Öz
Airline 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.
Anahtar Kelimeler
Airline ticket price prediction Machine learning Ensemble methods Airfare forecasting Big data analytics
Kaynakça
- Korkmaz, H. (2024). Prediction of Airline Ticket Price Using Machine Learning Method. Journal of Transportation and Logistics. https://doi.org/10.26650/jtl.2024.1486696
- Iswarya, G. (2024). Predicting Airline Ticket Prices Using Machine Learning. International Journal of Scientific Research in Engineering and Management. https://doi.org/10.55041/ijsrem31185
- Kumar, C., & Ponnala, R. (2023). Leveraging Machine Learning Techniques to Estimate Airline Ticket Pricing. 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), 269-274. https://doi.org/10.1109/ICAICCIT60255.2023.10465724
- Jwala, C., Jahnavi, K., Mukthamu, K., Madhavi, D., & Lakshmi, J. (2024). An Ensemble Learning Method to Predict Airline Ticket Price Using Machine Learning. International Journal of Advanced Research in Science,
- Communication and Technology. https://doi.org/10.48175/ijarsct-16664
- Kalampokas, T., Tziridis, K., Kalampokas, N., Nikolaou, A., Vrochidou, E., & Papakostas, G. (2023). A Holistic Approach to Airfare Price Prediction Using Machine Learning Techniques. IEEE Access, 11, 46627-46643. https://doi.org/10.1109/ACCESS.2023.3274669
- Rajure, P. (2021). Prediction of Domestic Airline Tickets using Machine Learning. International Journal for Research in Applied Science and Engineering Technology, 9, 666-674. https://doi.org/10.22214/IJRASET.2021.35053
- Nagesh, P., Naidu, K., Kowshik, P., & Sekhar, P. (2023). Airline Ticket Price Prediction Model. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2023.49537
- Bollack, J., & Vincent, J. (2023). Using Different Machine Learning Algorithms to Predict the Prices of Flight Tickets. Journal of Student Research. https://doi.org/10.47611/jsrhs.v12i4.5303
- Alapati, N., Prasad, B., Sharma, A., Kumari, G., Veeneetha, S., Srivalli, N., Lakshmi, U., & Sahitya, D. (2022). Prediction of Flight Fare using machine learning. 2022 International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP), 134-138. https://doi.org/10.1109/ICFIRTP56122.2022.10059429
- Vaishnavi, K., Bindu, H., Satwika, M., Lakshmi, U., Harini, M., & Ashok, N. (2023). FLIGHT FARE PREDICTION USING MACHINE LEARNING. EPRA International Journal of Research & Development (IJRD). https://doi.org/10.36713/epra14763
- Flight Price Prediction. https://www.kaggle.com/datasets/shubhambathwal/flight-price-prediction. Access 14 May 2025.
Öz
Havayolu bileti fiyat tahmini, öncelikle havayolu fiyatlandırma stratejilerinin çok faktörlü ve zamana duyarlı doğası nedeniyle oldukça karmaşık ve dinamik bir zorluğu temsil eder. Bilet fiyatlarının doğru tahmini, hem tüketiciler için optimum satın alma kararlarını mümkün kılarak hem de havayolu şirketleri için veri odaklı gelir yönetimi ve dinamik fiyatlandırmayı destekleyerek önemli bir değer taşır. Bu çalışmada, büyük Hint metropol şehirleri arasındaki 300.000'den fazla uçuş seçeneği kaydını kapsayan "Ease My Trip" platformundan elde edilen büyük ölçekli bir uçuş rezervasyonu veri setinin kapsamlı bir analizini yürütüyoruz. Lineer Regresyon, CatBoost, LightGBM, Random Forest ve XGBoost dahil olmak üzere bir dizi gelişmiş makine öğrenimi algoritması, bilet fiyatlarını modellemek ve tahmin etmek için uygulandı. Bu modellerin karşılaştırmalı bir değerlendirmesi, özellikle XGBoost ve Random Forest olmak üzere topluluk ve artırma algoritmalarının üstün tahmin performansı sağladığını, XGBoost'un 0,98'lik bir R² ve 2.035,51$'lık bir ortalama mutlak hata (MAE) elde ettiğini ortaya koymaktadır. Bu bulgular, sağlam makine öğrenimi tekniklerinin kullanılmasının ve güvenilir uçak bileti fiyat tahmini için çeşitli özelliklerin dahil edilmesinin kritik önemini vurgulamaktadır. Sonuçlar, hem uygun maliyetli seyahat arayan yolcular hem de gelir yönetimi stratejilerini optimize etmeyi amaçlayan havayolu paydaşları için değerli içgörüler sunmaktadır.
Anahtar Kelimeler
Uçak bileti fiyat tahmini Makine öğrenimi Topluluk yöntemleri Uçak ücreti tahmini Büyük veri analitiği
Kaynakça
- Korkmaz, H. (2024). Prediction of Airline Ticket Price Using Machine Learning Method. Journal of Transportation and Logistics. https://doi.org/10.26650/jtl.2024.1486696
- Iswarya, G. (2024). Predicting Airline Ticket Prices Using Machine Learning. International Journal of Scientific Research in Engineering and Management. https://doi.org/10.55041/ijsrem31185
- Kumar, C., & Ponnala, R. (2023). Leveraging Machine Learning Techniques to Estimate Airline Ticket Pricing. 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), 269-274. https://doi.org/10.1109/ICAICCIT60255.2023.10465724
- Jwala, C., Jahnavi, K., Mukthamu, K., Madhavi, D., & Lakshmi, J. (2024). An Ensemble Learning Method to Predict Airline Ticket Price Using Machine Learning. International Journal of Advanced Research in Science,
- Communication and Technology. https://doi.org/10.48175/ijarsct-16664
- Kalampokas, T., Tziridis, K., Kalampokas, N., Nikolaou, A., Vrochidou, E., & Papakostas, G. (2023). A Holistic Approach to Airfare Price Prediction Using Machine Learning Techniques. IEEE Access, 11, 46627-46643. https://doi.org/10.1109/ACCESS.2023.3274669
- Rajure, P. (2021). Prediction of Domestic Airline Tickets using Machine Learning. International Journal for Research in Applied Science and Engineering Technology, 9, 666-674. https://doi.org/10.22214/IJRASET.2021.35053
- Nagesh, P., Naidu, K., Kowshik, P., & Sekhar, P. (2023). Airline Ticket Price Prediction Model. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2023.49537
- Bollack, J., & Vincent, J. (2023). Using Different Machine Learning Algorithms to Predict the Prices of Flight Tickets. Journal of Student Research. https://doi.org/10.47611/jsrhs.v12i4.5303
- Alapati, N., Prasad, B., Sharma, A., Kumari, G., Veeneetha, S., Srivalli, N., Lakshmi, U., & Sahitya, D. (2022). Prediction of Flight Fare using machine learning. 2022 International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP), 134-138. https://doi.org/10.1109/ICFIRTP56122.2022.10059429
- Vaishnavi, K., Bindu, H., Satwika, M., Lakshmi, U., Harini, M., & Ashok, N. (2023). FLIGHT FARE PREDICTION USING MACHINE LEARNING. EPRA International Journal of Research & Development (IJRD). https://doi.org/10.36713/epra14763
- Flight Price Prediction. https://www.kaggle.com/datasets/shubhambathwal/flight-price-prediction. Access 14 May 2025.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Yönetim Bilişim Sistemleri, Bilgi Sistemleri (Diğer), Para-Bankacılık, Sermaye Piyasaları |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Haziran 2025 |
| Gönderilme Tarihi | 13 Haziran 2025 |
| Kabul Tarihi | 14 Haziran 2025 |
| Yayımlandığı Sayı | Yıl 2025 |
Nişantaşı Üniversitesi kurumsal yayınıdır.