California Flight Delay Analysis

Overview¶

This project analyzes and models flight delay behavior in California using data from the U.S. Department of Transportation. The main objectives are to look at airline and airport reliability, explore clustering structures among airlines and airports, and evaluate how well we can predict flight delays using statistical and machine learning methods.

Motivation¶

Because of the many recent flight delays due to the government shutdown (Shepardson & Ljunggren (2025)), we found it interesting to take a closer look at how delays behave in practice. This motivated us to analyze which airlines and airports perform best and whether delays can be predicted using historical data.

Dataset¶

The dataset used in this project is the U.S. Flight Delays Dataset (United States Department of Transportation (2015)), available on Kaggle:
https://www.kaggle.com/datasets/usdot/flight-delays

The data comes from from the Bureau of Transportation Statistics and contains on-time performance records of domestic U.S. flights across 14 airlines and 322 airports during 2015. Due to the large size of the dataset, a subset is used in this project focusing only on flights leaving from California airports.

Project Website¶

The project’s website can be accessed here:
https://ucb-stat-159-f25.github.io/final-group21/

Repository Structure¶

The repository is structured in the following way:

• README.md : Serves as the main entry point, describing the project purpose, structure, and usage.

• LICENSE : Defines the legal terms under which the code and data may be used.

• environment.yml : Specifies the environment and dependencies required to run the project.

• .gitignore : Specifies files and directories that should not be tracked by Git.

• Makefile : Provides convenient commands for running common tasks.

• .github/workflows : Contains GitHub Actions workflows.

• data/ : Stores the datasets used throughout the project.

• helper_functions.py : Contains utility functions.

• main.ipynb : Discusses the core parts of the analyses and results.

• get_data.ipynb : Fetches and preprocesses the raw data used in the analysis.

• airlines_airports.ipynb : Explores and analyzes delays with respect to different airlines and airports (including clusterings).

• months_days.ipynb : Explores and analyzes delays with respect to different months, days and hours in the day.

• predictions.ipynb : Generates predictions for different models determining whether flights are delayed and by how much, and evaluates the different models’ performances.

• figures/ : Contains generated figures and tables used in analysis.

• tests/ : Holds tests to verify the correctness of the codebase.

• pdf_builds/ : Contains PDF versions of all notebooks in the project.

• references.bib : Contains bibliographic references cited in notebooks or documentation.

• ai_documentation.txt : Documents how AI tools were used in the making of the project.

• myst.yml : Configures MyST build settings for the project.

• plane.png : Image used as the logo on the MyST web page.

• plane_favicon.ico : Favicon used for the web page.

Setup¶

Clone the repository:

git clone https://github.com/UCB-stat-159-f25/final-group21.git
cd final-group21

Create and activate the environment:

conda env update -f environment.yml --name proj03 --prune
conda activate proj03

Install the IPython kernel:

python -m ipykernel install --user --name proj03 --display-name "IPython - proj03"

Usage¶

Create or update the environment:

make env

Run all notebooks in the project:

make all

Testing¶

Tests can be executed from the project root using:

PYTHONPATH=./ pytest

License¶

This project is licensed under the BSD 3-Clause License.