Road accidents remain a major public safety concern, leading to significant loss of life and economic burdens. This project introduces an AI-Powered Road Accident Prediction System designed to analyze accident patterns using UK road safety data. The system employs data preprocessing, exploratory data analysis (EDA), and a Logistic Regression model to assess accident severity based on various influencing factors such as weather conditions, road types, and vehicle attributes. A web-based interface built with Streamlit enables users to input key accident-related data and receive predictive insights in real time. The system enhances road safety by providing data-driven insights, helping authorities and policymakers implement targeted safety measures. This research highlights the system's development, functionality, and potential impact in reducing accident risks and improving transportation safety.

Keywords: Road Accident Analysis, Machine Learning, Logistic Regression, Predictive Analytics, Data-Driven Decision Making, Traffic Safety.