Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7478
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dc.contributor.advisorAyav, Tolga
dc.contributor.authorGürakın, Çağrı-
dc.date.accessioned2019-12-13T11:40:51Z
dc.date.available2019-12-13T11:40:51Z
dc.date.issued2019-07en_US
dc.identifier.citationGürakın, Ç., (2019). A learning-based demand classification service with using XGBoost in institutional area. Unpublished master's thesis, İzmir Institute of Technology, İzmir, Turkeyen_US
dc.identifier.urihttps://hdl.handle.net/11147/7478
dc.descriptionThesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2019en_US
dc.descriptionIncludes bibliographical references (leaves: 48-49)en_US
dc.descriptionText in English; Abstract: Turkish and Englishen_US
dc.description.abstractThis study, purposes to explain the development stages and methodology of data classification service that has a text-based adaptable programming interface. One of the successful classification algorithms, XGBoost, was preferred in the study. The dataset that is used in the study obtained by 'Digital Business Tracking Application' of a name anonymized company. The dataset is tested by using different classification algorithms and detailed performance evaluation was conducted. As a result, highest accuracy rate is obtained with 'Data Classification Service' which was developed by using XGBoost algorithm.en_US
dc.description.abstractBu çalışma, metin-tabanlı, uyarlanabilir bir programlama arayüzüne sahip; veri sınıflandırma servisi geliştirme aşamalarını ve çalışmada takip edilen metodolojiyi konu alır. Çalışmada, başarılı sınıflandırma algoritmalarından biri olan XGBoost tercih edilmiştir. Çalışmada kullandığımız veri kümesi, bilgilerini anonimleştirdiğimiz bir şirketin; 'Dijital İş Takip Uygulaması' aracılığı ile elde edilmiştir. Veri seti farklı sınıflandırma algoritmaları ile de test edilmiş ve detaylı performans değerlendirmeleri yapılmıştır. Sonuç olarak, testlerimizde en yüksek doğruluk oranı, XGBoost algoritması ile geliştirdiğimiz veri sınıflandırma servisi ile elde edildi.en_US
dc.format.extentxii, 49 leavesen_US
dc.language.isoenen_US
dc.publisherIzmir Institute of Technologyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectXGBoosten_US
dc.subjectNatural language processingen_US
dc.subjectSupervised learningen_US
dc.subjectMachine learningen_US
dc.subjectMultinomial classificationen_US
dc.titleA learning-based demand classification service with using XGBoost in institutional areaen_US
dc.title.alternativeKurumsal alanda XGBoost ile öğrenme-tabanlı talep sınıflandırma servisien_US
dc.typeMaster Thesisen_US
dc.institutionauthorGürakın, Çağrı-
dc.departmentThesis (Master)--İzmir Institute of Technology, Computer Engineeringen_US
dc.relation.publicationcategoryTezen_US
item.openairetypeMaster Thesis-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
Appears in Collections:Master Degree / Yüksek Lisans Tezleri
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