This machine learning project predicts the success of SpaceX Falcon 9 first-stage landings, providing insights for estimating launch costs. Using launch data from 2010 to the present, the project applies classification models like Logistic Regression, Decision Tree, Random Forest, SGD, and SVM, with GridSearchCV for optimization. Key features include data scraping with BeautifulSoup, exploratory analysis of launch sites, payload mass, and booster versions, as well as interactive visual analytics using Folium. By analyzing factors like payload mass, orbit type, and launch site proximity, the project explores optimal locations for new launch sites to support successful missions.
