Accident Severity ML
A machine learning web app that predicts motor vehicle accident severity (Fatal or Non-Fatal) and recommends emergency deployment levels using Karnataka State Police FIR data. Trained on 329,000+ real accident records spanning 38 districts across 2016–2024.
Live App
What it does
Severity Prediction
Classifies accidents as Fatal or Non-Fatal from district, road type, month, year.
Deployment Level
Maps prediction to High Deployment or Standard Response with guidance.
Insights Dashboard
Five charts — monthly trends, severity split, top districts, road types, fatality rate by year.
Real KSP Data
329,000+ FIR records from 38 Karnataka districts, 2016–2024.
Tech Stack
| ML Model | CatBoost — gradient boosting with native categorical support |
| Web App | Streamlit with dark theme |
| Data Pipeline | Pandas — 1.69M FIR rows filtered and deduplicated |
| Evaluation | scikit-learn — stratified 80/20 split |
| Deployment | Streamlit Community Cloud, auto-deploys from main |
Context
Built during KSP Datathon 2024 — a competition organised by Karnataka State Police to extract insights from their open FIR dataset. The dataset spans 1.69 million FIR records across all crime categories; we filtered to motor vehicle accidents and trained a classifier to distinguish fatal from non-fatal outcomes using contextual features available at report time.
The ~59% accuracy honestly reflects the constraint: fatal vs non-fatal outcome depends heavily on vehicle speed and collision type, which are not recorded in the FIR master table. The model is useful as a baseline risk signal from location and time context alone.
Team
Shivam Sharma · Krishnan Lakshmi Narayana · Bhavya Vishal · Madhuri