🚦 Crash Analytics Dashboard
An interactive dashboard built to analyze when crashes occur, which conditions increase risk, and how vehicle characteristics relate to crash severity, using real-world crash data.
📌 Overview
This project explores crash patterns across time, weather, road conditions, and vehicles. The dashboard enables interactive exploration through filters and visualizations, helping uncover conditions that lead to higher injury severity.
Built with Python, Pandas, Plotly, and Dash.
❓ Key Questions
When are crashes most frequent during the day and week?
Which weather and lighting conditions are associated with higher crash severity?
How do road surface conditions affect outcomes?
Do vehicle age and type influence injury severity?
📊 Features
Hourly, weekly, and monthly crash trends
Crash patterns by lighting condition
Weather vs injury severity analysis
Weather vs road surface interaction
Vehicle year vs injury severity
Vehicle body type vs collision type
🛠️ Tech Stack
Python
Pandas, NumPy
Plotly, Dash
Open: http://127.0.0.1:8050/
💡 Key Insights
Crashes peak during weekday evening rush hours.
Fog and snow/ice show a higher share of severe injuries despite fewer crashes.
Unlit dark conditions increase crash severity.
Older vehicles are associated with more severe outcomes.
📁 Files ├── app.py ├── Crash_Reporting_-_Drivers_Data.csv ├── README.md
🎯 Why This Project
Demonstrates end-to-end data analysis, cleaning, feature engineering, and interactive visualization skills using a large real-world dataset.