I have a big list of spare parts with several parameters (material, weight, size, manufacturing complexity, ...). For some parts in this list, a price has either not been set or has to be adjusted in order to be in line with other parts. There are a few obvious and simple correlations in this dataset, for example:
- if material and complexity is the same, bigger parts are more expensive;
- if size and material is the same, more complex parts are more expensive;
- for equal size and complexity, more expensive material leads to higher price.
Trying to figure out all these rules by hand and sticking them together seems to be an endless endeavour, so I thought about training a neural network with the priced parts' parameters (input) and prices (output) and let it figure out the prices for the parts which don't have a price yet. The decisions of the NN could be supervised by an expert who knows the parts and could manually figure out a price.
Question 1) Is this a good idea in general?
Question 2) If yes, what type of NN would be most suited for such a problem?