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Context: I'm currently writing my bachelor thesis and have decided to focus on what factors that contribute to students that have risky alcohol habits at my university. I am planing on doing a big survey to gather data about the students habits.

First Possible Approach: Since the classification problem is alcohol consumption I am having a slight issue in phrasing the question and its options. A similar study worked with a dataset based on educational data mining that used two measures Daily=daily alcohol consumption and Weekly=weekly alcohol consumption. The measures were 1 - very low to 5 - very high. Then they calculated the consumption as such:

(Weekly * 2 + Daily + 5) / 7

If the value was > 3 then he/she was classified as big drinker and if the value was < 3 he/she was not classified as a big drinker.

Second Possible Approach: However each year my university sends out a big survey to gather data about how much alcohol our students drink. They define a risky alcohol consumption as such:

  1. If you drink less than once a month then you have a low risk.
  2. If you drink 1-3 times a month then it means an increased risk.
  3. If you drink 1 time a week or often then that means you're in the risk zone.

Problem: What are you thoughts on the matter? I am not an data mining expert and that's why I am turning to you guys. Is it necessary for a binary classification as the similar study with a delicate matter as alcohol consumption? Or is perhaps 3-5 options as a measure more suitable?

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