Predicting Road Accident Severity in Kenya Using Machine Learning

Challenge Background

Kenya has an extensive road network that serves as a vital transportation infrastructure for both urban and rural areas. The country's road network consists of national highways, major roads, secondary roads, and rural access roads. The road network plays a crucial role in facilitating the movement of people, goods, and services across the country.

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. 

Goal of the Project

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.

Project Timeline

What you'll learn

Enhance Problem-Solving Skills. Learn data manipulation techniques. Learn ML modelling techniques. Learn web development.