Machine learning-based feature selection approach for no-show rate prediction: A case of aviation industry

In today's environment of increasing competition, rapidly changing customer demands, and globalization, companies are compelled to operate with low profit margins and adapt swiftly to changing conditions to gain a competitive advantage. This thesis was conducted during a period when the importance of technologies such as artificial intelligence and big data is increasing in the highly competitive aviation industry. The aviation industry is known for its high capital requirements, low profit margins, intense competition, complex operational structures, and constant pressure from local or global conditions. Considering the low profit margins in the aviation industry, airlines are continually encouraged to enhance their efficiency, reduce operational costs while maintaining customer satisfaction, or explore areas that could generate additional revenue. A significant proportion of passengers who purchase tickets do not show up for their scheduled flights. This situation allows airlines to practice overbooking, and accurately predicting the rate of no-shows can provide substantial additional revenue. However, if all passengers do show up, airlines would incur additional costs and suffer reputational damage. Therefore, accurately predicting no-show rates and optimizing operational planning processes are critically important for airlines. No-show rates are generally estimated by analyzing historical data using statistical methods. However, recently developed machine learning and big data analytics have the potential to play a significant role in solving such complex problems. There are very few studies in the literature on predicting no-show rates using machine learning methods. These studies typically use only Passenger Name Record (PNR) data for no-show prediction, neglecting many external factors. Using only passenger information reduces the accuracy of predictions due to the unique characteristics of each passenger. Unlike other studies, this research investigates the impact of external factors such as weather conditions, traffic density, special occasions, and public holidays on no-show rates without using PNR data, employing big data analysis and machine learning methods. This study focuses on the unique and critical need to improve operational planning processes in the aviation industry. It aims to contribute to understanding and addressing the challenges in the aviation industry, particularly in accurately calculating the no-show rate. To achieve this goal, the importance and effects of accurately predicting the no-show rate have been comprehensively examined, and a model has been proposed using machine learning algorithms to address the problem at hand.