I have a table with many financial information of thousands of companies around the world (revenue, income, number of employees, total assets etc) and an extra information indicating whether that company owns a specific high value machine ("1" if the company has and "0" if the company hasn't).
My goal is to identify which companies don't have this machine, but are potential customers. My first approach was to use a classification algorithm, such as SVM, random Forest etc and use those financial characteristics as predictors of the "has machine"/"hasn't machine" column. The "potential customers" would be, actually, the individuals the model classified as "1" (has machine), but they are actually "0" (hasn't machine). In other words, the false positives of this model.
I am not comfortable with this approach (after all, a perfect model wouldn't indicate a single potential customer!), but I don't know any other way I could approach this problem. I was wondering if someone could give me some direction!