Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7478
Title: A learning-based demand classification service with using XGBoost in institutional area
Other Titles: Kurumsal alanda XGBoost ile öğrenme-tabanlı talep sınıflandırma servisi
Authors: Ayav, Tolga
Gürakın, Çağrı
Keywords: XGBoost
Natural language processing
Supervised learning
Machine learning
Multinomial classification
Issue Date: Jul-2019
Publisher: Izmir Institute of Technology
Source: Gürakın, Ç., (2019). A learning-based demand classification service with using XGBoost in institutional area. Unpublished master's thesis, İzmir Institute of Technology, İzmir, Turkey
Abstract: This 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.
Bu 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.
Description: Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2019
Includes bibliographical references (leaves: 48-49)
Text in English; Abstract: Turkish and English
URI: https://hdl.handle.net/11147/7478
Appears in Collections:Master Degree / Yüksek Lisans Tezleri

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