dc.contributor.advisor | Hossain, Muhammad Iqbal | |
dc.contributor.advisor | Rahman, Rafeed | |
dc.contributor.author | Maliha, Miftahul Zannat | |
dc.contributor.author | Trisha, Ananya Subhra | |
dc.contributor.author | Tamzid Khan, Abu Mauze | |
dc.contributor.author | Das, Prasoon | |
dc.contributor.author | Shakil, Shuhanur Rahman | |
dc.date.accessioned | 2023-08-01T06:08:29Z | |
dc.date.available | 2023-08-01T06:08:29Z | |
dc.date.copyright | 2023 | |
dc.date.issued | 2023-01 | |
dc.identifier.other | ID: 22341041 | |
dc.identifier.other | ID: 20241062 | |
dc.identifier.other | ID: 19301045 | |
dc.identifier.other | ID: 18101603 | |
dc.identifier.other | ID: 18301243 | |
dc.identifier.uri | http://hdl.handle.net/10361/19233 | |
dc.description | This 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.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 29-31). | |
dc.description.abstract | For 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 pertinence | en_US |
dc.description.statementofresponsibility | Miftahul Zannat Maliha | |
dc.description.statementofresponsibility | Ananya Subhra Trisha | |
dc.description.statementofresponsibility | Abu Mauze Tamzid Khan | |
dc.description.statementofresponsibility | Prasoon Das | |
dc.description.statementofresponsibility | Shuhanur Rahman Shakil | |
dc.format.extent | 31 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac 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.subject | Crypto-currency | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Bitcoin | en_US |
dc.subject | Sentiment analysis | en_US |
dc.subject | Prediction | en_US |
dc.subject | Stock Market | en_US |
dc.subject.lcsh | Machine learning | |
dc.subject.lcsh | Digital currency | |
dc.title | A deep learning approach to predict crypto-currency price by evaluating sentiment and stock market correlations | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |