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dc.contributor.advisorHossain, Muhammad Iqbal
dc.contributor.authorAhmed, Tanjil
dc.contributor.authorRahman, Salman
dc.contributor.authorRouth, Niloy
dc.contributor.authorNirob, Eftakhar Alam
dc.date.accessioned2019-10-14T04:17:32Z
dc.date.available2019-10-14T04:17:32Z
dc.date.copyright2019
dc.date.issued2019-08
dc.identifier.otherID 14101053
dc.identifier.otherID 15301021
dc.identifier.otherID 15301015
dc.identifier.otherID 15201036
dc.identifier.urihttp://hdl.handle.net/10361/12781
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 34-35).
dc.description.abstractFor improving business, the organization needs to analyze the buying pattern of their customer to keep track of all products that are being sold the most so that they could keep the stocks of those products and remove those which sells the least. Analyz- ing cross-selling, up-selling of products is one of the major issues for identifying the buying frequency pattern. We have proposed a machine learning-based model to rec- ommend and predict through K-Nearest Neighbor (KNN), fuzzy KNN (fKNN) Single Value Decomposition (SVD) algorithm to compare the best outcome. Our proposed recommendation model can be used to get an idea of which products need to keep more on their shelves and which products not for the convenience of the customers. Moreover, our proposed predictive model will help to forecast future pro tability with which business developer will be better equipped to see the bigger picture. So, we are hopeful our model will help to serve the interest of all the stakeholders.en_US
dc.description.statementofresponsibilitySalman Rahman
dc.description.statementofresponsibilityTanjil Ahmed
dc.description.statementofresponsibilityNiloy Routh
dc.description.statementofresponsibilityEftakhar Alam Nirob
dc.format.extent35 pages
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.subjectCross-sellingen_US
dc.subjectUp-sellingen_US
dc.subjectKNNen_US
dc.subjectfKNNen_US
dc.subjectSVD,en_US
dc.subjectRecommendationen_US
dc.subjectForecastingen_US
dc.subject.lcshMachine learning
dc.titleMarket sales prospecting by analyzing customer buying pattern using machine learningen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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