Predicting Road Accident Severity in Kenya Using Machine Learning

This Omdena Local Chapter Challenge runs for 8 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world. 

You will work on solving a local problem, initiated by Nairobi, Kenya Chapter.

The problem

Road accidents are a significant public safety concern in Kenya, leading to loss of lives, injuries, and economic costs. These have an effect on society when the sole breadwinner loses their life or is unable to work due to injuries, leaving the family vulnerable to hunger and poverty. Understanding the factors contributing to accidents, identifying accident-prone areas (hotspots), and predicting accident severity can play a crucial role in improving road safety measures and preventing economic impacts on society.

The goals

This project aims to leverage machine learning (ML) techniques to analyze and predict road accidents in Kenya:

• Analyze and understand the patterns and contributing factors of accidents on Kenyan roads using historical accident data.
• Identify accident-prone areas (hotspots) by analyzing accidents’ spatial and temporal patterns.
• Develop a machine learning model to predict the severity of accidents based on various factors such as road conditions, weather, time of day, and vehicle types.

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work