Comparative analysis of machine learning models for stock price analysis across different dataset sizes
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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Brac University
2023
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| Online Zugang: | http://hdl.handle.net/10361/22013 |
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10361-220132023-12-20T21:02:43Z Comparative analysis of machine learning models for stock price analysis across different dataset sizes Srabon, Abir Alam Abrar, Mahmudul Hasan Rahman, Nimur Ahmed, Washif Uddin Hridy, Salamat Sajid Mostakim, Moin Department of Computer Science and Engineering, Brac University Stock price analysis Explainable AI Q-learning Bi-LSTM Restricted boltzmann machine LSTM Deep belief network Dataset size Performance evaluation Interpretable models Predictive accuracy Machine learning Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 63-65). The nature of the stock market has always been ambiguous as it constantly fluctuates for various factors. The regular fluctuations have always made it difficult for investors to invest. This paper compares six different machine learning models for stock price analysis: Explainable AI, Q-learning method, LSTM, Bi-LSTM, Restricted Boltzmann Machine, and Deep Belief Network. Each model was evaluated using three different datasets consisting of 7000, 10000, and 14000 data points, respectively. The results of the experiments show that depending on the size of the dataset, the performance varies and the specific model used. In general, the deep learning models (LSTM, Bi-LSTM, Restricted Boltzmann Machine, and Deep Belief Network) outperformed the Explainable AI and Q-learning models in terms of predictive accuracy. However, the Explainable AI and Q-learning models had the advantage of being more interpretable and easier to understand, which may be desirable in certain applications. Overall, this study provides insights into the strengths and weaknesses of various machine learning models for stock price analysis and highlights the importance of choosing the right model for the specific task at hand. Future work concentrates on optimizing the performance of the models further or exploring the use of hybrid models that combine the strengths of multiple approaches. Abir Alam Srabon Mahmudul Hasan Abrar Nimur Rahman Washif Uddin Ahmed Salamat Sajid Hridy B.Sc. in Computer Science and Engineering 2023-12-20T06:43:04Z 2023-12-20T06:43:04Z 2023 2023-05 Thesis ID 18101389 ID 18201165 ID 18101111 ID 18301204 ID 22241140 http://hdl.handle.net/10361/22013 en 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. 65 pages application/pdf Brac University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Stock price analysis Explainable AI Q-learning Bi-LSTM Restricted boltzmann machine LSTM Deep belief network Dataset size Performance evaluation Interpretable models Predictive accuracy Machine learning Artificial intelligence |
| spellingShingle |
Stock price analysis Explainable AI Q-learning Bi-LSTM Restricted boltzmann machine LSTM Deep belief network Dataset size Performance evaluation Interpretable models Predictive accuracy Machine learning Artificial intelligence Srabon, Abir Alam Abrar, Mahmudul Hasan Rahman, Nimur Ahmed, Washif Uddin Hridy, Salamat Sajid Comparative analysis of machine learning models for stock price analysis across different dataset sizes |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Mostakim, Moin |
| author_facet |
Mostakim, Moin Srabon, Abir Alam Abrar, Mahmudul Hasan Rahman, Nimur Ahmed, Washif Uddin Hridy, Salamat Sajid |
| format |
Thesis |
| author |
Srabon, Abir Alam Abrar, Mahmudul Hasan Rahman, Nimur Ahmed, Washif Uddin Hridy, Salamat Sajid |
| author_sort |
Srabon, Abir Alam |
| title |
Comparative analysis of machine learning models for stock price analysis across different dataset sizes |
| title_short |
Comparative analysis of machine learning models for stock price analysis across different dataset sizes |
| title_full |
Comparative analysis of machine learning models for stock price analysis across different dataset sizes |
| title_fullStr |
Comparative analysis of machine learning models for stock price analysis across different dataset sizes |
| title_full_unstemmed |
Comparative analysis of machine learning models for stock price analysis across different dataset sizes |
| title_sort |
comparative analysis of machine learning models for stock price analysis across different dataset sizes |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/22013 |
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