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Using the UCI dataset here: http://archive.ics.uci.edu/ml/datasets/Congressional+Voting+Records

Each congressperson votes yay, nay or present (basically abstains) on 16 issues. My teacher wants us to treat missing values (vote present) as a third category. So I thought of making a new column for each vote. So columns for votes 1 to 16 are either 1 for yay or 0 for nay and no value or NaN for abstaining. Then the new 16 columns called abstention are either 1 if the corresponding vote column was a NaN or no value and 0 otherwise. We can use up to 3 items to find some associations.

Is the above correct method to create a third category? Then when I search associations using Python apyori or R, do I include 3 pairs of (vote, abstention)?

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No, given your teacher's request, you should not record abstain votes as missing values, but rather as a distinct third category. So for every question you have a factor variable with three levels. Otherwise many routines would just ignore the abstain votes instead of including them in the calculations.

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  • $\begingroup$ oh, I see so for association rules I need to binarize so there would be six classes. is yay, is not yay, is nay, is not nay, is abstain, is not abstain? $\endgroup$
    – mLstudent33
    Commented Mar 13, 2020 at 17:45
  • $\begingroup$ actually three would do, is yay, is nay, is abstain $\endgroup$
    – mLstudent33
    Commented Mar 13, 2020 at 17:49
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    $\begingroup$ Yes, three dummy variables will do. There is a handy function for this in Python: pandas.get_dummies(). $\endgroup$
    – Arne
    Commented Mar 13, 2020 at 22:19
  • $\begingroup$ haha, I spent so much time making the dummy variables manually before seeing this. And I didn't even do it right. $\endgroup$
    – mLstudent33
    Commented Mar 27, 2020 at 3:15

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