Students from the Faculty of Engineering were in the top three teams in the Data School Hackathon 2023 challenge, presented by the School for Data Science and Computational Thinking at Stellenbosch University in partnership with Standard Bank Lab. This year’s Hackathon required teams to use telematics data to create behavioural profiles of minibus taxi drivers.
The teams, Neural Ninjas and Hackabank, were placed second and third, respectively, with Hackabank also named the best women’s group. Team Daedalus, consisting of students from the Department of Computer Science, was the overall winner.
The Hackathon aims to bridge the gap between academia and the data science and software engineering industries by allowing students to solve business problems for a cash prize. The total prize money of R46 000, generously sponsored by Standard Bank Lab, was divided into R22 000 for the winning team, R12 000 for the runners-up, and R6000 for third place. A special price of R6000 also went to the best women’s group.
Mathew Prozesky, Divan van der Bank, Emma Sharratt and Nicholas Campbell formed the team Neural Ninjas. They are all fourth-year Mechatronic Engineering students. The Ninjas developed a unique solution focused on understanding the problem’s context and simplifying the solution by using their engineering intuition to find a creative solution.
“We generated a risk score, providing taxi owners real-time information on how their drivers operate. Our solution is incentivised and aims to improve our nation’s perception of taxis by promoting a culture of safer driving and service to communities,” says Emma.
“Our proposed solution rewards responsible drivers by involving them in initiatives serving under-resourced communities, like a bulk Checkers Sixty60 delivery programme. This offers drivers extra income during their usual rank waiting time.”
Divan says they knew they didn’t have the data analytics expertise that some of the other groups would have. “Therefore, we had to find a different approach to understanding and solving the problem given. Instead of a machine learning approach, we opted to find a solution based on the insights the community and regular taxi users could provide us.”
Nicholas adds: “We created a solution that would cater to the needs of the taxi owners and drivers alike. Our solution provided them with transparent, scalable, and reliable results that would have a positive impact on the community, rather than just highlighting the problems that were present.”
The team met in their first year and have become good friends through helping one another through shared challenges in engineering. They decided to enter the competition, seeing this as a great opportunity to work together as friends. “All of us enjoy coding and thought it would be a great way to put our skills to the test. The extra challenge was something different to the work we normally do for most of our modules, but we thought we could find an innovative approach with a different background to the data analytics and computer science students,” Matthew explains.
Emma concludes by saying the challenge reminded them of the “important phenomenon that you won’t know unless you try!”
The all-female team Hackabank were Danel Adendorff, Lise Prinsloo, Minette Farrell and Rachel Rawraway, all from the Department of Electrical and Electronic Engineering, specialising in Data Engineering, who decided to enter as they all share a passion for data science and innovation. The team built a cloud-based solution using Google Cloud Platform.
Rachel says their studies in Data Engineering equipped them with a strong foundation in data analysis, machine learning and cloud computing. “These skills were instrumental in approaching the Hackathon’s telematics challenge. We leveraged cloud-based solutions, data pre-processing techniques and machine learning algorithms to create comprehensive behavioural profiles of taxi drivers,” she explains.
She adds: “Participating in this Hackathon was a tremendous learning experience. We realised the potential of data science to drive positive change in critical industries, such as transportation. Furthermore, we learned the importance of adaptability and versatility in facing complex challenges. These key takeaways enriched our academic and professional journey and inspired us to keep pushing the boundaries of what’s possible in data engineering.”
📸- (left) Team Neural Ninjas and (right) Team Hackabank.
(Article by Amber Viviers)
