Paper Contents
Abstract
Road traffic accidents are a major global concern, leading to significant fatalities and injuries each year. Anticipating the severity of such incidents is vital for governments, transport authorities, and healthcare systems to design preventive measures and ensure quick response. This paper investigates the use of Multilayer Perceptron (MLP), a supervised machine learning model, implemented in Python for accident severity prediction. Unlike conventional statistical methods, MLP demonstrates superior performance in handling nonlinear and high-dimensional accident data, offering better accuracy and feature representation. The study reviews related literature, explains the dataset and preprocessing steps, describes the design of the MLP model in Python, and evaluates its effectiveness in predicting severity levels ranging from minor to fatal.Keywords: Machine Learning, Multilayer Perceptron (MLP), Traffic Accident Severity, Python, Predictive Modeling, Supervised Learning
Copyright
Copyright © 2025 Ishwarya S. This is an open access article distributed under the Creative Commons Attribution License.
