Airline Passenger Satisfaction Determinants

Background: Understanding the factors influencing airline passenger satisfaction is essential for the aviation industry to enhance customer experience and loyalty. This research aims to identify key determinants of passenger satisfaction using exploratory data analysis (EDA) and machine learning algorithms.

Objectives: Investigate the impact of various factors including online boarding, in-flight services, flight distance, legroom service, age, ease of online booking, seat comfort, departure and arrival time convenience, baggage handling, gate location, and cleanliness on airline passenger satisfaction.

Methods: Employed EDA techniques and machine learning algorithms, including random forest, probit, and naive Bayes models. Applied undersampling and bootstrapping methods to address sample imbalance. Evaluated model performance using ROC/AUC curves and confusion matrices.

Results: Identified online boarding, in-flight services, flight distance, legroom service, age, ease of online booking, seat comfort, departure and arrival time convenience, baggage handling, gate location, and cleanliness as significant determinants of passenger satisfaction. Developed models achieved high accuracy ranging from 88% to 99%.

See the full project at https://github.com/georgeoiko3/Personal-Projects/blob/main/Pj3-Report.pdf