Abstract
The global epidemic of the novel coronavirus (COVID-19) called SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has infected millions and killed millions. The prevalence of the virus is of paramount importance in identifying future infections and preparing healthcare facilities to avoid death. Accurately predicting the spread of COVID-19 is a challenging analytical and practical task for the research community. We can learn to use predictive analytics to predict the positive outcomes of these risks. These predictive analytics can look at the risks of past successes and failures.
 In this paper, the Facebook prophet model discusses the number of large-scale cases and deaths in India based on daily time-series data from 30 January 2020 to 30 April 2021, for forecasting and visualization. The covid-19 pandemic could end prematurely if social distancing and safety measures are required to stabilize and control is required to achieve treatment in India. This paper suggests that the Prophet Model is more effective in predicting COVID-19 cases. The forecast results will help the government plan strategies to prevent the spread of the coronavirus.
Highlights
Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends
This paper suggests that the Prophet Model is more effective in predicting COVID-19 cases
To stop Covid -19 is biggest challenges to county like India
Summary
Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. The ARIMA model combines automatic regression and moving averages with realistic confidence intervals This algorithm requires a large amount of data to predict future cases [10,11]. The mathematical logic underlying the Prophet model includes three main elements, such as trends, seasonality, holidays, and an unlimited time series model with an additional fourth noise/error element. Since it follows the principle of additive regression, the Prophet is well suited as a component of linear and nonlinear functions of time [14]
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