Prediction of Customer Satisfaction in Airline Hospitality services for improved service delivery using Support Vector Machine

  • Adedayo Sobowale
  • Olukemi Osadare
  • Afeez A. Soladoye Federal university Oye-Ekiti
  • Peace O. Sobowale


The recent entry of Air peace airline service to London and competition resulting from this with other airlines showcased the trending competitiveness in airline industry which might affect the patronage of an airline over others. In line of this, ensuring customer’s satisfaction would enable business growth, sustainability and provision of improved service delivery. Gauging customer satisfaction would help in knowing steps to take to ensure better service delivery but employing traditional approach might be subjected to travel experience, time consumption, inaccurate which would hinder its objective. However, application of Machine learning techniques would give a faster, accurate and concise prediction resulting from historical customer’s reviews. This study aims to employing Support Vector Machine a traditional machine learning classifier for prediction of airline customer’s satisfaction in hospitality services employing an open access dataset comprising of different attributes like age, gender, flight distance among others. This dataset was pre-processed using various pre-processing techniques like normalization using standard scalar, transformation using label encoder, removal of missing values, feature selection using forward-backward feature selection techniques. The well pre-processed dataset was implemented using the SVM classifier with its three kernels for comparative analysis. The study used hold-out evaluation method with 80-20 split and gave average accuracies with Rbf, Linear and poly kernels of 93, 85 and 90% respectively. This study showed that the RBF kernel of SVM gave the best predictive performance with average accuracy, precision and F1-score of 93, 93 and 92% respectively with its good predictive airlines can identify the major factors that contribute to customer’s satisfaction to prioritize to improve service delivery and optimization of staffing at during strategic period. This would enhance customer’s satisfaction and ensure improved good service delivery.  


ockhorst, J., Yu, S., Polania, L and Fung G. (2017). “Predicting self-reported customer satisfaction of interactions with a corporate call center” Y.Altun et al. (Eds): ECML PKDD 2017, part III, LNAI 10536, pp.179-190

Chan, K. T., Kwong, C. K. and Kremer, G. E. (2020). “Predicting customer satisfaction based on online reviews and hybrid ensemble genetic programming algorithms” Engineering Applications of Artificial Intelligence, 95

Lee, m., Kwon, W. and Back, K (2021). “Artificial intelligence for hospitality big data analytics: developing prediction model of restaurant review helpfulness for customer decision-making” International Journal of Contemporary Hospitality, 33(6), pp. 2117-2136

Lucini. F. R., Tonetto, L. M., Fogliatto, F. S. and Anzanello, M. J. (2020) “Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews” Journal of Air transport Management, 83

Oh, S., Ji, H., Kim, J., Park, E. and P.del, A. (2022). “Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service” Information Technology and Tourism, 24, pp.109-126

Qin Q, Zhou X. And Jiang Y. (2021) "Prognosis Prediction of Stroke based on Machine Learning and Explanation Model" International Journal of Computers Communications & Control, 16(2).

Sarp, G and Ozcelik, M. (2017) “Water body extraction and change detection using time series: A case study lake Burdur, Turkey” Journal of Taibah University of Science. 1,(1) 391-391

Zhao, Y., Xu, X. and Wang, M. (2019).”Predicting Overall Customer satisfaction: Big data evidence from hotel online textual reviews.” International Journal of Hospitality Management. 76, pp. 111-121