Predicting Airline Passenger Satisfaction with Stacking Classifiers and Machine Learning Models

Mandava, Thanmayee (2024) Predicting Airline Passenger Satisfaction with Stacking Classifiers and Machine Learning Models. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the airline industry, the important aspect that impacts an airline’s performance is the satisfaction of passengers. Airlines that can understand and satisfy their passengers’ expectations succeed through customer loyalty and increased sales. In addition to ticket pricing, modern passengers assess the kind of services provided by various carriers before making their choices. Therefore, airlines need to ensure service excellence. Passenger feedback leads to these expectations, and consequently, the airlines will take note of these for fine-tuning. This research has performed data mining on a dataset of over 130,000 customer satisfaction ratings for various airlines to analyze the main drivers of satisfaction. Even with increasing competition, most studies in the past failed to address the complexity of passenger satisfaction, often limiting either the factors or traditional methods used. This study focused on bridging the gap that identifies and predicts satisfaction drivers by embedding stacking classifiers and machine-learning models. The best performance was obtained for the Stacking model with the meta-learner classifier MLP, with a 96.53% accuracy, 97.97% precision, 94.06% recall, 95.97% F1 score, and 92.99% MCC. The study has underpinned key fact-oriented decision-making in achieving high satisfaction of customers in the airline sector to gain a competitive advantage.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Qayum, Abdul UNSPECIFIED
Uncontrolled Keywords:	Airline satisfaction; machine learning; stacking model; customer satisfaction; data mining
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Aviation Industry H Social Sciences > HF Commerce > Marketing > Consumer Behaviour Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2025 11:55
Last Modified:	03 Sep 2025 11:55
URI:	https://norma.ncirl.ie/id/eprint/8742

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