Imagine if products are classified as "Hot" and "Normal" based on their total annual sales.
I want to detect what are the possible variables / attributes that make a product "hot", based on this knowledge we can "config" a new product (or even an old one) so that it becomes "hot".
Another use case: if we know products with attribute A will be "hot" only when condition C is present (A and C are included in the data ofc) then we will not roll out such products until condition C is present (like certain season, certain holidays).
What methods do you suggest for this problem?
My current thought: This is a classification problem. Make a decision tree, and then check what are the decision points / rules and pick those as the "cause" (of course only if those coincide with the domain knowledge).
Another idea: go with neural nets -- (can I find the contributing attributes using this?)
I appreciate if you give me some advice and some tips for approaching this problem.
Important: The goal is not to classify the new products (although that will be a side effect) the main goal is to detect the "causes" of being "hot".