2015

Havayolu Taşımacılığında Müşteri Memnuniyetinin Topluluk Öğrenmesi Yöntemleri ile Belirlenmesi

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.

Anahtar Kelimeler:

Uçuş memnuniyeti, makine öğrenmesi, havayolu taşımacılığı

Determining Airline Customer Satisfaction with Ensemble Learning Methods

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,

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