SpaceX Landing Success Predictive Analysis

This project is part of the IBM Applied Data Science Professional Certificate Capstone

The final report can be found here

Summary

• Constructed a full data science pipeline using Python (Pandas, Scikit-learn) for the SpaceX Falcon 9 launches, encompassing data acquisition (SpaceX API and Web Scraping) and feature engineering.
• Developed and optimized classification models (Logistic Regression, SVM, KNN, Decision Tree) to predict first-stage landing success, achieving the highest accuracy with the Decision Tree model.
• Engineered an interactive analytics dashboard using Plotly Dash to visualize success rates by launch site and payload mass, and utilized Folium for geographical analysis of sites.