Determining Airline Customer Satisfaction with Ensemble Learning Methods

Year 2022, , 2763 - 2774, 30.09.2022

Uğur Ercan

https://doi.org/10.29023/alanyaakademik.1095574

Cited By: 1

Abstract

Estimating the customers who aren’t satisfied with their travels in air transportation is extremely important in terms of structuring the companies themselves and managing the revenues. In this study, it’s aimed to predict neutral or dissatisfied customers with ensemble learning methods by using data compiled from airlines in the U.S. In the modeling phase, Random Forest, Gradient Boosting, and XGBoosting methods, which are current machine learning methods that produce high estimation accuracy in classification problems, were used. The best accuracy obtained was 96.4%, while the best Specificity and Negative Prediction Rate values were 97.7% and 96%, respectively. The high Specificity, Negative Prediction Rate, and Accuracy values obtained from the model results show that machine learning methods can be used to predict whether customers will reuse airline companies in airline transportation.

Keywords

Airline customer satisfaction, random forest, Gradient Boosting, XGBoost, airline transportation

Havayolu Taşımacılığında Müşteri Memnuniyetinin Topluluk Öğrenmesi Yöntemleri ile Belirlenmesi

Abstract

Havayolu taşımacılığında seyahatinden memnun olmayan müşterilerin tahmin edilmesi firmaların kendilerini yapılandırması ve gelirlerinin yönetilmesi açısından son derece önemlidir. Gerçekleştirilen çalışmada Amerika Birleşik Devletleri’ndeki havayollarından derlenen veriler kullanılarak uçuş seyahatinden nötr ya da memnun olmayan müşterilerin topluluk öğrenmesi yöntemleriyle tahmin edilmesi amaçlanmıştır. Modelleme aşamasında sınıflandırma problemlerinde yüksek tahmin doğruluğu üreten ve güncel makine öğrenmesi yöntemlerinden Rastgele Orman, Gradient Boosting ve XGBoost yöntemleri kullanılmıştır. Elde edilen en iyi doğruluk oranı %96,4 iken en iyi Özgüllük ve Negatif Tahmin Oranı değerleri sırasıyla %97,7 ve %96’dır. Model sonuçlarından elde edilen yüksek Özgüllük, Negatif Tahmin Oranı ve Doğruluk değerleri makine öğrenmesi yöntemlerinin havayolu taşımacılığında müşterilerin havayolu firmasını tekrar kullanıp kullanmayacağı tahmin işlemlerinde kullanılabileceğini göstermektedir.

Keywords

Uçuş memnuniyeti, makine öğrenmesi, havayolu taşımacılığı

References

• Abdi, A. M. (2020). “Land Cover And Land Use Classification Performance of Machine Learning Algorithms In A Boreal Landscape Using Sentinel-2 Data”, GIScience & Remote Sensing, 57(1), 1-20.
• Agarwal, I., & Gowda, K. R. (2021). “The Effect of Airline Service Quality on Customer Satisfaction And Loyalty In India”. Materials Today: Proceedings, 37, 1341-1348.
• Al Daoud, E. (2019). “Comparison Between Xgboost, Lightgbm And Catboost Using A Home Credit Dataset”, International Journal of Computer and Information Engineering, 13(1), 6-10.
• Alpaydin, E. (2010). Introduction To Machine Learning (second edition), The MIT Press Cambridge, Massachusetts, London, England.
• An, M., & Noh, Y. (2009). “Airline Customer Satisfaction And Loyalty: Impact of In-Flight Service Quality”, Service Business, 3, 293-307.
• Ataseven, B. (2013). “Yapay Sinir Ağları İle Öngörü Modellemesi”, Öneri Dergisi, 10 (39), 101-115.
• Anggraina, A., Primartha, R., & Wijaya, A. (2019). “The Combination of Logistic Regression And Gradient Boost Tree For Email Spam Detection”, In Journal of Physics: Conference Series (Vol. 1196, No. 1, p. 012013). IOP Publishing.
• Baydogan, C., & Alatas, B. (2019). “Detection of Customer Satisfaction on Unbalanced and Multi-Class Data Using Machine Learning Algorithms”, In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1-5). IEEE.
• Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
• Çelikkol, E. S., Uçkun, C. G., Tekin, V. N., & Çelikkol, Ş. (2012). “Türkiye’de İç Hatlardaki Havayolu Taşımacılığında Müşteri Tercihi ve Memnuniyetini Etkileyen Faktörlere Yönelik Bir Araştırma”, İşletme Araştırmaları Dergisi, 4(3), 70-81.
• DPT, Devlet Planlama Teşkilatı (2001). “Sekizinci Beş Yıllık Kalkınma Planı, Ulaştırma Özel İhtisas Komisyonu Raporu Hava Yolu Ulaştırma Alt Komisyon Raporu”, DPT:2584, ÖİK:596, Ankara.
• Duman, S., Elewi, A., & Yetgin, Z. (2022). “Distance Estimation From a Monocular Camera Using Face and Body Features”, Arabian Journal for Science and Engineering, 47(2), 1547-1557.
• Ecer, O., Yetgin, Z., & Celik, T. (2018). “Air Write Letter Recognition Using Random Forest Classification On Arduino Dataset”, International Journal of Scientific and Technological Research, 4(7), 1-9.
• Ercan, U. (2021). “İnternetten Alışveriş Yapan Hanelerin Rastgele Orman Yöntemiyle Tahmin Edilmesi”, Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 12(24), 728-752.
• Flores, V., & Keith, B. (2019). “Gradient Boosted Trees Predictive Models For Surface Roughness In High-Speed Milling In The Steel And Aluminum Metalworking İndustry”, Complexity,
• Friedman, J. H. (2001). “Greedy Function Approximation: A Gradient Boosting Machine”, Annals of statistics, 1189-1232.
• Giao, H. N. K. (2017). “Customer Satisfaction of Vietnam Airline Domestic Service Quality”, International Journal of Quality Innovation. 3(10), 1-11.
• Hussain, R., Al Nasser, A., & Hussain, Y. K. (2015). “Service Quality And Customer Satisfaction Of A UAE-Based Airline: An empirical investigation”, Journal of Air Transport Management, 42, 167-175.
• Hwang, S., Kim, J., Park, E., & Kwon, S. J. (2020). “Who Will Be Your Next Customer: A Machine Learning Approach To Customer Return Visits In Airline Services”, Journal of Business Research, 121, 121-126.
• Jiang, H., & Zhang, Y. (2016). “An Investigation of Service Quality, Customer Satisfaction And Loyalty In China's Airline Market”, Journal of Air Transport Management, 57, 80-88.
• Kaggle, Airline Passenger Satisfaction, https://www.kaggle.com/datasets/teejmahal20/airline-passenger-satisfaction.
• Kumar, Y. J. N., Spandana, V., Vaishnavi, V. S., Neha, K., & Devi, V. G. R. R. (2020). “Supervised Machine Learning Approach For Crop Yield Prediction In Agriculture Sector”, In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 736-741). IEEE.
• Kumar, S., & Zymbler, M. (2019). “A Machine Learning Approach To Analyze Customer Satisfaction From Airline Tweets”, Journal of Big Data, 6, 1-16.
• Kumar, G., Yadav, S. S., Yogita, Y., & Pal, V. (2022). “Machine Learning Based Framework to Predict Finger Movement for Prosthetic Hand”, IEEE Sensors Letters.
• Küçük Çırpın, B., & Kurt, D. (2016). “Havayolu Taşımacılığında Hizmet Kalitesi Ölçümü”, Journal of Transportation and Logistics, 1(1), 83-98.
• Mariescu-Istodor, R., & Jormanainen, I. (2019). “Machine Learning For High School Students”, In Proceedings of the 19th Koli Calling International Conference On Computing Education Research (pp. 1-9).
• Noviantoro, T., & Huang, J. P. (2021). “Investigating Airline Passenger Satisfaction: Data Mining Method”, Research in Transportation Business & Management, 100726.
• Oshiro, T. M., Perez, P. S., & Baranauskas, J. A. (2012). “How many trees in a random forest?”, International Workshop on Machine Learning and Data Mining in Pattern Recognition in (pp. 154-168). Springer-Verlag, Berlin, Heidelberg
• Pal, M. (2005). “Random Forest Classifier For Remote Sensing Classification”, International journal of remote sensing, 26(1), 217-222.
• Ponraj, A. S., & Vigneswaran, T. (2020). “Daily Evapotranspiration Prediction Using Gradient Boost Regression Model For İrrigation Planning”, The Journal of Supercomputing, 76(8), 5732-5744.
• Rajula, H. S. R., Verlato, G., Manchia, M., Antonucci, N., & Fanos, V. (2020). “Comparison Of Conventional Statistical Methods With Machine Learning In Medicine: Diagnosis, Drug Development, and Treatment”, Medicina, 56(9), 455.
• Rumora, L., Miler, M., & Medak, D. (2020). “Impact of various atmospheric corrections on sentinel-2 land cover classification accuracy using machine learning classifiers”, ISPRS International Journal of Geo-Information, 9(4), 277.
• Topal, B., Şahin, H., & Topal, B. (2019). “Havayolu İle Yolcu Taşımacılığında Müşteri Memnuniyetini Etkileyen Faktörlerin Belirlenmesi: İstanbul Hava Limanları Örneği”, Balkan Sosyal Bilimler Dergisi, 8(16), 119-128.
• Üstüner, M., Abdikan, S., Bilgin, G., & Şanlı, F. B. (2020). “Hafif Gradyan Artırma Makineleri ile Tarımsal Ürünlerin Sınıflandırılması”, Turkish Journal of Remote Sensing and GIS, 1(2), 97-105.
• Zhang, L., Liu, Z., Ren, T., Liu, D., Ma, Z., Tong, L. & Li, S. (2020). “Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier”, Remote Sensing, 12(3), 362.
• Vinod, B. (2021). “Artificial Intelligence in Travel”, Journal of Revenue and Pricing Management, 20(3), 368-375.