Chakrabarty, AmitabhaAlam, Md. Golam RabiulMahmud, Abdullah AlNoor, Jannat-EReshad, Sadman AlamFuad, Syed Nafis2021-09-062021-09-0620212021-06ID 17301033ID 17101021ID 17101403ID 17101250http://hdl.handle.net/10361/14976Cataloged from PDF version of thesis.Includes bibliographical references (pages 20-21).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.Text Documents often contain valuable data. But not all data is relevant. That is why extracting relevant data from text documents is an essential task. Extracting relevant data from text documents refers to the study of classifying text documents into such groups that describe the contents of documents. There are many methods to find out relevant data from a cluster of text or a text document. Classifying extensive textual data helps to organize the records better, make the search easier and relevant and simplify navigation. That makes this task still an open research issue. This paper uses three techniques of classifying text documents: convolution neural networks (CNN) with deep learning, Gaussian Na¨ıve Bayes and support vector machines (SVM). With these three algorithms, the text we want to classify goes through three layers of checks. So, it gives us more reliability.22 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.CNNSVMGaussian Na¨ıve BayesText classificationMachine learningThesis