Case study 02
Applied analytics · Transportation
Houston METRO Bus Delay Prediction
A classification-based data science project exploring which service and route conditions are most associated with late bus arrivals in Houston.
- Python
- pandas
- scikit-learn
- EDA
- Classification
- 2
- arrival classes modeled
- 5
- analysis stages
- HTX
- local decision context
01 · Business question
What needed to change?
Late arrivals affect rider trust and the usability of public transportation. The project asked whether available Houston METRO service data could be cleaned and structured well enough to classify an arrival as late or on time.
02 · Approach
From ambiguity to a system.
- 01
Defined a binary target that separated late arrivals from on-time service.
- 02
Cleaned the source data and reviewed missing, inconsistent, and extreme values.
- 03
Used exploratory analysis to identify temporal and operational patterns associated with delay.
- 04
Selected candidate features and compared classification behavior against a simple baseline.
- 05
Evaluated the model with emphasis on interpretability, error tradeoffs, and practical limitations.
03 · Analytical flow
A repeatable path from question to action.
- 01METRO data
- 02Cleaning
- 03Exploration
- 04Feature selection
- 05Model evaluation
04 · Outcome
A local public-data problem translated into an interpretable prediction workflow.
- Created a repeatable notebook workflow from raw public data to an evaluated classification result.
- Connected model output to a real operational question rather than treating prediction as an isolated exercise.
- Documented limitations and the additional service data needed for a stronger production model.
Reflection
“The project reinforced that a useful model begins with a defensible target definition and careful data preparation—not the most complex algorithm available.”