Accident Risk Prediction through Machine Learning: A Comprehensive Study

Abstract

Abstract—In the realm of road safety, the mitigation of accidents and their potential consequences remains a paramount concern. This research paper embarks on an extensive exploration of accident risk prediction, harnessing the capabilities of Extra Trees Regressor and XGBoost for the critical task of parameter importance analysis. The study is centered around the development of a sophisticated web-based prediction system, encompassing various facets such as system architecture, meticulous data preprocessing, and the intricate intricacies of machine learning model development. The dataset used for the same is from Kaggle comprising of data about accidents in US between 2016-2023 . Through the lens of parameter importance analysis, this research unveils the key determinants that underpin accident risk, casting a spotlight on the profound implications of these insights on prediction accuracy which is currently 85%. The ensuing discourse delves into the nuanced interpretation of results, culminating with a forward- looking perspective that outlines potential pathways for future research endeavors in this consequential domain. Keywords—Accident Risk Prediction, Accident Prevention, Road Safety, Data-Driven Analysis, Feature Selection, Risk Assessment, Machine Learning Algorithms, Data Preprocessing, Model Evaluation, Hist Gradient Boosting Regressor, XGBoost, Parameter Importance Analysis, Predictive Systems.