An end-to-end data and machine learning project to analyze factors influencing launch success and cost reduction through booster reusability.
Scope
- Data ingestion from multiple sources including SpaceX API.
- Data transformation pipeline for exploration and model-ready datasets.
- Interactive dashboard to analyze payload ranges, launch-site success rates, and operational patterns.
- Predictive modeling with algorithm comparison (Logistic Regression, SVM, Decision Tree, KNN) and performance-based selection.
- Web deployment packaging using Gunicorn.