Overview
A continuous, smartphone-sensor approach to detecting alcohol-influenced driving suited to low-resource contexts. The model was trained on a Nigeria-collected dataset and achieves recall 100%, precision 60%, F1 75%, and accuracy 90.91%.
Technology
- Python
- scikit-learn
- Decision Trees
- Smartphone Sensors
Themes
- Machine Learning
- Nigeria
- Driving Behaviour
- Smartphone Sensing