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I am working on this dataset: https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016, and it has a lot of categorical variables, while I am more used to work with the continuous ones.

Except for the binary "sex" variable, which is simple, there are variables with more categories: "generation", "continent" and, especially, "country". There are ~100 countries, and countries are definitely not ordinal, so I suppose I cannot just convert countries to numbers since the distances between them will not make sense. But at the same time I do not have a good feeling about make ~100 columns for countries dummy variable.

  1. Is it a good approach to create these dummy variables and then just carry out dimensionality reduction? Which kind of dimensionality reduction would be the most suitable?
  2. Is there a better alternative I don't know about?
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  • $\begingroup$ Have a look at stats.stackexchange.com/questions/146907/… $\endgroup$ – kjetil b halvorsen Jan 12 at 3:36
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    $\begingroup$ Everything depends on how you are going to use these categorical variables. If you are talking of reducing the number of categories - see the link provided above. If by dimensionality reduction you mean (as normally) imbedding the many categories in few dimensions that are continuous variables - then this quantification of categorical variables is known as (multiple) correspondence analysis. $\endgroup$ – ttnphns Jan 12 at 3:41
  • $\begingroup$ @ttnphns yes, I was originally thinking about performing MCA, but I am not sure if it make sense in this case (as I've mentioned, I have experience only with categorical data). So, if have columns country_0, country_1,..., country_100 and all the other categorical columns and then perform MCA, this would be a viable solution to this problem? And if I want to combine it with the continuous data, should I eg. perform PCA separately on the continuous data, and then take results both from MCA and PCA? (I know that FAMD exists, but I currently cannot seem to run the only implementation I've found). $\endgroup$ – Valeria Jan 12 at 3:46
  • $\begingroup$ What is the problem with having 100 one-hot encodings? $\endgroup$ – Tim♦ Jan 12 at 7:49
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    $\begingroup$ "Lack of correlation" is not a valid criteria for excluding variables. $\endgroup$ – Tim♦ Jan 12 at 8:33
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In the case of countries I would recommend using predefined groups( at least in the beginning). geographic groups(continent, region etc.) economic groups(currency, trade, income etc.) cultural/political groups(religion, state, war etc.)

I would prefer a solution using this kind of variables because interpreting results will be a lot easier if results are based on groups that are already studied.

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  • $\begingroup$ In the dataset there is total GDP & GDP per capita & population. Do you think that it would make sense to perform clustering on those, and from these create several groups of countries for these dataset? (As it's my exploration and not real-life project, I'm just curious to do as much as possible with the dataset itself, without bringing additional sources.) $\endgroup$ – Valeria Jan 12 at 4:21

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