Abstract
COVID-19, being the virus of fear and anxiety, is one of the most recent and emergent of various respiratory disorders. It is similar to the MERS-COV and SARS-COV, the viruses that affected a large population of different countries in the year 2012 and 2002, respectively. Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty. The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution, and Random Forest Model. The analysis upon dataset has been performed considering the time duration from January 1st 2020 to 16th July 2021. The model has been developed to obtain the forecast values till September 2021. This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country. In India, the cases are rapidly increasing day-by-day since mid of Feb 2021. The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave. To empower the prediction for future validation, the proposed model works effectively.