A Jupyter Notebook to analyze revenue per seat mile of US commercial airlines.
To complement an investment analysis in the US commercial airline industry, I dedicated some time to research indicators beyond the usual financial data available across different market data sources. During this reserach, I came across the Airline Data Project (ADP) website, an effort leaded by the MIT Global Airline Industry Program "to better understand the opportunities, risks and challenges" of the industry.
The ADP website offers consolidated data regarding revenue, expenses, compensation, traffic, capacity and other financial related data.
With the objective to answer the question "which airline is most efficent in terms of revenue?", I decided to run a quick data project to analyze and compare the two main indicators of efficiency across the industry: TRESM (Total Revenue per Equivalent Seat Mile) and PRESM (Passenger Revenue per Equivalent Seat Mile).
The data used in the project was sourced from teh ADP website and cleaned the purpose of this study. The dataset can be viewed at the following Google Spreadsheet
The data and visualizations can be viewed in the Jupyter Notebook hosted in Github.
Alternatively, you can clone the repo, explore the data and remix the project.
git clone https://github.com/ricardo-hdz/airlines-data.git
cd airlines-data
jupyter notebook