GitHub / Ljove02 / spacex-falcon9-analysis
The Falcon 9 Landing Success Prediction project predicts Falcon 9 first-stage landings using machine learning models like Logistic Regression, Random Forest, Gradient Boosting, and Neural Networks. Key features include payload mass, orbit type, and booster reuse. Data is balanced with SMOTE for better accuracy.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ljove02%2Fspacex-falcon9-analysis
PURL: pkg:github/Ljove02/spacex-falcon9-analysis
Stars: 0
Forks: 0
Open issues: 0
License: mit
Language: Jupyter Notebook
Size: 18.7 MB
Dependencies parsed at: Pending
Created at: 11 months ago
Updated at: 5 months ago
Pushed at: 5 months ago
Last synced at: 5 months ago
Topics: data-analysis-python, data-visualization, falcon9-spacex-landing, gradient-boosting, logistic-regression, machine-learning-models, neural-networks, random-forest, rocket-landing, simulation, smote, space-exploration, spacex