ROAD ACCIDENT PREDICTION

Road accidents remain one of the leading causes of injuries and fatalities worldwide, posing significant challenges to public safety and transportation systems. Traditional methods of accident analysis often rely on historical statistics and manual interpretation, which limit their ability to provide timely and accurate predictions. This project leverages the power of machine learning (ML) and data science to develop a predictive model capable of identifying accident-prone scenarios based on diverse factors such as traffic density, weather conditions, road type, time of day, and driver behavior.
The proposed system integrates data pre-processing, feature engineering, and model training using algorithms such as Random Forest, Logistic Regression, and Neural Networks to evaluate accident likelihood. By applying advanced data science techniques, the model not only predicts the probability of accidents but also highlights key risk factors contributing to them. The outcome of this research is a decision-support tool that can assist traffic authorities, urban planners, and policymakers in implementing proactive safety measures, optimizing traffic management, and reducing accident rates.
This project demonstrates how data-driven approaches can transform road safety strategies, offering scalable solutions that combine predictive accuracy with practical applicability in real-world transportation systems.