Forecasting and Analysis of COVID-19 Pandemic

Kaushik, Kanak (2020) Forecasting and Analysis of COVID-19 Pandemic. Masters thesis, Dublin, National College of Ireland.

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Abstract

COVID-19 is also known as Novel Coronavirus, was first found at a wet market in Wuhan, China. As the upsurge of the COVID-19 affects the respiratory system caused by severe acute respiratory syndrome (SARS) virus advances within the world, an increase in epidemiological data ensures researchers to make a plan of action for societal awareness and precautions against the virus. Machine learning algorithms can be applied to available datasets to explore insights that will help the countries to be prepared. This research uses multiple machine learning algorithms to predict the infected cases of coronavirus all over the world and analysis of the datasets. Machine learning algorithms like Support Vector Regressor have the lowest R2 score = 0.8273 among Polynomial Regression, and Bayesian Ridge Regression, and the highest RMSE value = 124328.5297 amongst the three models, which tells us that the Support Vector Regressor is the least preferred model to choose. Bayesian Ridge Regression has R2 score = 0.9321 and the lowest RMSE value = 71920.7332 to be the best model among the three.
Keywords: COVID-19, SARS, Machine learning, data analysis, SVR, Regression

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software R Medicine > R Medicine (General)
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Dan English
Date Deposited:	18 Jun 2020 15:42
Last Modified:	18 Jun 2020 15:42
URI:	https://norma.ncirl.ie/id/eprint/4311

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