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dc.contributor.advisorMostakim, Moin
dc.contributor.advisorNoor, Jannatun
dc.contributor.authorDas, Kaushik
dc.contributor.authorAzad, Tahzib
dc.contributor.authorIslam, Sadid
dc.contributor.authorChowdhury, Nuzhat
dc.contributor.authorAhmed, Nazia
dc.date.accessioned2023-12-06T05:30:10Z
dc.date.available2023-12-06T05:30:10Z
dc.date.copyright2023
dc.date.issued2023-05
dc.identifier.otherID 19101600
dc.identifier.otherID 19101464
dc.identifier.otherID 19101296
dc.identifier.otherID 19101262
dc.identifier.otherID 19101227
dc.identifier.urihttp://hdl.handle.net/10361/21928
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 43-45).
dc.description.abstractAccurately predicting the stock value enables investors to earn more money, reducing their uncertainty on whether to buy and sell. Again during the COVID-19 period, many companies have shown a different picture of the stock market situation. That is why investors cannot consider the company’s exact status in the stock market. The primary objective of our paper is to predict the future behavior of the stock market in the event of a pandemic using machine learning classification. To consider the future stock market condition, first, we looked at the past stock market condition and tried to make predictions by collecting data from two companies. Second, we tried to understand what happened in the stock market during the pandemic and used machine learning algorithms. Finally, make predictions through machine learning classifications by merging the data during the pandemic with past data. In conclusion, we have attempted to identify what was lacking in our instance and provide a concise description of the next steps.en_US
dc.description.statementofresponsibilityKaushik Das
dc.description.statementofresponsibilityTahzib Azad
dc.description.statementofresponsibilitySadid Islam
dc.description.statementofresponsibilityNuzhat Chowdhury
dc.description.statementofresponsibilityNazia Ahmed
dc.format.extent45 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.subjectStock marketen_US
dc.subjectPredictionen_US
dc.subjectData miningen_US
dc.subjectCovid-19en_US
dc.subject.lcshMachine learning
dc.subject.lcshDatabase management
dc.titleStock price predictionen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB.Sc. in Computer Science and Engineering


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