague. Within the first phase of the irruption, eight supervised classification epidemiologic models are used to estimate the day-to-day and monomer incidents of coronavirus throughout the world, as well as the vital replica variety, growth rate, and increasing time. Calculations are also made for the more intricate efficacious replica variety, which reveals that since the predominant cases are confirmed to the specific countries, the severity has decreased. Machine learning models' prognosticative capabilities are found to provide an additional satisfactory match, and simple estimates of daily incidents around the world. © 2022 IEEE.