I have a table with each row representing single printer model, its features, and price. I want to know how price is formed based on these features. What should I start with? Multiple regression, so I could cut off insignificant features? Cluster analysis to define small clusters with equal price? What are the ways to do the task?
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Welcome to the site.
I don't see how cluster analysis helps you with what you want to do. Regression is much more appropriate. That is, you have a dependent variable (price) and a bunch of independent variables (features) = a classic regression problem.
Of course, problems may arise. This would depend on how many different printer models there are, how many features there are, how many levels each feature has, and so on.
I think this depends on what you really hope to achieve with the analysis. In your case (given how you describe your data), both methods will be descriptive. Regression will help you answer a question such as which features have the strongest impact on price?, whereas clustering (such a MCA) will help you answer questions like which features are shared by printers of different price range?