Road accidents constitute a major problem in our societies around the world.
The World Health Organization (WHO) estimated that 1.25 million deaths were related to road traffic injuries in the year 2010. For the year 2016, the USA alone had recorded 37, 461 motor vehicle crash-related deaths, averaging around 102 people per day. In Europe, the statistics also indicate that each minute, there are 50 road deaths recorded in the year 2017. Can machine learning help us understand the causes and the factors that affect car crash severity?
In this project, we have done a complete machine learning pipeline from getting data through different sources, performing exploratory data analysis and formulating a real-world problem into a machine learning model.
The main use of machine learning over here is to predict the accident-prone zones such that we can narrow down the service space. Our primary goal is to make a prediction of accident-prone zone very effective such that in future we can prove that out of all accidents most of the accidents occurs in the zone which we have predicted as accident prone.