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Goal: Predict airline passenger satisfaction to identify service quality gaps and support operational improvements in staffing, process design, and resource allocation.
You can download the data through this llink in kaggle.
Expectations:
- Data Preparation: Clean and preprocess the dataset, addressing missing values, outliers, and categorical encodings such as gender, customer type, and travel class.
- Exploratory Data Analysis (EDA): Examine satisfaction levels across flight types, service touchpoints such as boarding efficiency, seat comfort, and inflight service, and passenger demographics. Identify which operational factors most strongly drive dissatisfaction. Conduct any additional EDA relevant to the modeling process and final conclusions.
- Modeling: Build and evaluate classification models to predict whether a passenger is satisfied or neutral/dissatisfied. Compare model performance using appropriate evaluation metrics and interpret key contributing features.
- Insights and Recommendations: Provide 23 operationally grounded recommendations: such as prioritizing service improvements in high-dissatisfaction touchpoints, adjusting staffing or training protocols for underperforming service areas, or identifying passenger segments that disproportionately experience service failures.
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