# Can I use categorical data and Decision Trees to regress a continuous variable?

Is there a way to take a set of data that consists of discrete values and predict a continuous value? Take for instance data that looks like:

sample matrix of jewel data
color |  size | shape
['red' ,'large','square']
['blue','small','circle']
['blue','small','square']

sample array of price labels
[9.99, 7.00, 6.37]


Can I do Decision Tree Regression on this to predict the price of a jewel with a given set of features? What if some of the data is continuous? Also is there any way I can/should pre-process the categorical data other than onehot encoding?