Forecasting the potential influence of Covid-19 using Data Science and Analytics

The major purpose of the study topic is to use data science to anticipate the future effect of COVID-19 using existing data. The goal of this research is to use data science and analytics to generate precise forecasts of the number of substantiations and deaths. LSTM, GRUs, and Prophet are the major models created and tested for the solution. An LSTM model is a type of Recurrent Neural Network that is used to forecast datasets with increasingly changing patterns. Gated recurrent units only has two gateways: reboot and update. The prophet is best suited for forecasting assignments involving observation swith at least a year of history. The various models discussed above were used to the covid-19 data set to forecast the number of positive cases, active cases, and deaths associated with covid-19. We trained the model using data from April and May 2021 to demonstrate a comparison between the observed and expected number of positive events. To assume the future happing of COVID-19 by applying models which are in use, so that we will be able to calculate the impact of the disease's potential spread throughout the human being, preparing our selves to make proper planning and idea to prevent further transmission and equip health systems to manage the disease properly and battle the worldwide pandemic.