Covid'19 virus life progress span by using machine learning algorithms and time series methods

On 30 January 2020, first coronavirus case was found in India, which was originated from a seafood wholesale market in Wuhan, China. World Health Organization (WHO) officially named this coronavirus as COVID-19. Since now the first case found, India has reported a total of 182,490 confirmed cases and 5186 deaths as of 31 May 2020. Currently, 6,172,448 confirmed cases and 371,186 deaths from the coronavirus COVID-19 outbreak as of now. COVID-19 plague does incredible damage to individuals’ everyday life and nation’s financial turn of events. This paper embraces two sorts of numerical models, i.e., Linear Regression model, Skew Normal Distribution Model. The pestilence patterns of SARS were first fitted and dissected so as to demonstrate the legitimacy of the current scientific models. The outcomes were then used to fit and examine the circumstance of 400COVID-19. The forecast consequences of two distinct models are diverse for various parameters and in various locales. By and large, the fitting impact of Skew Normal Distribution model might be better than the impact of Linear Regression Model. As per the present pattern, in view of the two models, the all-out number of individuals expected to be tainted is 136000-301720 in India. COVID-19 will be over most likely in Late-September, 2020 in India and before Late-October, 2020 in different territories individually.