Mostakim, MoinSrabon, Abir AlamAbrar, Mahmudul HasanRahman, NimurAhmed, Washif UddinHridy, Salamat Sajid2023-12-202023-12-2020232023-05ID 18101389ID 18201165ID 18101111ID 18301204ID 22241140http://hdl.handle.net/10361/22013Cataloged from PDF version of thesis.Includes bibliographical references (pages 63-65).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.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.65 pagesenBrac 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.Stock price analysisExplainable AIQ-learningBi-LSTMRestricted boltzmann machineLSTMDeep belief networkDataset sizePerformance evaluationInterpretable modelsPredictive accuracyMachine learningArtificial intelligenceThesis