In this project, I accomplished the following:
- Extracted datasets through an open-source SpaceX REST API and used web scraping with Python's BeautifulSoup to gather additional historical data on SpaceX launch and landing proceedings for the Falcon 9 rocket from 2010 to 2020.
- Conducted exploratory data analysis through SQL, Matplotlib, and Pandas, extracting relationships between key performance variables like payload mass, orbit type, launch site, and mission status.
- Created visualizations using Folium and generated an interactive analytical dashboard of results with Python’s Plotly and Dash frameworks.
- Trained three different machine learning models (Decision Tree Classifier, K-Nearest Neighbors, and SVM) and analyzed which would be the most accurate at predicting whether a given launch would land the first stage successfully.
- Generated a sample stakeholder report with important conclusions generated from overall data analytics and visualizations.
Overall, it was determined that across 2010-2020, approximately two-thirds of all SpaceX launches typically landed their first stages successfully.
This project was completed as part of the Applied Data Science Capstone course in the IBM Data Science Certification.