Developed a predictive model to determine the success of Falcon 9 first-stage landings, essential for estimating launch costs. Leveraged machine learning techniques to analyze SpaceX data and make informed predictions.
- Conducted comprehensive data collection using RESTful API and web scraping, followed by data wrangling and exploratory data analysis.
- Built an interactive dashboard using Plotly Dash and analyzed launch site proximity with Folium.
- Implemented ML models, including SVM and Classification Trees, optimizing hyperparameters for accurate predictions.
- Utilized Python for data manipulation (Pandas), machine learning (Scikit-learn), and visualization (Plotly, Folium). -Employed SQL for data querying and exploration.
- Identified key factors influencing successful first-stage landings, including launch site proximity and payload mass. Established the Tree Classifier Algorithm as the optimal model for predicting outcomes.