Flight Delay Risk Tracker
Completed
Developed a Python model that analyzes airline data and classifies delay risk based on factors such as weather, time, and flight distance. Used pandas for data organization and analysis and Matplotlib to visualize patterns and risk categories. Claude was used to assist with synthetic data generation and coding guidance during the learning process.
The challenge
Delay risk depends on several interacting factors at once, which makes raw flight records hard to reason about without a structured model.
My role
Sole developer: data organization, analysis, classification logic, and visualization.
Research & requirements
Explored which factors plausibly influence delays (weather, departure time, distance, and operational conditions) and decided how to represent each in the model.
The solution
Built a Python model in Google Colab that organizes airline data with pandas, classifies delay risk across those factors, and visualizes the resulting patterns and risk categories with Matplotlib. Claude assisted with synthetic data generation and coding guidance.
Technical approach
- pandas for loading, cleaning, and organizing flight records
- Risk-classification logic based on weather, time, and distance factors
- Matplotlib charts for comparing patterns across risk categories
- Synthetic data generation used for learning and testing
Tools used
- Python
- pandas
- Matplotlib
- Google Colab
- Claude
Business impact
A personal learning project with no production deployment. It sharpened my workflow for going from raw data to a defensible classification and chart.
What I learned
Cleaning and modeling assumptions matter as much as the analysis. Documenting those choices made the results easier to trust and revisit.