Abstract
The customer’s experience is one of the most critical concerns for the airline industry. This study investigates customer satisfaction in the airline industry through machine learning analysis, employing decision trees, random forests, support vector machines, and XGBoost models to predict customer satisfaction levels. Each model offers a unique approach to analyzing and predicting customer satisfaction levels, contributing to a comprehensive evaluation of their effectiveness. The data undergoes exploratory data analysis (EDA) and preprocessing, enabling quality assessment. XGBoost consistently outperforms other models in predictive accuracy. Key metrics, including accuracy, precision, recall, F1 score, and ROC AUC, are employed to evaluate model performance. The XGBoost model was identified as being the best among the four models tested (accuracy: 0.9576, precision: 0.9683, recall: 0.9528, F1 score: 0.9574, ROC AUC score: 0.9940). These findings contribute valuable insights into improving airline customer satisfaction and inform decision-making processes within the industry.
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References
Ramanathan, U., Subramanian, N., Parrott, G.: Role of social media in retail network operations and markseting to enhance customer satisfaction. Int. J. Oper. Prod. Manag. 37(1), 105–123 (2017)
Reichheld, F.F.: The one number you need to grow. Harv. Bus. Rev. 81(12), 46–55 (2003)
Psychogios, A.G., Tsironis, L.K.: Towards an integrated framework for Lean Six Sigma application: lessons from the airline industry. Total Qual. Manag. Bus. Excell. 23(3–4), 397–415 (2012)
Yirakpoa, B.S., Nwanyanwu, M.: Capstone project: marketing-airplane passenger satisfaction prediction using machine learning techniques. West Afr. J. Ind. Acad. Res. 23(2), 39–61 (2022)
Park, S.-H., Kim, M.-Y., Kim, Y.-J., Park, Y.-H.: A deep learning approach to analyze airline customer propensities: the case of South Korea. Appl. Sci. 12(4), 1916 (2022)
Rane, A., Kumar, A.: Sentiment classification system of twitter data for US airline service analysis. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 1, pp. 769–773. IEEE (2018)
Dehkharghani, R., Mercan, H., Javeed, A., Saygin, Y.: Sentimental causal rule discovery from Twitter. Expert Syst. Appl. 41(10), 4950–4958 (2014)
Jana, S.: Airline Customer satisfaction. Kaggle. https://www.kaggle.com/datasets/sjleshrac/airlines-customer-satisfaction
Priyam, A., Abhijeeta, G.R., Rathee, A., Srivastava, S.: Comparative analysis of decision tree classification algorithms. Int. J. Curr. Eng. Technol. 3(2), 334–337 (2013)
Ali, J., Khan, R., Ahmad, N., Maqsood, I.: Random forests and decision trees. Int. J. Comput. Sci. Issues (IJCSI) 9(5), 272 (2012)
Zhou, X., Lu, P., Zheng, Z., Tolliver, D., Keramati, A.: Accident prediction accuracy assessment for highway-rail grade crossings using random forest algorithm compared with decision tree. Reliab. Eng. Syst. Saf. 200, 106931 (2020)
Zhu, X., Chu, J., Wang, K., Wu, S., Yan, W., Chiam, K.: Prediction of rockhead using a hybrid N-XGBoost machine learning framework. J. Rock Mech. Geotech. Eng. 13(6), 1231–1245 (2021)
Awad, M., Khanna, R., Awad, M., Khanna, R.: Support vector machines for classification. In: Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, pp. 39–66 (2015)
Ibrahim, A.O., Shamsuddin, S.M., Abraham, A., Qasem, S.N.: Adaptive memetic method of multi-objective genetic evolutionary algorithm for backpropagation neural network. Neural Comput. Appl. 31, 4945–4962 (2019)
Azam, N.N.M., Ismail, M.A., Mohamad, M.S., Ibrahim, A.O., Jeba, S.: Classification of COVID-19 symptoms using multilayer perceptron. Iraqi J. Comput. Sci. Math. 4(4), 100–110 (2023)
Ibrahim, A.O., et al.: Classification of mammogram images using radial basis function neural network. In: Saeed, F., Mohammed, F., Gazem, N. (eds.) IRICT 2019. AISC, vol. 1073, pp. 311–320. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33582-3_30
Clemes, M.D., Gan, C., Kao, T.-H., Choong, M.: An empirical analysis of customer satisfaction in international air travel. Innov. Market. 4(2), 1–15 (2008)
Amalia, S., Deborah, I., Yulita, I.N.: Comparative analysis of classification algorithm: random forest, SPAARC, and MLP for airlines customer satisfaction. SINERGI 26(2), 213–222 (2022)
Hong, A.C.Y., Khaw, K.W., Chew, X., Yeong, W.C.: Prediction of US airline passenger satisfaction using machine learning algorithms. In: Data Analytics and Applied Mathematics (DAAM), pp. 8–24 (2023)
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Ibrahim, A.O., Yi, C.C., Elsafi, A., Ghaleb, F.A. (2024). Revolutionizing Airline Customer Satisfaction Analysis with Machine Learning Techniques. In: Saeed, F., Mohammed, F., Fazea, Y. (eds) Advances in Intelligent Computing Techniques and Applications. IRICT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-59707-7_13
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