US Accidents Exploratory Data Analysis

Performed exploratory data analysis (EDA) on the Kaggle US Accidents dataset (7M+ records), uncovering insights into accident severity, weather conditions, and temporal-spatial patterns using Python (Pandas, NumPy, Matplotlib, Seaborn).

I started this project after coming across this dataset and, being a Civil Engineering student specializing in transportation engineering, I was intrigued by its potential. Understanding accident patterns is crucial for evaluating the historical performance of roads and the level of service they provide. Through my analysis, I observed that coastal regions experience more frequent accidents. While one reason could be human behaviour - such as carelessness when people travel to coastal areas for vacation trips - there is also a civil engineering perspective to consider. Weather and climatic conditions in these regions may affect road durability and serviceability, increasing the likelihood of accidents.

However, road conditions alone may not fully explain these patterns. My future goal is to integrate data on road infrastructure and conditions to validate this hypothesis. In the meantime, the findings can help encourage safer driving practices and better enforcement of traffic regulations, especially in high-risk areas.