Predicting Falcon 9 Landing Success & Launch Cost Impact

Project Overview

This project uses machine learning to predict whether a SpaceX Falcon 9 first stage will successfully land and be reused. Successful recovery dramatically reduces launch cost (approx. $165M down to $62M), so forecasting landing outcomes provides financial insight for satellite operators planning missions.

Objectives

• Collect and prepare Falcon 9 launch data from SpaceX API
• Engineer features related to flight history, payload, site, and booster config
• Train and tune ML models to classify landing success
• Estimate expected launch cost based on predicted reuse probability
• Provide risk guidance for mission planning

Methods

• Data: SpaceX v4 API (90 Falcon 9 launches through 2020)
• Models: Logistic Regression, SVM, Decision Tree, KNN
• Techniques: One-hot encoding, standardization, GridSearchCV, confusion matrices
• Metrics: Cross-validation accuracy, test accuracy

Key Results

• Best models: Logistic Regression & SVM (~0.83 test accuracy)
• Demonstrated cost-impact calculation using landing probability
• Example inference for a new mission:
  • Landing success probability: ~85%
  • Estimated launch cost: ~$77M
  • Risk level: Low

Strategic Insights

• Booster landing probability drives launch-cost expectation
• Customers can use probability-based pricing logic to:
  • Negotiate contracts when risk is higher
  • Compare SpaceX vs. traditional launch pricing
  • Allocate insurance and contingency spending
  • More historical data will improve model robustness, especially across orbit classes and booster configurations

Future Work

• Incorporate telemetry & weather data
• Deploy as interactive dashboard
• Add calibration + ROC-AUC reporting