Authors :
Preshita Bhortake; Vivek Barhate
Volume/Issue :
Volume 8 - 2023, Issue 1 - January
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3WT21OR
DOI :
https://doi.org/10.5281/zenodo.7599119
Abstract :
India has a substantial population, which
contributes to a significant daily automobile commuter
population. This leads to several accidents occurring
every day. These mishaps frequently result in severe
financial hardship for families as well as the possibility of
fatalities. The goal of this article is to identify accidentprone areas and alert regular commuters to the incidents
that are occurring there. Accidents can occur at any time
and without warning, but as users of this interface, we
can be more cautious in locations where accidents occur
frequently. The user interface will alert a user to the
high-medium accident risk areas.
Keywords :
Random Forest Algorithm,GaussianNaïve Bayes algorithm, Logistic regression, Machine Learning.
India has a substantial population, which
contributes to a significant daily automobile commuter
population. This leads to several accidents occurring
every day. These mishaps frequently result in severe
financial hardship for families as well as the possibility of
fatalities. The goal of this article is to identify accidentprone areas and alert regular commuters to the incidents
that are occurring there. Accidents can occur at any time
and without warning, but as users of this interface, we
can be more cautious in locations where accidents occur
frequently. The user interface will alert a user to the
high-medium accident risk areas.
Keywords :
Random Forest Algorithm,GaussianNaïve Bayes algorithm, Logistic regression, Machine Learning.