https://github.com/vedanthv/road-traffic-accident-severity-classification

An end to end road traffic severity classification ML solution based on real world data of accidents in Nigeria
https://github.com/vedanthv/road-traffic-accident-severity-classification

data-science machine-learning python

Last synced: about 6 hours ago
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An end to end road traffic severity classification ML solution based on real world data of accidents in Nigeria

• Host: GitHub
• URL: https://github.com/vedanthv/road-traffic-accident-severity-classification
• Owner: vedanthv
• Created: 2022-07-11T07:47:15.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-07-18T17:30:35.000Z (over 2 years ago)
• Last Synced: 2023-03-05T00:20:13.531Z (over 1 year ago)
• Topics: data-science, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 10.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# Road Traffic Accident Severity Classification

![road_traffic](https://user-images.githubusercontent.com/44313631/178222800-9d70bd6a-665a-42ae-84a0-45e0ea25e40a.jpg)

## Problem Description

Every year the lives of approximately 1.3 million people are cut short as a result of a road traffic crash. Between 20 and 50 million more people suffer non-fatal injuries, with many incurring a disability as a result of their injury.

Road traffic injuries cause considerable economic losses to individuals, their families, and to nations as a whole. These losses arise from the cost of treatment as well as lost productivity for those killed or disabled by their injuries, and for family members who need to take time off work or school to care for the injured.

This data set is collected from Addis Ababa Sub-city police departments for master's research work. The data set has been prepared from manual records of road traffic accidents of the year 2017-20. 

All the sensitive information has been excluded during data encoding and finally it has 32 features and 12316 instances of the accident. Then it is preprocessed and for identification of major causes of the accident by analyzing it using different machine learning classification algorithms. 

## Overview of the Project

Read this [blog]("https://vedanthvbaliga.netlify.app/blog/road-traffic-accident-classification/") for a complete understanding of the project right from the preliminary analysis to explanable AI with SHAP! PS : Blog is under draft but you can read it as and when its updated.