Assessment of Prediction Models of Confirmed, Recovered and Deceased cases due to COVID-19. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Rakshit, P; Debnath, S; Mistri, J; Kumar, S
• Is Part Of: Journal of physics. Volume 1797:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1797/1/012004
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Pandemic relates to a situation where any disease starts spreading geographically and affects a entire country or the whole world. So when an epidemic becomes pandemic, it really a question of our survival. COVID -19 has become a pandemic as we all know and needs real and underneath research on that. The procession of death is uncountable still now. It can cause significant economic, social, and political disruption. So it's very necessary to know the impact of it on originating venue so that we can analyze its potential and rate of spreads. So to do this we have applied here some Machine learning algorithm and concepts of regression for prediction. In this present work we have made prediction model of confirmed cases, Recovered and death cases using K-Nearest Neighbour regressor and Gradient Boosting Regressor. The model performance is very good in predicting all the cases. The R squared value is very near to 1.