I have a survey dataset collected from several countries. The questions are mostly on a likert type 5-point scale, and the rest consists of yes/no questions. I have no hypothesis to test, but I believe that some countries differ from each other. Previous studies usually present descriptives of the surveys and try to explain the results, but I think machine learning can be applied here:
Responses to survey questions of a single person can be considered as a data point, and that person's country will be the class of the data point. Then, I can apply a machine learning classifier (such as a Random Forest) to:
- All countries
- One country vs others (US vs others; Japan vs others; etc.)
- One country vs another country (US vs Japan; etc.)
And I can interpret the results based on which features (feature = question) had the most/least predictive power. For example,
"We found that this question/feature was most powerful in distinguishing US from Japan. When we removed this question, the prediction accuracy dropped from 90% to 70%."
I have no intention of predicting the country, and I am just trying to understand the data. Is it a good idea to apply machine learning here?
Edit: The number of data points is about 10 000. That is, 10 000 people from 12 countries filled the questionnaire. The number of classes for the classification task is the number of countries, which is 12.