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dc.contributor.advisorHossain, Muhammad Iqbal
dc.contributor.advisorRahman, Rafeed
dc.contributor.authorMaliha, Miftahul Zannat
dc.contributor.authorTrisha, Ananya Subhra
dc.contributor.authorTamzid Khan, Abu Mauze
dc.contributor.authorDas, Prasoon
dc.contributor.authorShakil, Shuhanur Rahman
dc.date.accessioned2023-08-01T06:08:29Z
dc.date.available2023-08-01T06:08:29Z
dc.date.copyright2023
dc.date.issued2023-01
dc.identifier.otherID: 22341041
dc.identifier.otherID: 20241062
dc.identifier.otherID: 19301045
dc.identifier.otherID: 18101603
dc.identifier.otherID: 18301243
dc.identifier.urihttp://hdl.handle.net/10361/19233
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 29-31).
dc.description.abstractFor the technological shift, advancing epoch towards cryptocurrency intensified the impactful method. Metaverse can originate the base operation into a diversified level. The extension of digital marketing contributes to blockchain technology more.Our research demonstrates, attested cryptocurrency price evaluation associated with the stock and sentiment. In our research, we have implemented various techniques to predict cryptocurrency prices. Crypto like bitcoin, ethereum and litecoin are the primary focus in this paper. Our research observes the fluctuation into the cryptocurrency prices. In our research procedure, we used the LSTM-GRU hybrid, ARIMA for time series prediction. The research follows sentiment analysis from the twitter scrapped data. The research provides cogent insights of cryptocurrency price prediction fluidity with the stock price and the twitter sentiment on following cryptocurrencies. Additionally, the data merge with the LSTM time series model depicts the cryptocurrency stock market and shows us the relationship between stock price, twitter sentiment and cryptocurrency price pertinenceen_US
dc.description.statementofresponsibilityMiftahul Zannat Maliha
dc.description.statementofresponsibilityAnanya Subhra Trisha
dc.description.statementofresponsibilityAbu Mauze Tamzid Khan
dc.description.statementofresponsibilityPrasoon Das
dc.description.statementofresponsibilityShuhanur Rahman Shakil
dc.format.extent31 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.subjectCrypto-currencyen_US
dc.subjectMachine learningen_US
dc.subjectBitcoinen_US
dc.subjectSentiment analysisen_US
dc.subjectPredictionen_US
dc.subjectStock Marketen_US
dc.subject.lcshMachine learning
dc.subject.lcshDigital currency
dc.titleA deep learning approach to predict crypto-currency price by evaluating sentiment and stock market correlationsen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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