Rasel, Annajiat AlimKhan, Rubayat AhmedShachcha, Ifad BhuiyanSiam, Muhammad Ziaus2022-12-132022-12-1320222022-05ID: 17201120ID: 21341055ID: 17201027http://hdl.handle.net/10361/17645Cataloged from PDF version of thesis.Includes bibliographical references (pages 39-40).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.Predicting financial data is really important for investors Often times investors do not have a proper tool to properly assess the market and forecast their predictions. Furthermore, not only investors in modern day civilians are also willing to invest as well and as there is an abundant amount of data available from the financial sector it is of utmost significance to find the optimal algorithm in a general case scenario. This project aims to show a comparison between the results found from some of the popular neural network algorithms. In this project we have employed the help of Dense Neural Network [DNN], Recurrent Neural Network [RNN], Long Short Term Memory unit [LSTM], Convolutional Neural Network [CNN] and a pipeline where we combined LSTM and CNN. We have kept some of the parameters similar and compared the results to determine an algorithm in a general case. This would help people take informed decisions while investing.40 Pagesen-USBrac 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 marketMachine LearningFinancePredictionDense NNRNNLSTMCNNBusiness enterprises -- Finance.Thesis