Predicting RTC Severity using Machine Learning
Challenge Background
UK RTCs which have resulted in a persons death have been on a downward trend since the 1960s - however in 2020 1,516 people lost their lives on UK roads. The UK road systems, especially in Liverpool, are dated which means they have not been upgraded to reflect the increase of cars on the road. This means there are still preventative measures that could be implemented to prevent even more deaths on UK roads.
The UK government compiles and disseminates extensive data about road incidents around the nation (often once per year). This data is particularly fascinating and thorough for analysis and research because it contains, but is not limited to, geographic areas, weather conditions, vehicle types, casualty numbers, and vehicle manoeuvres.
Project Timeline
1. Data preprocessing
2. Exploratory Data Analysis to draw insights
3. Feature Engineering - creating new features based on insights drawn from EDA.
4. Model Development
5. Model Evaluation and Deployment - perhaps on AWS or Google Cloud.
What you'll learn
Data Processing, Exploratory Data Analysis, Feature Engineering, Model Development, Model Evaluation and Model Deployment.
First Omdena Local Chapter Project?
Beginner-friendly, but also welcomes experts
Education-focused
Duration: 4 to 8 weeks
Open-source
Your Benefits
Address a significant real-world problem with your skills
Build your project portfolio
Access paid projects (as an Omdena Top Talent)
Get hired at top organizations
Requirements
Good English
Suitable for AI/ Data Science beginners but also more senior collaborators
Learning mindset
Application Form
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