Rather than relying on automated scripts, you will write custom Python code to build, tune, and evaluate several core predictive models. The project requires you to split your data to find the optimal hyperparameters for Support Vector Machines (SVM), Decision Tree Classifiers, Logistic Regression, and K-Nearest Neighbors (KNN). By performing a rigorous comparative analysis using test data, you will identify which mathematical model yields the highest predictive accuracy for rocket stage recovery.

This capstone is modeled exactly after an enterprise data consulting role. You will follow the complete data science methodology starting from scratch: collecting real-world launch records via RESTful APIs and web scraping, handling missing data during data wrangling, conducting exploratory data analysis (EDA), and building an interactive dashboard with Plotly Dash alongside geospatial mapping using Folium. It forces you to manage the entire pipeline from raw data ingestion to deployment-ready analytics.

A great data scientist must be able to translate complex algorithmic outputs into actionable business strategies. The final phase of this capstone requires you to compile your findings into a comprehensive report for stakeholders. Using your model's predictive insights on whether the Falcon 9 first stage will land successfully, you will formulate a competitive bidding strategy for an alternate aerospace startup. This portfolio piece proves to employers that you possess both technical machine learning skills and corporate data literacy.