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Parking data analysis

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorShakil, Arif
dc.contributor.advisorSadeque, Farig Yousuf
dc.contributor.authorAhamed, Kasfi
dc.contributor.authorHaque, Fardin
dc.contributor.authorRasheed, Ahnaf Ar
dc.contributor.authorRahman, Md. Arban
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2023-08-06T04:35:53Z
dc.date.available2023-08-06T04:35:53Z
dc.date.copyright2023
dc.date.issued2023-03
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 55-56).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.en_US
dc.description.abstractCar parking is a hassle for everyone in one word, but when it is in a country where population is an issue, parking problems can easily stress someone out. Car parking is a key contributor to traffic congestion and has been, and continues to be, a big concern as car sizes grow in the luxury category, limiting parking spots in metropolitan areas. The problem of a shortage of parking spaces is becoming more acute as the number of automobiles on the road grows rapidly across the world. Many countries’ issues will increase without a planned and convenient withdrawal from the vehicle as the world’s population continues to urbanize. Because it is impossible to handle the growing number of automobiles in a proper, comfortable manner with the present unmanaged car parks and transit amenities, an efficient and smart parking system is required. Here, An examination will be undertaken on a dataset of parking occurrences in this Learning Analytics Visualization project. This analysis will be able to answer some of the most crucial questions that may ease the parking problems. The use of parking spots will increase as a result of the implementation of this system.en_US
dc.description.degreeBachelor of Science in Computer Science and Engineering
dc.description.statementofresponsibilityKasfi Ahamed
dc.description.statementofresponsibilityFardin Haque
dc.description.statementofresponsibilityAhnaf Ar Rasheed
dc.description.statementofresponsibilityMd. Arban Rahman
dc.format.extent56 pages
dc.identifier.otherID 19101094
dc.identifier.otherID 19101333
dc.identifier.otherID 23141079
dc.identifier.otherID 19101106
dc.identifier.urihttp://hdl.handle.net/10361/19289
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.subjectPrediction modelen_US
dc.subjectKNNen_US
dc.subjectDecision treeen_US
dc.subjectForecast modelen_US
dc.subjectRandom foresten_US
dc.subjectNaive Bayes Classifieren_US
dc.subjectSupport Vector Machineen_US
dc.subjectBangladesh parking locationen_US
dc.subjectRaw data seten_US
dc.subject.lcshMachine learning
dc.titleParking data analysisen_US
dc.typeThesisen_US

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