In the tutorial part of this I mentioned a couple of things you can do to handle missing attributes if you are coding a decision tree algorithm: * Simply have another possible value that an attribute can take - 'blank'. This is then treated as any other value, with branches in a tree being labelled by this if necessary. This is useful if some meaning can potentially be attached to missing atributes. * Replace the black value with the value that occurs most frequently in a similar context.