dc.contributor.advisor | Hossain, Muhammad Iqbal | |
dc.contributor.author | Ahmed, Tanjil | |
dc.contributor.author | Rahman, Salman | |
dc.contributor.author | Routh, Niloy | |
dc.contributor.author | Nirob, Eftakhar Alam | |
dc.date.accessioned | 2019-10-14T04:17:32Z | |
dc.date.available | 2019-10-14T04:17:32Z | |
dc.date.copyright | 2019 | |
dc.date.issued | 2019-08 | |
dc.identifier.other | ID 14101053 | |
dc.identifier.other | ID 15301021 | |
dc.identifier.other | ID 15301015 | |
dc.identifier.other | ID 15201036 | |
dc.identifier.uri | http://hdl.handle.net/10361/12781 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 34-35). | |
dc.description.abstract | For 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.statementofresponsibility | Salman Rahman | |
dc.description.statementofresponsibility | Tanjil Ahmed | |
dc.description.statementofresponsibility | Niloy Routh | |
dc.description.statementofresponsibility | Eftakhar Alam Nirob | |
dc.format.extent | 35 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 | Cross-selling | en_US |
dc.subject | Up-selling | en_US |
dc.subject | KNN | en_US |
dc.subject | fKNN | en_US |
dc.subject | SVD, | en_US |
dc.subject | Recommendation | en_US |
dc.subject | Forecasting | en_US |
dc.subject.lcsh | Machine learning | |
dc.title | Market sales prospecting by analyzing customer buying pattern using machine learning | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |