Space X Falcon 9 Landing Success Prediction
Situation: This project is a predictive analysis of the Space X Falcon 9 success landing rate for IBM Data Science Certification final capstone.
Task: Data collection from Space X database and web, descriptive data analysis, predictive analysis with Machine Learning.
Action: I collected, processed and analyzed data and built, validated and compared Machine Learning models to understand the most relevant variables related to the improving of success rate. To do that, I perform data wrangling including missing values treatment and class labeling. Furthermore, I conducted exploratory data analysis (EDA) using visualization and SQL and built interactive visual analytics using Folium & Plotly Dash. Finaly, I developed a predictive analysis using classification models and hyperparameter tuning for SVM, Classification Trees and Logistic Regression.
Result: The launch site KS LC-39A, payload mass between 2500-5000 kg and orbit type SSO have highest success rate. We can predict if a launch will be successful or fail with 87% accuracy in test data. See the full repository code and reports.
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- Space X Falcon 9 Landing Success Prediction
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Contact
Feel free to reach out to me on LinkedIn or contact by email dimas.jackson.ds@gmail.com. I’m always open to interesting discussions and collaborations!
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