Objective: Identified and analyzed key factors influencing customer satisfaction in air travel. Approach: Developed a predictive model using Python's scikit-learn and XGBoost to analyze a datasetwith 25 features. Conducted data cleaning, visualization, and implemented Logistic Regression, RandomForest, Decision Tree, and XGBoost classifiers. Results: Logistic Regression: Train: 72.49%, Test: 72.58% Random Forest: Train: 100%, Test: 95.65% Decision Tree: Train: 100%, Test: 93.59% XGBoost: Train: 96.91%, Test: 95.80% Achievements: Identified key flight satisfaction factors. Implemented highly accurate ML models. Communicated findings effectively through visuals