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A counseling system to predict the study path for freshmen

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorMajumdar, Mahbub
dc.contributor.authorUsha, Rowshni Tasneem
dc.contributor.authorParvez, Shiny Raisa
dc.contributor.authorSejuti, Fariha Sazid
dc.contributor.authorHossain, Maisha
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2019-06-30T06:51:03Z
dc.date.available2019-06-30T06:51:03Z
dc.date.copyright2019
dc.date.issued2019-04
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 60-61).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.en_US
dc.description.abstractNow a days, dilemma related to one's career has been considered as a serious issue, specially among fresh graduates. Starting at the age of 18, the students usually fail to grasp the idea of which career path to pursue as they lack maturity and experience on the matter. Moreover, students su er greatly in deciding which faculty would result the highest bene t for them due to the insu ciency of counselors in the pre-university education. The students do not have the su cient knowledge to make themselves aware of the real life career related challenges, which is supported by academic majors. It is crucial for a student to make the proper decision on the matter of their career in order to avoid consequences that may be the result of wrong career selection. As a result, selecting an proper career with highest bene t has become one of the most di cult as well as challenging task for the students because wrong career selection may lead to a work eld which was not meant for them. This paper presents a counselling system to predict study path for the freshmen by analyzing necessary attributes such as skills, interests, values and motivation, academic background. Moreover, the proposed freshmen counseling system helps the freshmen in their career choice as well as guides toward their respective appropriate career for future. We have used several di erent approaches for modeling and prediction such as Decision tree classi er, Random Forest, SVM, K-Nearest-Neighbors Classi ers etc. and di erentiated between the resulting precision scores. The results were also cross-checked which determined the best parameters that is responsible for providing highest accuracy scores. Furthermore, some ranking algorithms were used to generate a ranked output for the student counseling system. In this paper, we have separated our work into di erent parts. Chapter 1 contains the overall idea about our work. Chapter 2 contains Related Works, followed by Chapter 3, where we have mentioned about the methodologies which we have used for the system. After that Chapter 4 contains the implementation of the system. Next in Chapter 5 result analysis has been mentioned. Lastly, Chapter 6 ended with conclusion, our limitations and future scopes of improvements related to our work.en_US
dc.description.degreeBachelor of Science in Computer Science and Engineering
dc.description.statementofresponsibilityRowshni Tasneem Usha
dc.description.statementofresponsibilityShiny Raisa Parvez
dc.description.statementofresponsibilityFariha Sazid Sejuti
dc.description.statementofresponsibilityMaisha Hossain
dc.format.extent61 pages
dc.identifier.otherID 15301082
dc.identifier.otherID 15301047
dc.identifier.otherID 15101027
dc.identifier.otherID 15301096
dc.identifier.urihttp://hdl.handle.net/10361/12276
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBrac 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.subjectMachine learningen_US
dc.subjectPredictionen_US
dc.subjectDecision treeen_US
dc.subjectRandom foresten_US
dc.subjectRanking algorithmen_US
dc.subjectMajor selectionen_US
dc.subjectAHPen_US
dc.subject.lcshMachine learning
dc.titleA counseling system to predict the study path for freshmenen_US
dc.typeThesisen_US

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