SpaceX Falcon 9 Landing Analysis

This project involved analyzing past SpaceX launches that utilized the Falcon 9 rocket to gain insights into the different attributes of the launches and their relationship with the success of Falcon 9's first-stage landing. Data was collected from SpaceX API and the SpaceX Wikipedia launch page using Python's Beautiful Soup.

The project then focused on data wrangling using Pandas and exploratory data analysis (EDA) using Python's Matplotlib and Seaborn. SQL (SQLAlchemy) was used to extract data from the IBM DB2 database. An interactive Dash app was built using Python to visualize the landing success rate across various launch sites and payload mass for different booster versions. Finally, a predictive model was built and tuned using Scikit-Learn to predict the landing outcome based on launch attributes.