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falcon9-spacex-landing

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Ibrahim-Maiga / SpaceX-Falcon9-Landing-Prediction-V2

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Machine learning project to predict the success of SpaceX Falcon 9 first stage landings, with the goal of estimating launch costs. It utilizes classification models such as Logistic Regression, Decision Tree, Random Forest, SGD, and SVM to analyze launch data from 2010 to the present (Edition 2)​.

visualization machine-learning random-forest logistic-regression decision-trees predictive-modeling svm-classifier sgd-classifier falcon9-spacex-landing
  • Updated Nov 11, 2024
  • Jupyter Notebook

Ljove02 / spacex-falcon9-analysis

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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.

simulation random-forest data-visualization neural-networks logistic-regression spacex smote gradient-boosting machine-learning-models space-exploration data-analysis-python rocket-landing falcon9-spacex-landing
  • Updated Sep 16, 2024
  • Jupyter Notebook

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