How can I use the rule-based learning algorithms for this example

问题

I have data as follows in order to do a predictive learning as to what feature do people find attractive in a model when purchasing clothes online.

So I have data as follows.

COLORofCLOTHING  MODELHAIR_COLOR MODEL_BUILD SELLER_CATEGORY
  Red               Black         Lean           1
  Blue              Brown         Lean           5
  Black             Blonde        Healthy        10

In order to predict if the clothing will sell well given a set of attributes. However seller category can be anything between 1 to 10 (1 being best and 10 being worst) I am not sure how to approach this problem. I am using weka for this purpose. Can people please give me ideas on how to approach this problem?

basically I want to build a model which learns the features like color of the clothing etc and can predict how well the clothes will sell.

回答1:

Transform and normalise your dataset into something along the lines of:

 color_red   color_blue color_black  hair_black  hair_brown  hair_blonde ... prediction
 1           0          0            1           0           0           ... 0
 0           1          0            0           1           0           ... 0.5
 0           0          1            0           0           1           ... 1

Random Forests and Neural Networks should be able to give you predictions.

来源：https://stackoverflow.com/questions/13983955/how-can-i-use-the-rule-based-learning-algorithms-for-this-example