Lighting-pole Maintenance Prioritization

What it is

The project used condition data to help a startup prioritize lighting-pole maintenance. I cleaned and prepared the datasets with Python and RapidMiner, trained and compared several models, and produced two final models.

The problem

A startup needed to decide which lighting poles to maintain first based on available condition data, with limited local training data.

What I worked on

• Cleaned and prepared condition datasets with Python and RapidMiner.
• Trained and compared several machine-learning models.
• Produced a model reaching 76% accuracy on a small local dataset.
• Produced a separate model reaching 96% accuracy on a larger dataset with more features.
• Communicated model results and limitations to a non-ML stakeholder.

Decisions

Testing and verification

• Compared multiple models on each dataset before selecting final candidates.
• Documented dataset differences alongside accuracy results.

Stack