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dc.contributor.advisorEsmaeily, Amir Sajaden
dc.contributor.authorAli, Mohammad Danish
dc.date.accessioned2021-04-28T19:52:49Z
dc.date.available2021-04-28T19:52:49Z
dc.date.issued2020
dc.identifier.citationAli, M.D. (2020). Forecasting and analysis of COVID-19 pandemic. Masters Thesis, Dublin Business School.en
dc.identifier.urihttps://esource.dbs.ie/handle/10788/4245
dc.description.abstractCoronaviruses are a group of viruses that cause various diseases in mammals and birds. In humans, they cause a range of respiratory disorders. This paper presents the analysis of the transmission of COVID19 disease and predicts the scale of the pandemic, the recovery rate as well as the fatality rate. We have used some of the well-known machine learning techniques as well as mathematical modeling techniques such as Support Vector Machine (SVM), Bayesian Ridge and Polynomial Regression and SIR model. 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.en
dc.language.isoenen
dc.publisherDublin Business Schoolen
dc.rightsItems in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.en
dc.rights.urihttp://esource.dbs.ie/copyrighten
dc.subjectCOVID-19 Pandemic, 2020-en
dc.subjectMachine learningen
dc.subjectData analyticsen
dc.titleForecasting and analysis of COVID-19 pandemicen
dc.typeThesisen
dc.rights.holderCopyright: The publisheren
dc.type.degreenameMSc in Data Analyticsen
dc.type.degreelevelMScen


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