sugatagh / Road-Traffic-Accident-Severity-Classification

The aim of the project is to build prediction models to classify severity of road traffic accidents (slight injury, serious injury or fatal injury) based on various relevant information regarding the involved vehicles, drivers, casualties and surrounding conditions.

Road Traffic Accident Severity Classification

Project Report

https://sugatagh.github.io/dsml/projects/road-traffic-accidents-severity-classification/

Overview

• The severity of road traffic accidents may depend on several factors, including the attributes of the involved vehicles, drivers, casualties, and surrounding conditions.
• In this project, we aim to predict the severity of an accident in terms of a given hierarchy (slight, serious, and fatal), with the help of information on the relevant attributes.
• A detailed exploratory data analysis on the dataset is carried out.
• The observations obtained from EDA are used in the data preprocessing stages.
• We employ decision tree, random forest, XGBoost, and ExtraTrees classifiers to predict the severity of an accident as slight, serious, or fatal.
• We apply hyperparameter tuning to the XGBoost classifier, which appears to perform best among the baseline candidates. We also tune the ExtraTrees classifier and the random forest classifier, as their performance is very close to that of the XGBoost classifier.
• The final model obtains a weighted $F_1$-score of $0.795060$ on the test set.