dc.contributor.advisor | Rafi, Tahmid Ul Islam | |
dc.contributor.advisor | Mostakim, Moin | |
dc.contributor.author | Dhrubo, Najmus Sakib | |
dc.contributor.author | Islam, Md Samiul | |
dc.date.accessioned | 2021-09-14T08:29:13Z | |
dc.date.available | 2021-09-14T08:29:13Z | |
dc.date.copyright | 2021 | |
dc.date.issued | 2021-06 | |
dc.identifier.other | ID: 16101152 | ID |
dc.identifier.other | ID: 17101419 | |
dc.identifier.uri | http://hdl.handle.net/10361/15011 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (page 30). | |
dc.description.abstract | Programming is a very useful skill nowadays. Programming contests give people the
opportunity to increase their programming skills. By solving programming contest
problems contestants can increase not only their programming skills but also their
mathematical and algorithmic knowledge. The competitive programming problems
are presented in problem statements. Sometimes they are presented in the form
of a story or sometimes directly. To solve the problem contestants must read the
problem statement carefully. The problems can be of many categories. We have tried
to classify number theory and graph theory problems. At first, we collected data
from competitive programming problem statements. Then we used different machine
learning algorithms such as fully connected neural network, naive bayes classifier,
support vector machine on the data to predict if the category of the problem is
either number theory or graph theory. With such machine learning approaches we
achieved test accuracy of about 72%, 75% and 74%. | en_US |
dc.description.statementofresponsibility | Najmus Sakib Dhrubo | |
dc.description.statementofresponsibility | Md Samiul Islam | |
dc.format.extent | 30 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac 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. | |
dc.subject | Competitive Programming | en_US |
dc.subject | Number Theory | en_US |
dc.subject | Graph Theory | en_US |
dc.subject | Neural Network | en_US |
dc.subject | Naive Bayes Classifier | en_US |
dc.subject | Support Vector Machine | en_US |
dc.subject.lcsh | Competitive Programming | |
dc.title | Assistive Guideline of Categorization for Competitive Programming Problems | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |