In this capstone project, the objective is to make predictions on the successful landing of the Falcon 9 first stage. SpaceX offers rocket launches on their website, priced at 62 million dollars, significantly lower than other providers whose costs can reach 165 million dollars per launch, mainly due to the reusability of the first stage. By accurately determining the landing outcome, we can estimate the cost of a launch, which can be valuable information for competing companies interested in bidding against SpaceX for a rocket launch.

This cost reduction is mainly attributed to SpaceX's ability to reuse the first stage of the rocket. Consequently, accurately predicting the probability of a successful first stage landing enables the determination of launch costs. Leveraging the insights and models derived from utilizing Data Science techniques, what are certain factors that may affect the landing outcome?

• Data collected from Space X API
• Additional information collected through Web scraping Link here.
• Data Wrangling and processing
• Exploratory analysis using SQL magic and Python visualization tools.
• Data analytics using Folium Interactive maps and Plotly Dshboards
• Predictive analysis using Machine Learning Classification algorithms.

• Data collection Libraries
  • Requests
  • Beasutiful Soup
• Data Wrangling and processing Libraries
  • Pandas
  • Numpy
  • SQLAlchemy
• Visualization Libraries
  • Matplotlib
  • Seaborn
  • Folium
  • Plotly
  • Dash
• Machine Learning Libraries
  • Scikit Learn

• Comprehensive businees report in form of a power point presentation.
• Jupyter notebooks containing codes and results

Through analysis, it is deduced that the main factors to consider when predicting the outcome of a successful landing can be attributed to (but not limited to):

• Mass of the payload
• Orbit Type