For Detection of Vehicle Accident Using A Data Science with Web Application

Authors

  • Ajay Kumar Pathak 1 Research Scholar, YBN University, Rajaulatu, Ranchi, Jharkhand, India
  • Birendra Goswami Dean, Dept of CS & IT, Director of Training & Placement, YBN University, Ranchi, Jharkhand, India

DOI:

https://doi.org/10.48165/tjmitm.2021.1107

Keywords:

Streamlit, Data Visualization, Vehicles, Data Science, Web Application

Abstract

These days’ vehicles have become a basic requirement of the population and it is increasing fast. Statistics show that the current situation is very alarming as every minute at least an accident takes place on an average, In India alone 1,21,000 average  (last five years 747,362 (year 2017 -Total Number of Persons Killed (in numbers) 147,914, year 2018 Total Number of Persons Killed (in numbers) 151,418, year 2019 Total Number of Persons Killed (in numbers) 151,114) people die per year due to vehicle accidents. As there is a large data on vehicle accident it is difficult to find the particular place where the accidents are occurring frequently and how many people are injured. If the location is known then safety measures are taken at that particular place. In this paper a Web Application using Data Science is proposed to know the details of the crash incident. Generally, Data Science doesn’t handle the front-end development and it is more focused on the internal functions of an application. Data visualization is the most important step of data analysis. Matplotlib, Plotly, Seaborn, Geoplotlib are some of the libraries that are available for data visualization which allows visualizing a large variety of charts and plots but these libraries do not have any functionality to make them in the form of a web application. Streamlit is used to make a Web Application and display the application in the localhost.

Published

2021-12-12

How to Cite

For Detection of Vehicle Accident Using A Data Science with Web Application. (2021). Trinity Journal of Management, IT & Media (TJMITM), 12(1), 45–50. https://doi.org/10.48165/tjmitm.2021.1107