Learning decision tress

Design and implement a decision tree classifier that learns to classify the abalone data set from the UC Irvine machine learning repository . You may download and learn to use WEKA's decision tree classifier instead of writing your own decision tree classifier.

Extra credit Run your decision tree on three other data sets from the UCI ML repository

General rules
No code sharing. Good Luck

Turning it in

1. Your source (for your decision tree classifier), your data, executable (specify linux, android, mac, or windows).
2. Analyze your results and explain how you chose you training set, how you chose your test set, and how you determined performance? How well can you predict age? How did you discretize continuous values? How did you prune your rules?
3. A sorted set of pruned rules representing your decision tree along with their accuracy
4. Classification accuracy on a random sample of 500 instances