Introduction:

 Machine learning (ML) has proved itself as a prominent field of study over the last decade by solving many very complex and sophisticated real-world problems. The study is focused on live forecasting of COVID-19 confirmed cases and study is focused on the forecast of COVID19 outbreak and early response. These prediction systems can be very helpful in decision making to handle the present scenario to guide early interventions to manage these diseases very effectively This study aims to provide an early forecast model for the. The aim of this study is the future forecasting of COVID19 spread focusing on the number of new positive cases, the number of deaths, and the number of recoveries.

Methods:

 The aim of this study is the future forecasting of COVID19 spread focusing on the number of new positive cases, the number of deaths, and the number of recoveries. The dataset used in the study has been obtained from the GitHub repository provided by the centre for Systems Science and Engineering, Johns Hopkins University. The folder contains daily time series summary tables, including the number of confirmed cases, deaths, and recoveries.

Results:

 This study attempts to develop a system for the future forecasting of the number of cases affected by COVID-19 using machine learning methods. The dataset used for the study contains information about the daily reports of the number of newly infected cases, the number of recoveries. The study performs predictions on death rate and according to results ES performs better among all the models, LR and LASSO perform well and achieve almost the same R2 score.

Conclusion:

The precariousness of the COVID-19 pandemic can ignite a massive global crisis. Some researchers and government agencies throughout the world have apprehensions that the pandemic can affect a large proportion of the world population. The results of the study prove that ES performs best in the current forecasting domain given the nature and size of the dataset. Overall the authors conclude that model predictions according to the current scenario are correct which may be helpful to understand the upcoming situation. The study forecasts can be of great help for the authorities to take timely actions and make decisions to contain the COVID-19 crisis.

Important Sentences

• Machine learning (ML) has proved itself as a prominent field of study over the last decade by solving many very complex and sophisticated real-world problems
• This study attempts to develop a system for the future forecasting of the number of cases affected by COVID-19 using machine learning methods
• Some researchers and government agencies throughout the world have apprehensions that the pandemic can affect a large proportion of the world population
• An ML-based prediction system has been proposed for predicting the risk of COVID19 outbreak globally
• According to the results, the death rates will increase in upcoming days, and recoveries rate will be slowed down
• Overall we conclude that model predictions according to the current scenario are correct which may be helpful to understand the upcoming situation