@article{Alpeshbhai_Vohra_2022, title={SARS-CoV-2 Future Forecasting Using Multi-Linear Regression Model}, volume={10}, url={https://ijisae.org/index.php/IJISAE/article/view/1719}, abstractNote={<p>The 2019 pandemic in Wuhan, China caused a devastating global outbreak of the Coronavirus Disease (SARSCoV-2). Machine <br>learning offers a number of prediction models for future events that are based on training and testing, including conventional machine <br>learning and Deep Learning. This study shows that machine-learning models can anticipate the number of future SARS-CoV-2 patients <br>that are currently seen as a possible risk to the human race. Supervised machine learning models like linear regression, vector support and <br>regression tree are used for prediction. Data on the total cases and recovery cases are based on two types of predictions: new infections and <br>recovery situations. The machine-learning regression model is used to generate the outcome. In this paper, we present prediction of future <br>forecasting of Covid cases based on current situation by applying dataset of before and after pre-trial vaccine.</p>}, number={2}, journal={International Journal of Intelligent Systems and Applications in Engineering}, author={Alpeshbhai, Patel Jinal and Vohra , Safvan}, year={2022}, month={May}, pages={159–165} }