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?