(San Diego, California, USA) -- Fair Isaac Corporation (NYSE: FIC), the leading provider of analytics and decision management technology, and the University of California, San Diego (UCSD) today announced the winners of the fourth annual UCSD data mining contest.
Fair Isaac has sponsored the data mining contest since 2004, which provides undergraduate and graduate students an opportunity to test out their data mining skills on a real-world data set. Fair Isaac pioneered the use of predictive modeling to represent and explain the underlying relationships in data and make predictions and classifications about future events.
In this year's data mining contest, participants were asked to build models that correctly predict useful information from two different data sets provided by DataQuick, a San Diego-based real estate information provider. The contest was open to all undergraduate students, graduate students and post-doctoral researchers studying full-time at an accredited college or university in the United States.
Fair Isaac and UCSD awarded prizes to first, second and third place winners in four categories: Refinance Prediction Graduate/post-doctoral; Refinance Prediction Undergraduate; Sale Price Prediction Graduate/post-doctoral; and Sale Price Prediction Undergraduate. The winners took home a total of $4,000 in prize money.
The Refinance Prediction winners in the graduate/post-doctoral category are:
1st Place: Rongsheng Gong and Hua Li, Team ISLAB, University of Cincinnati
2nd Place: Ka Cheung Sia, Team yuning, University of California, Los Angeles (UCLA)
3rd Place: Christopher Wassman, Team tol, UC Irvine
The Refinance Prediction winners in the undergraduate category are:
1st Place: Christopher Berger, Team ImaginaryRealty, UC Santa Cruz
2nd Place: Kei Shun Ma, Team Franleo, UCSD
3rd Place: Hyun Park, Team Mebapa, UCSD
The Sale Price Prediction winners in the graduate/post-doctoral category are:
1st Place: Kung-Hua Chang, Team Century21, UCLA
2nd Place: Rongsheng Gong and Hua Li, Team ISLAB, University of Cincinnati
3rd Place: Nikolaos Trogkanis, Team Ismene, UCSD
The Sale Price Prediction winners in the undergraduate category are:
1st Place: Hyun Park, Team Mebapa, UCSD
2nd Place: Wang, G. Fangwen, Team teamwang, UCLA
3rd Place: Chuan Sheng Foo, Team decaff, Stanford University
"Through this academic contest, we're offering students the opportunity to look at data in new and interesting ways in a real-world scenario," said Professor Charles Elkan, Computer Science and Engineering at UC San Diego. "We're proud to be associated with the analytics industry leader in recognizing our students' scholastic achievements through this innovative data mining competition."
"We're delighted to engage these talented students by sponsoring the data mining contest," said Dr. Andrew Jennings, vice president of Research at Fair Isaac. "This contest gives students an opportunity to discover what their peers at Fair Isaac have long known - that business is an inexhaustible source of analytic challenges. No other sphere of activity presents as great an opportunity to change the world with the skills you acquire at school."
In addition to sponsoring UCSD's annual data mining contest, Fair Isaac supports student internships throughout the year. For more information about the data mining contest, visit http://mill.ucsd.edu/.
About Fair Isaac
Fair Isaac Corporation (NYSE:FIC) combines trusted advice, world-class analytics and innovative applications to help businesses make smarter decisions. Fair Isaac's solutions and technologies for Enterprise Decision Management turn strategy into action and elevate business performance by giving organizations the power to automate more decisions, improve the quality of their decisions, and connect decisions across their business. Clients in 80 countries work with Fair Isaac to increase customer loyalty and profitability, cut fraud losses, manage credit risk, meet regulatory and competitive demands, and rapidly build market share. Fair Isaac also helps millions of individuals manage their credit health through the www.myFICO.com website.
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