Airline Satisfaction

Below is a group project where we analyzed over 100,000 rows of real airline customer satisfaction data, examining factors such as seat comfort, entertainment, and overall experience. We applied principal component analysis (PCA) and k-means clustering to segment customers and run separate regressions for each group, aiming to identify strategies for improving satisfaction. However, despite clear behavioral differences, the demographic profiles within each cluster were too similar, making targeted marketing efforts ineffective. Instead, we shifted our focus to analyzing marginal effects for specific demographics, allowing us to derive actionable insights for more precise and impactful customer targeting.