Predictive Analytics for Enhanced Passenger Satisfaction in the Airline Industry: Leveraging Machine Learning to Drive Strategic Decision-Making

• Main Authors: Ismail, Walaa Saber, Bawazeer, Hesham, Almansori, Hamad
• Format: Conference Proceeding
• Language: English
• Subjects: Airline industry; Atmospheric modeling; Bayes methods; Decision making; in-flight services; k-NN; Machine learning; Measurement; Naive Bayes; Nearest neighbor methods; Optimization; passenger satisfaction; Predictive analytics; Predictive models; service improvement
• ISSN: 2768-6388
• DOI: 10.1109/ICOA62581.2024.10753807

In the highly competitive airline industry, enhancing passenger satisfaction is crucial for maintaining customer loyalty and market competitiveness. This study utilizes advanced machine learning techniques to analyze comprehensive dataset comprising approximately 130,000 entries related to airline passenger experiences. The dataset includes both qualitative and quantitative attributes from in-flight services to logistical details of air travel. Two predominant machine learning models, Naive Bayes and k-Nearest Neighbors (k-NN) were employed to predict passenger satisfaction levels and identify key factors impacting their experience. The Naive Bayes model demonstrated superior predictive performance compared to k-NN, with significant accuracy, precision, recall, and AUC metrics. This research not only offers valuable insights into the key factors influencing passenger satisfaction but also provides data-driven recommendations for service improvement. It lays the groundwork for airlines to utilize predictive analytics in strategic decision-making, thereby enhancing passenger satisfaction and operational efficiency.