Unlocking Insights into Airline Passenger Satisfaction with Data-Driven Exploratory Mining Approach

Abstract

This paper thoroughly analyzes an airline passenger satisfaction survey dataset by employing Exploratory Data Analysis (EDA) and machine learning modeling by leveraging Python-based libraries including Pandas, NumPy, Seaborn, Matplotlib and a few others. Through EDA, we examine diverse aspects of the dataset, encompassing flight attributes, delays, and service ratings. Subsequent utilization of machine learning techniques such as regression, classification, or ensemble methods aim to uncover patterns and predict passenger contentment levels. The integration of EDA and machine learning methods enhance insights, validate findings, and contribute to a comprehensive understanding of factors influencing passenger satisfaction within the aviation sector.