Final Project Peer review --cjb327

[celinebrass]

The goal of this project is to build a model that predicts whether or not a consumer will be a repeated buyer of the online merchant. This will help merchants target their advertising towards customers who are more likely to return.

Strengths :

You use a wide variety of models -- I can see you really experimented and did not just assume one might work better than the other. I think the bredth of models you tried helped you gain a sense of which models you should pursue further.

Your report is broken up into clear, manageable subsections. The organization of information makes it really easy to grasp the main ideas you are trying to convey. This is especially helpful in such a technical report :)

Areas of improvement :

My biggest critique is that your report appears unfinished. You briefly touch on several models you used, several of which had promising error results, however, at no point do you select a "best model" and really evaluate its implications on shopping trends There is no "improvements" or "further steps" section.

I think some of the explanation of how the models work is unnecessary. Instead, I would have focused on dedicating more space to explaining your methods. For example, the picture from the textbook of random forests are unnecessary within the text itself and should have perhaps been listed as a reference instead. The same can be said for many of the bold equations. To touch on what I would have liked to learn more about -- what were the weights associated with the perceptron algorithm? What does this reveal about which features affect a consumers likelihood to return?

I think the application of this model is a little backwards. You state that the benefit of a model like this is so that buyers who are more likely to return can be sent promotions, but it doesn't really make sense for merchants to be spending more resources on these customers, as they are already more likely to return. Wouldn't a better use of this information be to help merchants figure out which customers aren't coming back so they can develop strategies to change that? Though this does not have as much to do with the technical aspect of the project, I think having a solid grasp of where the value lies in a model like this is fundamental to conducting a study like this.