How to select a bunch of optimized data from a larger data set? I have a data set containing the products to be sold to the customers. And the products were sold before, it has some information, like the review, ratings, how many were sold per product from the customers. Now, I want to select a bunch of optimized data from a larger data set based on the number of units sold, ratings, review etc all the information. The purpose is to increase the number of units to be sold in the future. How to do this? What kind of model, method, algorithms should be used here? If it is related to machine learning, what kind of machine learning tools should I use here? Thank you so much.
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1$\begingroup$ You can definitely do sales prediction and analytics. But, what do you really mean by: select a bunch of optimized data from a larger data set? $\endgroup$– Dawny33Commented Nov 23, 2015 at 16:51
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$\begingroup$ I have some products, each product have their own selling information, like # of units sold before, review, ratings, etc, want to select a subset from the data to recommend to the retailers, so to increase the # of units being sold for the retailers. I want the subset to be like "optimized" subset, so that it can increase the retailers' # of units sold as much as possible. $\endgroup$– user3634601Commented Nov 23, 2015 at 16:57
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$\begingroup$ Unless you have a compelling reason why, generally you do not want to subset data. Rather, you will train on all data. I think the optimization you are referring to is the ability to provide an optimal recommendation. For this you could look into collaborative filtering techniques. $\endgroup$– user13684Commented Nov 23, 2015 at 19:24
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$\begingroup$ Thank you so much. For the collaborative filtering, there is a project about it: blog.yhathq.com/posts/recommender-system-in-r.html. I took a look, but when you do it, you need a reference data point for referring. Here we do not have a reference data point. So I am wondering how. Thank you. $\endgroup$– user3634601Commented Nov 23, 2015 at 21:17
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$\begingroup$ Nice blog btw... By reference data point do you mean that you do not have a similarity measure for users in your system? You will create one, e.g. in your case it might be finding the Jaccard similarity of users based on their purchases. See datascience.stackexchange.com/questions/8873/… for links on collaborative filtering and creation of a utility matrix. $\endgroup$– user13684Commented Nov 23, 2015 at 21:31
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