Top Projects Announced for Data-X Fall 2020
The Data-X course (Applied Data Science with Venture Applications, INDENG 135/290) has just announced its top projects from Fall 2020. 140 students submitted twenty-eight high quality data science innovation projects to leverage the latest mathematical techniques and tools to solve highly relevant industry problems with the most impact.
The faculty and staff wish to congratulate all the teams for their hard work this semester! The Data-X team wishes to recognize the following three teams for their outstanding creativity, potential market value and technical rigor:
- Kinesis.ai – Faster, cheaper and smarter rehabilitation experience. This team aims to revolutionize the physical rehabilitation industry which is currently expensive, difficult to access, and vague about their tracking techniques. The team solves the problem using computer vision to recognize physical therapy poses and allow self-administration of physical therapy at home, which the team claims will result in lower cost of care, more patients per day, more time per patient, and clearer insights.
- GGWP – Using machine learning algorithms and publicly available data, team GGWP seeks to improve gaming by identifying and reducing toxic behavior from players. Toxic behavior in gaming creates a bad user experience, impacts player retention, and is difficult to identify because of the sheer number of conversations happen. To solve the problem, the team wishes to equip game developers with a machine learning classier model that will help them identify toxic behavior in their games.
- Anchain.ai Bitcoin Prediction – This team uses a machine learning model to help Bitcoin investors make more reliable investment decisions while minimizing risk. Bitcoin price is a risky investment due to its price volatility which is often driven by human irrationality. To assist investors, the team created a Bitcoin dashboard that forecasts the price, displays real-time trading signals, and live sentiment of users on Twitter.
Cheers to all the teams. More Data-X projects can be found at Data-X.
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