I would like to predict a LABEL: A,B or C using a classification machine learning model.
My data to train the model is like:
LABEL AGE12-18 AGE19-24 AGE25-35 A 10 30 60 B 40 20 40 C 5 5 90
Where AGE-12-18, AGE19-24 and AGE25-35 are the percentage of users with age between [12-19),[19-25) and [25-35) in each cluster. Then
So, I have aggregations of A,B,C instead of all the data.
I would like to transform this data to predict users with data like:
USER AGE AGECAT a 24 AGE19-25 b 32 AGE25-35
I was thinking to create a new dataset with a distribution with the same % of users in each cluster as:
LABEL AGECAT A AGE12-18 X 10 rows A AGE19-24 x 30 rows A AGE25-35 x 60 rows
However, I don't like really much this solution as I am not sure If it is going to work. I have seen another similar question with aggregated dependent variable but not with the independent variables.
Do anybody knows if this is correct of any other way to achieve a classification model with this data? Thank you