dc.contributor.advisor | Mostakim, Moin | |
dc.contributor.advisor | Noor, Jannatun | |
dc.contributor.author | Das, Kaushik | |
dc.contributor.author | Azad, Tahzib | |
dc.contributor.author | Islam, Sadid | |
dc.contributor.author | Chowdhury, Nuzhat | |
dc.contributor.author | Ahmed, Nazia | |
dc.date.accessioned | 2023-12-06T05:30:10Z | |
dc.date.available | 2023-12-06T05:30:10Z | |
dc.date.copyright | 2023 | |
dc.date.issued | 2023-05 | |
dc.identifier.other | ID 19101600 | |
dc.identifier.other | ID 19101464 | |
dc.identifier.other | ID 19101296 | |
dc.identifier.other | ID 19101262 | |
dc.identifier.other | ID 19101227 | |
dc.identifier.uri | http://hdl.handle.net/10361/21928 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 43-45). | |
dc.description.abstract | Accurately 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.statementofresponsibility | Kaushik Das | |
dc.description.statementofresponsibility | Tahzib Azad | |
dc.description.statementofresponsibility | Sadid Islam | |
dc.description.statementofresponsibility | Nuzhat Chowdhury | |
dc.description.statementofresponsibility | Nazia Ahmed | |
dc.format.extent | 45 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 | Stock market | en_US |
dc.subject | Prediction | en_US |
dc.subject | Data mining | en_US |
dc.subject | Covid-19 | en_US |
dc.subject.lcsh | Machine learning | |
dc.subject.lcsh | Database management | |
dc.title | Stock price prediction | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B.Sc. in Computer Science and Engineering | |