ML - Based Traffic Violation Severity Scorer

Abstract: The title of this project is "Ml-Based Traffic Violation Security Score.” This project is developed using HTML, CSS, Bootstrap, and JS for the front end, along with a Python back end, where the framework is Flask.This project is based on the evaluation and prediction of the risk of accidents happening on the road. It is based on the analysis of the history of accidents, along with detailed information regarding the drivers, vehicles, and violations, to evaluate the potential hazards that may occur. The features that are considered for the prediction of the potential causes of accidents include Belts, Personal Injury, Property Damage, Commercial License, Commercial Vehicle, Contributed To Accident, Vehicle Type, Year, Make, Model, Color, Charge, Driver City, Driver State, Arrest Type, and Violation Type. It uses an algorithm that can handle the features efficiently for the prediction results. On the basis of the obtained results, it classifies the level of risk as Low, Medium, or High. This will help you decide how to use your resources to issue warnings and prevent accidents before they happen. It uses real data to help you make smart decisions and keep the roads safe by preventing accidents. It’s a combination of past data, predictions, smart scoring, and an efficient system that can be used to manage traffic.

Keywords: Machine Learning, Traffic Violation Prediction, Road Safety Analysis, Random Forest Algorithm, Traffic Risk Assessment, Traffic Violation Security Score, Accident Prediction, Intelligent Traffic Management.