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dc.contributor.advisorParvez, Mohammad Zavid
dc.contributor.authorAhmed, Tanvir
dc.contributor.authorHasan, MD. Nahid
dc.contributor.authorAshik, Md.
dc.contributor.authorHasan, Md. Jahid
dc.date.accessioned2021-10-12T04:14:00Z
dc.date.available2021-10-12T04:14:00Z
dc.date.copyright2021
dc.date.issued2021-06
dc.identifier.otherID 17301054
dc.identifier.otherID 17301096
dc.identifier.otherID 17301131
dc.identifier.otherID 17301227
dc.identifier.urihttp://hdl.handle.net/10361/15221
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 34-37).
dc.description.abstractSince pandemic disease outbreaks are causing a major financial crisis by affecting the worldwide economy of a nation, machine learning techniques are urgently required to forecast and analyze the economy for early economic planning and growth and to resettle it.A large number of studies have shown that the spread of the disease has experienced a significant change in the economy. As a consequence, we will use machine learning to construct early warning models for economic crisis prediction. This paper used a publicly available data-set containing information about National Revenue, Employment Rate, and Workers Earnings of USA over 239 days (1 January 2020 to 12 May 2020). Then, we applied Multilayer Perceptron (MLP) Neural Network and Random Forest classifier to identify recession in revenue, employment rate, and workers earnings, therefore, got accuracy 95%, 81%, 89% and 85%, 81%, 89% respectively. To analyze how much the economy affected by this pandemic, we drive revenue, employment rate, and workers earnings data-set into Long shortterm memory (LSTM) and Random Forest Regressor hence got accuracy 92%, 90%, 90%, and 95%, 93%, 93% respectively. Before a country faces an economic recession, it is important to identify which sector to emphasize to minimize this unexpected scenario. Using machine learning, we analyzed the data and predicted the economy so we could help save a significant amount of capital for a country.en_US
dc.description.statementofresponsibilityTanvir Ahmed
dc.description.statementofresponsibilityMD. Nahid Hasan
dc.description.statementofresponsibilityMd.Ashik
dc.description.statementofresponsibilityMd. Jahid Hasan
dc.format.extent37 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectMultilayer perceptronen_US
dc.subjectLong short-term memoryen_US
dc.subjectRandom foresten_US
dc.subjectEconomic recessionen_US
dc.subjectMachine learningen_US
dc.subjectPandemicen_US
dc.subjectCovid-19en_US
dc.subject.lcshCOVID-19 (Disease)
dc.titleEconomic crisis prediction due to pandemic outbreak using machine learningen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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