Enhancing Transportation Prediction in Space Tourism via Hybrid Machine Learning and AR Integration

Abstract: The increasing relevance of space tourism necessitates the development of predictive systems for ensuring passenger safety and transportation efficiency. This paper presents a hybrid machine learning pipeline that utilizes advanced techniques including feature engineering, anomaly detection (Isolation Forest), stacking classifiers, SHAP explainability, and NLP with transformer models to predict survival rates based on the Spaceship Titanic dataset. Additionally, we propose an Augmented Reality (AR) simulation tool to enhance passenger experience and operational preparedness. Our methodology achieves robust predictive performance, improved model interpretability, and practical integration for real-time space tourism applications.