Predictive Analytics for Falcon 9 First Stage Landing Success

Project Background and Context

• Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore, if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch. This goal of the project is to create a machine learning pipeline to predict if the first stage will land successfully.

Problems to Answers

• What factors determine if the rocket will land successfully?
• The interaction amongst various features that determine the success rate of a successful landing?
• What operating conditions needs to be inplace to ensure a successful landing program?

Executive Summary

• Data collection methodology:
• Data was collected using SpaceX API and web scraping from Wikipedia.
• Perform data wrangling
• One-hot encoding was applied to categorical features
• Perform exploratory data analysis (EDA) using visualization and SQL
• Perform interactive visual analytics using Folium and Plotly Dash
• Perform predictive analysis using classification models
• How to build, tune, evaluate classification models