Understanding Airline Passenger Satisfaction Level Using Machine Learning - Serban ANGHEL; Adina Petruta PAVEL (Communications of International Proceedings, 2020)
Air travel has come a long way from the crowded and noisy experience in the 1940s, through the glamorous experience in the 1970s and 1980s to the diversity of services provided today. The markets of the traditional airlines are more and more intertwined with the market of low-cost carriers (LCCs), the latter penetrating the upper class market by providing different paid services, such as cargo luggage, lunch, selection of seats and others. The future passengers’ decision of which airline to choose becomes more difficult, as the airlines offer similar services, therefore the interest of the airlines to attract new passengers and to make them satisfied is more and more pronounced. This paper proposes a new approach to find out what exactly could be a crucial factor in improving the passengers’ satisfaction by applying a machine learning algorithm on a large database with more than 100,000 passengers and more than 20 data points on each of them. The results could be used in the business decision making process, by focusing on providing the services which improve the most the passengers’ satisfaction, therefore ensuring a sustainable development for an airline.
Keywords: Passenger satisfaction; Airline; Machine Learning; Decision Tree