Rahman, TanvirSadman, Syed HassanRahat, Hasib AlHamim, Atikur RahmanAl Kafi, Sultan Gias UddinKhan, Nasik Ali2021-09-092021-09-0920212021-06ID 17101238ID 17101017ID 17101089ID 17301203ID 20301459http://hdl.handle.net/10361/14991Cataloged from PDF version of thesis.Includes bibliographical references (page 34).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.The stock market plays an important role in the growth of industries by supplying funding. Thousands of Bangladeshis use the stock market as a means of employment. The stock markets in Bangladesh have been declining lately, impacting millions of individuals. What if stock markets could allow investors to know which stock is more reliable or less dependable. We will be using the Linear Regression algorithms along with the K-Nearest Neighbors algorithm, the Support Vector Machine (SVM), Lasso Regression and Multi-Linear Regression to predict the stocks.34 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.Data MiningMachine LearningStock MarketPredictionLinear Regression AnalysisK-Nearest NeighbourSupport Vector MachineLassoAnalysisMarket share--BangladeshThesis