| dc.description.abstract | Many nations are concerned about the high number of traffic fatalities, which has led to a search
for efficient prevention methods. This study addresses road fatalities brought on by drunk
driving incidents. The study uses machine learning algorithms to detect and comprehend
patterns related to fatalities brought on by drunk driving. The study builds prediction models
that can help with early detection and policy development using a number of variables,
including crash features, demographic information, and historical trends. This models are
formulated by various machine learning techniques. Supervised machine learning algorithms,
such as Random Forests (RF), Decision Tree (DT), Naïve Bayes (NB), Logistic Regression
(LR), Support Vector Machine (SVM) are implemented on traffic fatalities dataset. The
findings of this study indicate that the RF model can be a promising tool for predicting of death
by drunk driving. RF algorithm has shown better performance with 100% accuracy than DT
with 95%, NB with 74%, LR with 94% and SVM with 67% accuracy. | en_US |
