Road accident risk Prediction and Classification using Machine Learning

Sanjay Kumar Patre , Dr. Ashish Kumar Tamrakar

road accident prediction; machine learning; Kaggle S5E10; XGBoost; SHAP; accident_risk; severity classification; LightGBM; deep neural network; intelligent transportation systems

Road traffic accidents represent one of the foremost preventable causes of mortality and morbidity globally, claiming approximately 1.35 million lives annually. This paper presents a rigorous, data-driven framework for road accident risk prediction and multi-class severity classification employing an ensemble of supervised machine learning algorithms.

Published Paper   E-Certificate

"Road accident risk Prediction and Classification using Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 1, page no.b659-b673, January-2026, Available :https://ijsdr.org/papers/IJSDR2601182.pdf

Volume 11 Issue 1, January-2026

Pages : b659-b673

Paper Reg. ID: IJSDR_310025

Published Paper Id: IJSDR2601182

Downloads: 000141

Research Area: Science and Technology

Country: -, -, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2601182

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2601182

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition