Roady: Improving Road Safety in Canada by Analyzing Vehicle Defects Using Machine Learning
This Omdena Local Chapter Challenge runs for 5 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 Winnipeg, Canada Chapter.
The problem
Vehicle defects can pose a significant risk to road safety, potentially leading to accidents, injuries, and fatalities. While regular maintenance and repairs can help address some defects, identifying potential issues early on is crucial in preventing accidents caused by mechanical failure. In this context, the development of a machine learning model to analyse vehicle defects and aid in road safety is essential. Such a model can help identify patterns in data related to vehicle defects and predict potential issues before they become a safety risk. By leveraging data from various sources, including car manufacturers, repair shops, government agencies, and telematics, the machine learning model can provide valuable insights to car owners and mechanics, allowing them to take proactive measures to address potential defects and improve road safety. Therefore, the problem statement is to develop a machine learning model for vehicle defect analysis that can aid in preventing accidents and improving road safety.
The goals
The goals of this project are:
- To develop a machine learning model that can accurately predict the likelihood of a vehicle having a defect based on various features, such as the make and model of the vehicle, the type of defect, and the location of the defect.
- To use the developed model to analyze data on vehicle defects and identify patterns and trends that could be used to improve road safety.
- To provide actionable insights to car owners, repair shops, and government agencies that could help prevent accidents and improve the overall safety of the road.
- To develop a scalable and sustainable solution that can be easily integrated into existing systems and technologies.
- To document the entire machine learning pipeline and communicate the results of the project to stakeholders in a clear and understandable manner.
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.
First Omdena Local Chapter Challenge?
Beginner-friendly, but also welcomes experts
Education-focused
Open-source
Duration: 4 to 8 weeks
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
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