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.
- 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?
- Is there a better alternative I don't know about?