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dc.contributor.advisorChakrabarty, Amitabha
dc.contributor.authorQudar, Mohiuddin Md. Abdul
dc.contributor.authorTazrin, Tahrat
dc.contributor.authorKhanam, Kazi Zainab
dc.date.accessioned2018-12-04T08:07:30Z
dc.date.available2018-12-04T08:07:30Z
dc.date.copyright2018
dc.date.issued2018
dc.identifier.otherID 15101014
dc.identifier.otherID 15101131
dc.identifier.otherID 15101119
dc.identifier.urihttp://hdl.handle.net/10361/10961
dc.descriptionThis thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 42-45).
dc.description.abstractFor business booming, in recent years the idea of find an ideal location for reaching potential customers has been the focus of past research. Traditional approaches have faced many negative responses, now to make business profitable the new marketing strategy is live campaigns. With the growth of location-based social networks collecting data of user mobility and popularity of places has recently become attainable, but not without analyzing to find the optimal location and time for live campaigns with greater accuracy. In this paper, we study the predictive power of various machine learning and mining features on finding suitable location for live campaigns through the use of a dataset collected from Foursquare in New York. We selected 10 candidate areas where the data was preprocessed according to the feature, a score is computed on the candidate areas to do live campaigns based on the features using most suitable algorithm with the accuracy. The results with Random Forest and Decision Tree are shown at the end of the report. Lastly, our proposed model shows how performance varies when using different features and predicting the suitable locations for live campaigns. We achieve 88.25% accuracy in Decision Tree regression model and an accuracy of 88.48% and 70.04% in Support Vector Machine (SVM) and Random Forest respectively.en_US
dc.description.statementofresponsibilityTahrat Tazrin
dc.description.statementofresponsibilityMohiuddin Md Abdul Qudar
dc.description.statementofresponsibilityKazi Zainab Khanam
dc.format.extent45 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.subjectLBSNen_US
dc.subjectLive campaignsen_US
dc.subjectLocation, algorithmsen_US
dc.subjectSVMen_US
dc.subjectRandom foresten_US
dc.subjectDecision treeen_US
dc.subject.lcshMachine learning -- Mathematical models.
dc.titleFinding suitable locations for live campaigns using different machine learning techniquesen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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