kraproject/nairobi-kenya-road-accident

# Kenya Road Accidents - Nairobi Chapter ## Predicting Road Accident Severity in Kenya Using Machine Learning ### Problem Statement 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. ### Project Goals This project aims to leverage machine learning (ML) techniques to: - 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. ------------------------- ## Contribution Guidelines - Have a Look at the project structure and folder overview below to understand where to store/upload your contribution - If you're creating a task, Go to the task folder and create a new folder with the below naming convention and add a README.md with task details and goals to help other contributors understand - Task Folder Naming Convention : _task-n-taskname.(n is the task number)_ ex: task-1-data-analysis, task-2-model-deployment etc. - Create a README.md with a table containing information table about all contributions for the task. - If you're contributing for a task, please make sure to store in relavant location and update the README.md information table with your contribution details. - Make sure your File names(jupyter notebooks, python files, data sheet file names etc) has proper naming to help othe …