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| Date of the event | September 12 - 15, 2018 |
| Location | Marietta Campus, J/Atrium Building |
| Cost | Free |
| Eligibility | This event is open to all KSU CCSE students (ACS, BASIT, IT, CS, SWE, and CGDD) who have passed their first few programming courses. Graduate students who have exempted all transitional courses or have passed at least three 5000 transition courses are also eligible. |
| Registration | More information about the event |
| HPCC Systems Hackathon Team | Big Data Analytics on HPCC Systems Are you interested in how analytics can identify trends which help businesses from a wide range of markets improve their decision making? Join our team and learn how to use the open source HPCC Systems platform and ECL-ML machine learning libraries, to build predictive models in the financial industry. |
| Challenge | Reconciliation, Fraud, Compliance & Analytics as a service is fast becoming a critical function in the FinTech industry. While many have previously tried to build homegrown solutions, few have been successful for various reasons. The DataSeers appliance provides a unique solution of accomplishing this. In the future, we would want DataSeers customers to not only know “what happened,” we also want them to know “if it will happen” or “when will it happen.” As part of the Hackathon experience, you will be provided with anonymized datasets of real transactional data, from which you will have to derive predictions around customer behavior. Hear from DataSeers CEO, Adwait Joshi, on how his company leverages HPCC SystemsYou will be presented with two problems, you can choose either of the one. Both are real world problems and you will be working with real dataset. Problem 1 You have been given a dataset of users on a banking platform. This dataset contains about a 100 "bad actors" a.k.k individuals on an ofac sanctions list. Given the entire data set of data and a full ofac list you would have to write an algorithm to detect the bad actors in the main dataset. Problem 2 Merchant name cleaning and grouping is fairly common challenge in payments industry. You have been given a transactional dataset which contains transactions performed at various merchants. Given dataset contains inconsistent format of merchant names. For example, ‘walmart’, ‘wal-mart’, wal mart etc. However, all these merchants are really the same. We think using a combination of data cleaning technique and machine learning algorithm you will be required to cluster the merchant name together in the best possible way. To make your machine learning algorithm more efficient you could use the other fields provided to besides the merchant name. |
| Data Source | TBA |
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| Mentors available during the Hackathon | The following mentors will be on site for the duration of the event: Dan Camper, Arjuna Chala and Richard Taylor, as well as our community users from DataSeers, Adwait Joshi and Gurjot Bandasha. The following mentors will be available to give assistance remotely: Roger Dev. |
| Slack Channel | https://ksuccsehackathon.slack.com/ |
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