Road Accident Analysis

Description

Road Accident Data Analysis is a data-driven project that explores crash patterns using Python. It analyzes accident timing, weather, lighting, and injuries using libraries like Pandas, Matplotlib, and Seaborn. The insights help identify high-risk situations and support better traffic safety planning.

Key Highlights

• Visualized crash data trends across different timeframes and weather conditions.
• Analyzed the impact of road conditions and driver behavior on accident severity.
• Identified peak accident hours and high-risk scenarios using visual tools.

Technologies Used

1. Python
2. Numpy
3. Pandas
4. Matplotlib
5. Seaborn
6. Data Visualization

Optimized Performance & Code Quality

• Optimized Performance: Used vectorized operations and efficient filtering to handle large datasets.
• Optimized visualizations for clarity and performance in Jupyter Notebooks.
• Ensured High Code Quality: Modularized the code with comments, markdowns, and consistent structure.
• Maintained code readability with meaningful variable names and logical sections.
• Impact & Achievements:
• Highlighted critical accident trends that can aid traffic policy decisions.
• Report shared with stakeholders for potential road safety improvements.