Machine Learning for Airline Passenger Satisfaction

In-depth study on predicting airline passenger satisfaction using advanced machine learning techniques. This research covers various aspects, including preprocessing, feature selection, application of multiple models, extensive evaluation metrics, and visualization of results.

Key Features

Rich Dataset: Utilizing a comprehensive dataset encompassing various factors such as in-flight services, seat comfort, and more, to capture the multifaceted nature of passenger experience.

Preprocessing: Employing rigorous data preprocessing techniques to clean, transform, and prepare the dataset for modeling.

Feature Selection:

Recursive Feature Elimination (RFE) Principal Component Analysis (PCA) SelectKBest Mutual Information and more

Model Application:

Random Forest Gradient Boosting Support Vector Machines (SVM) Neural Networks Decision Trees k-Nearest Neighbors (k-NN) Logistic Regression Naive Bayes XGBoost AdaBoost Bagging Extra Trees LightGBM and more

Evaluation Metrics:

Accuracy Precision Recall F1 Score Area Under the Receiver Operating Characteristic Curve (AUC-ROC) Area Under the Precision-Recall Curve (AUC-PR) Matthews Correlation Coefficient Cohen's Kappa Hinge Loss

Visualization of Results:

Generating visualizations to intuitively present model predictions, feature importance, and evaluation metrics.