Enhancing Airline Passenger Experience
Objective: The primary goal is to leverage the "Airline Passenger Satisfaction" dataset to uncover insights into what factors contribute most to passenger satisfaction and dissatisfaction. The project aims to predict passenger satisfaction levels based on various service aspects provided by the airline.
Context: In a competitive airline industry, understanding and improving passenger satisfaction is crucial for retaining customers and enhancing service quality. Airlines strive to identify key factors that influence passenger experience and satisfaction. This project will enable an airline to strategically invest in areas that significantly impact passenger satisfaction, thereby improving overall service quality and competitive advantage.
Challenge: Students will analyze the dataset to identify patterns and correlations between different service aspects (such as inflight wifi service, seat comfort, and cleanliness) and overall passenger satisfaction. They will develop a predictive model to forecast a passenger's satisfaction level based on these features. The model's accuracy and insights will guide the airline in prioritizing service improvements and personalizing the passenger experience.
Deliverables:
This project will not only help students apply their data science skills in a real-world context but also contribute to enhancing the airline's service quality by understanding and addressing passenger needs and preferences.
Introduction to Basic Machine Learning Concepts:
Understanding the Business Problem:
Transforming Business Problems into Data Science Problems:
Data Investigation:
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