Winning space race with data science

Project description

Develop and evaluate machine learning algorithms to determine the likelihood of reusing the initial stage of a rocket in a space launch.

Background and context

SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars.

Other providers cost upwards of 165 million dollars each.

Much of the savings is because SpaceX 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 SpaceX for a rocket launch.

The problem

Use public information to predict if SpaceX will reuse the first stage.

Methodology

Data collection methodology

SpaceX REST API.

Web scraping related Wikipedia pages.

Perform data wrangling

Cleaning, filtering and dealing with missing values.

Applying one-hot encoding to categorical features.

Perform EDA using visualisation and SQL

Perform interactive visual analytics using Folium and Plotly Dash

Perform predictive analysis using classification models

Developing logistic regression, k-nearest neighbors, support vector machines and decision tree models.

Comparing them to determine which model performs the best.

Results