Large-scale airline ticket price prediction using ensemble machine learning algorithms

Basit Öğe Kaydı
dc.authorid0000-0002-5916-4478
dc.authorid0000-0002-9407-1728
dc.authorid0000-0001-5160-7687
dc.authorid0000-0003-2995-5188
dc.contributor.authorErtürk, Muzaffer
dc.contributor.authorEmeç, Murat
dc.contributor.authorAtılgan Sarıdoğan, Ayşe
dc.contributor.authorKüçükgergerli, Nabi
dc.date.accessioned2026-06-01T13:59:17Z
dc.date.available2026-06-01T13:59:17Z
dc.date.issued2025
dc.departmentFakülteler, İktisadi, İdari ve Sosyal Bilimler Fakültesi, İşletme Bölümü
dc.description.abstractAirline ticket price prediction represents a highly complex and dynamic challenge, primarily due to the multifactorial and time-sensitive nature of airline pricing strategies. Accurate forecasting of ticket prices holds substantial value for both consumers, by enabling optimal purchase decisions, and airline companies, by supporting data-driven revenue management and dynamic pricing. In this study, we conduct a comprehensive analysis of a large-scale flight booking dataset obtained from the “Ease My Trip” platform, encompassing over 300,000 records of flight options between major Indian metropolitan cities. A suite of advanced machine learning algorithms, including Linear Regression, CatBoost, LightGBM, Random Forest, and XGBoost, was implemented to model and predict ticket prices. A comparative evaluation of these models reveals that ensemble and boosting algorithms, particularly XGBoost and Random Forest, deliver superior predictive performance, with XGBoost achieving an R² of 0.98 and a mean absolute error (MAE) of $2,035.51. These findings underscore the critical importance of employing robust machine learning techniques and incorporating a diverse set of features for reliable airline ticket price prediction. The results offer valuable insights for both passengers seeking cost-effective travel and airline stakeholders aiming to optimise revenue management strategies.
dc.identifier.citationErtürk, M., Emeç, M., Atılgan Sarıdoğan, A., & Küçükgergerli, N. (2025). Large-scale airline ticket price prediction using ensemble machine learning algorithms. İstanbul Nişantaşı Üniversitesi Sosyal Bilimler Dergisi, 13(1), pp. 436-446. https://doi.org/10.52122/nisantasisbd.1719245
dc.identifier.doi10.52122/nisantasisbd.1719245
dc.identifier.endpage446
dc.identifier.issn2147-5121
dc.identifier.issn2717-7610
dc.identifier.issue1
dc.identifier.startpage436
dc.identifier.trdizinid1330933
dc.identifier.urihttps://doi.org/10.52122/nisantasisbd.1719245
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1492
dc.identifier.volume13
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorKüçükgergerli, Nabi
dc.institutionauthorid0000-0003-2995-5188
dc.language.isoen
dc.publisherŞerafettin Sevgili
dc.relation.ispartofİstanbul Nişantaşı Üniversitesi Sosyal Bilimler Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAirline Ticket Price Prediction
dc.subjectMachine Learning
dc.subjectEnsemble Methods
dc.subjectAirfare Forecasting
dc.subjectBig Data Analytics
dc.subjectUçak Bileti Fiyat Tahmini
dc.subjectMakine Öğrenimi
dc.subjectTopluluk Yöntemleri
dc.subjectUçak Ücreti Tahmini
dc.subjectBüyük Veri
dc.titleLarge-scale airline ticket price prediction using ensemble machine learning algorithms
dc.title.alternativeTopluluk makine öğrenimi algoritmaları kullanarak büyük ölçekli havayolu bilet fiyatı tahmini
dc.typeArticle
dspace.entity.typePublication

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Kapalı Erişim
İsim:
Tam Metin / Full Text.pdf
Boyut:
3.04 MB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Kapalı Erişim
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

İşletme Bölümü Koleksiyonu
TR Dizin İndeksli Yayınlar Koleksiyonu