// case study — 04/05
Accident Risk Model
ML pipeline predicting 2026 road-accident severity & blackspots
overview
End-to-end machine learning pipeline that ingests road-accident records and predicts 2026 accident occurrences and geographic blackspots — high-risk locations where collisions are statistically concentrated. Exploratory data analysis surfaces hidden patterns; feature engineering extracts time-of-day, weather, road-condition, and vehicle-type signals. The trained classifier outputs risk-tier scores with per-feature importance breakdowns and pinpoints accident-prone zones for targeted intervention.
metrics
- ML
- severity classifier
- multi-factor
- feature engineering
- interpretable
- risk-tier output
architecture
- 01Ingest — raw accident records dataset
- 02EDA — pattern & correlation analysis
- 03Feature engineering — time, weather, road, vehicle
- 04Classifier training — severity prediction model
- 05Output — risk-tier scores + feature importance
stack
- Python
- scikit-learn
- pandas
- EDA
- Feature Engineering
links
next project
SecureScout