End-to-end machine learning project to predict airline customer satisfaction using XGBoost, Random Forest and Neural Networks, combining EDA, PCA and SHAP explainability to identify the service, customer and travel variables that most strongly influence satisfaction and support data-driven service improvement strategies.
machine-learning random-forest xgboost pca classification data-analysis logistic-regression decision-tree customer-satisfaction feature-importance customer-experience shap airline-analytics logistic-regression-
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Updated
Apr 6, 2026 - Jupyter Notebook