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Problem: I want to identify the characteristic(s) of people who would shop on Monday vs Sunday (or any such dichotomous response variable).

I have over a million observations and >50 variables/characteristics of customers in my data. I have this Day variable(binary) in my dataset too. Some of the variables are income intervals, age intervals, type of family,gender,Place of residence etc.(Not continuous data).

I am looking to answer this for example as following:People shopping on Sunday are: Florida-income range 50k-100k-family with kids-aged 30-45. Basically a profile of with as many characteristics to narrow down as possible.

Any help with the statistics/analytics methods that can be used in this context would be great.Thanks!

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    $\begingroup$ Hi Robin, welcome to CV! On this site there's no need to say "thank you" or "any help would be appreciated" at the end of your post - it might seem rude at first, but it's part of the philosophy of this site (tour) to "Ask questions, get answers, no distractions" and it means future readers of your question don't need to read through the pleasantries. $\endgroup$
    – Jan Kukacka
    May 14, 2018 at 15:17

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You want interpretability in your model. Decision trees give you exactly that. You can use scikit-learn Decision trees, to get a tree which says for example if income range is less then 30k , they shop on Monday etc.

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