Majumdar, Mahbub AlamTabassum, ParisaHalder, Mita2018-12-042018-12-0420182018-08ID 14301009ID 14301012http://hdl.handle.net/10361/10958Cataloged from PDF version of thesis.Includes bibliographical references (pages 45-47).This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.In a financially volatile market, as the stock market, it is important to have a very precise prediction of a future trend. Because of the financial crisis and scoring profits, it is mandatory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires advanced algorithms of machine learning. The literature contains the stock price prediction algorithm by using Bayesian network. The network is determined from the daily stock price. The prediction error is evaluated from the daily stock price and its prediction. The present algorithm is applied for predicting Google, Procter & Gamble and General Motors stock price. The results of this study show that the algorithm is capable of predicting future stock price more accurately than a lot of another machine learning algorithm available so far.47 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 marketBayesian networkWard methodK2 algorithmBayesian statistical decision theory -- Data processing.Computers -- Enterprise applications -- Business intelligence tools.Thesis