Analyze SpaceX Falcon 9 missions using survival analysis, geospatial maps, and EDA to uncover factors behind successful landings.
- Fetched real-time data via SpaceX public API
- Flattened nested JSON (e.g., cores, rockets, launchpads)
- Rule-Based Landing Success Estimator to predict landing success rate using mission parameters — implemented directly in the notebook
- Kaplan-Meier survival curve using
lifelines - Interactive launch site map with
folium - Visualized booster reuse, landing types, and flight experience
- Python, Pandas, Matplotlib, Seaborn
- Lifelines, Folium, Requests
- Jupyter Notebook
