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I'm software engineer of an E-commerce company, facing a problem like this:

An e-commerce shop sells their products daily and wants to know what conditions that might improve their sales. I'm building a AI sales predictor based on:

Categorical variables

  • week days (Mon, Tue, Wed,... Sun)
  • day period in a month (<10, 10<= ... <= 20, >20)
  • event level of that day (A, B, C, S, R)

Continuous variables

  • number of months data has been training (1, 2, 3, 4, ...)

I'm looking for a best model to fit mixed independent variables like this. Any ideas or sites could you redirect me to?

Many thanks!!

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  • $\begingroup$ Just to be clear: The feature "number of months" is acutally not continuos, since positive integers aren't. $\endgroup$ – Simon Jun 2 '17 at 7:27
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One model that I know can handle both ordinal and nominal variables are Gaussian Processes.

Not only you can use mixed types, but you can also model prior knowledge into the prediction process by defining a Kernel for the covariance matrix accordingly.

When I first learned about them, this video helped me a lot: Neil Lawrence, https://www.youtube.com/watch?v=pmeAgona_to&t=3893s

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  • $\begingroup$ Thank you pretty much. If you could give a clearer example, it would be a lot helpful. $\endgroup$ – Binh Nguyen Jun 6 '17 at 7:44
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Impute your missing data, transform categorical inputs into numbers and use a good old generalized linear model.

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