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Date of the eventSeptember 12 - 15, 2018
LocationMarietta Campus, J/Atrium Building
CostFree
EligibilityThis 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

You 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 SourceTBA
General Instructions
  1. Please follow all the guidelines specified by the data provider.
  2. Provide a brief design/tech document describing the proposed solution (2-3 pages max).
  3. Provide the bio of all participants and their roles on the project.
  4. Use the HPCC Systems platform for executing the solution.
Rating Criteria
  1. Innovation involved
  2. Understanding of Big Data patterns and its application to HPCC Systems.
  3. Team work and execution
  4. Presentation quality
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.More information about our mentors and how to contact them

/wiki/spaces/hpcc/pages/23579358

Slack Channelhttps://ksuccsehackathonjoin.slack.com/t/ksuccsehackathon/shared_invite/enQtNDI4MzUwMjExNzk5LTY5ODQ0ZTNjNzliZmMzYmVjZGM3ZGE3MTNkZWFkZWVkZDNhZTE1ODUyZDkxNDg3ZWM0MjdkM2NmOGNjZWMwNmM

What can I do to prepare?

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