I'm working on the following problem and was hoping that someone in
the forum could lend a hand. I've got the following data:

publication | article | sales conversion
forbes | top 10 businesses | 0.283
newsweek | best 10 pet stores | 0.347
 |  | 

The goal is to use the publication and article to select future
articles which we predict will have a high sales conversion rate. I've
transformed the data into the following ARFF file.

@resource articles

@attribute publication {forbes, newsweek, ...}
@attribute top {yes, no}
@attribute 10 {yes, no}
@attribute businesses {yes, no}
@attribute best {yes, no}
@attribute pet {yes, no}
@attribute stores {yes, no}

@attribute sales_conversion NUMERIC

{0 Forbes, 1 yes, 2 yes, 3 yes, 7 0.283}
{0 newsweek, 4 yes, 2 yes, 5 yes, 6 yes, 7 0.283}

What do you feel would be the best way to approach this problem (I'm
using WEKA)?

Thank you for your help,

CLASS-L list.
Instructions: http://www.classification-society.org/csna/lists.html#class-l