@Johnny Fugers This could go a few different ways depending on the business objective. Is there a reason why you are asking about a supervised approach? If you are open to any ML approach, here's what I'd recommend... Recommendation Engine: The first thing that comes to mind is to use a recommendation engine. In spark 1.6, there's a collaborative filtering algorithm. This algorithm makes a recommendation based on a user's behavior and their similarity to other users. Frequent Patterns: Another interesting option, would be to perform frequent pattern mining. This examples provides a way to identify frequent item sets and association rules. Clustering: There are several additional clustering algorithms in Spark, which you can leverage to identify similar customer cohorts. You could also look to identify the total value of these cohorts based on their overall spending, or you could try to identify who they are based on purchases (i.e. single adults, elderly, parents, etc.). You might also want to check out time series analysis / forecasting if you want to predict spending, growth/seasonal patterns, etc. So most of my recommendations are to use an unsupervised approach, but that is what is a good fit for this type of data and use cases similar to this. ..If you'd prefer to take a supervised approach, you could always use a decision tree, random forest, or similar and try to predict the purchase value based on store, time of day, day of week, customer, and maybe even through in a customer segment variable (which you can derive using the above approach 🙂 ).
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