Analyzing Airline Services and Communication Systems by Designing Machine Learning Model to Predict Passenger Satisfaction

Rodzan Iskandar, Okta Reni Azrina Rasyid Anies, Rusydi Iskandar, Mezan el-Khaeri Kesuma, Mario Konecki

Abstract


This research explores the methods of assessing airline passenger satisfaction through surveys and analyzing factors that are strongly linked to whether a passenger is satisfied or dissatisfied. The aim is also to investigate if it is possible to predict passenger satisfaction levels. The dataset used in this study comes from a Kaggle dataset titled "Airline Passenger Satisfaction," which includes 223,690 records with 23 measurement variables and 1 response variable. It identifies three key factors critical to airline service improvement: delays, online boarding, and class. Airlines can enhance their service offerings by focusing on these areas as air travel activities pick up. Specifically, online boarding is highlighted as a significant factor in reducing the need for manual check-ins and waiting in queues, thereby providing a faster and more efficient process. Furthermore, the study's analysis of categorical data and its correlation with satisfaction levels yields important insights into customer preferences within the airline industry. The differentiation between loyal and disloyal customers, as visualized in the study, shows that many loyal customers are dissatisfied. This points to the fact that loyal customers, despite their overall satisfaction, have faced varying levels of service quality.

Keywords


Airline Services; Communication System; Machine Learning; Passenger Satisfaction

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References


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DOI: http://dx.doi.org/10.24042/ijecs.v3i2.19782

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