Fall 2024\ Yun Fei Chen (\<student number>)\ David Krljanovic (301427415)
Spirit Airlines Inc. is a popular American airline known for it's affordable airfare, and poor service and overall customer experience. Frequent delays, uncomfortable seats, and questionably terrible in-flight service plagues travelers on their journies to their target destinations. One could argue that the sole purpose of a plane is to transport people from point A to point B, and that selling an experience is not a necessity. This begs the question: Does the quality of these services actually influence the satisfaction of airline customers to a significant degree? This is a good question to ask – a business certainly does not wish to deter its target demographics from considering them for future travels. It is worth noting that as of recent, Spirit Airlines stock has fallen ninety-five percent in the the last year, and the company has filed for bankruptcy.
Using a Kaggle-sourced dataset containing over 100,000 airline customer satisfaction survey results of 24 features, we will perform data mining techniques to uncover hidden patterns and information that may prove to be useful to data scientists and businesses alike. Following a routine preprocessing of the data, exploratory data analysis will reveal surface level information that will serve to guide the procedure of machine learning clustering and classification tasks. Each section will serve as an overview of our procedure, and the final section will conclude our findings and attempt to present a meaningful discovery.