Check out the UCI Machine Learning Repository for a (huge) list of data / problems to test your ML-fu on : http://archive.ics.uci.edu/ml/ http://archive.ics.uci

Check out the UCI Machine Learning Repository for a (huge) list of data / problems to test your ML-fu on : http://archive.ics.uci.edu/ml/ http://archive.ics.uci.edu/ml/ library(randomForest) in R comes with some automobile data from the above repository and a small worked example which can be greatly expanded upon. For example, it's fun to try and predict manually whether the car is a 2-door or 4-door by staring at the data, which has 26 columns, so it may be not that obvious: > summary(imp85$numOfDoors) four two 112 81 The variable-importance plot reveals all : the bodyStyle is a key predictor! > varImpPlot(numDoors.rf) I uploaded it here :http://i.imgur.com/IU98gfo.png http://i.imgur.com/IU98gfo.png > summary(imp85$bodyStyle) convertible hardtop hatchback sedan wagon 6 8 63 92 24 Hope this is useful.