I know a number of teams on the leaderboard were using this contest as a way to learn more about data mining.

Rather than waiting for the best approaches to be formalized in papers and at ISMIS, it would greatly aid our learning to read short summaries of others' approaches via this forum.

Personally, I was able to get an accuracy of .74 by combining several submodels (in part by using class-probability estimates as weights). The two most effective classifiers in these models were MultilayerPerceptron and Logistic (which I ran using weka). Using PCA also helped to simplify the number of variables (191 to 75, while maintaining 95% of the variance).

Could others share (at least brief) insights from their learnings?

Thanks very much,

John McDowell

The Wharton School at the University of Pennsylvannia