This data science project for CST383: Introduction to Data Science investigates the relationship between a satellite’s physical characteristics and its intended mission. By developing a predictive model to categorize satellites into six primary classes, the study demonstrated that specific mission types are strongly tied to distinct orbital patterns. Key achievements include:

• High Accuracy Modeling: Developed a predictive model using multinomial Logistic Regression that achieved a 94.4% accuracy rate.
• Feature Engineering: Analyzed key orbital parameters such as inclination, apogee, and eccentricity alongside physical features like launch mass.
• Data Integration: Utilized and merged comprehensive datasets from the UCS Satellite Database and Celestrak SatCat.
• Actionable Insights: Demonstrated that publicly available orbital data serves as a powerful proxy for identifying the purpose of unknown satellites.

The research illustrates a scalable method for rapid cataloging and provides significant engineering insight into the deployment of orbital assets.