I have a dataset with more than 100,000 observations (rows) and 24 variables in which 23 are continuous and one is categorical variable.
The categorical variable has 13 categories (1, 2, 3, ..., 13) and one more category (0) which is outlier.
I have to build a model which will predict outliers (category with 0) with the highest accuracy.
Should I apply k-mode clustering? Or which algorithm will be most suitable?
I was thinking of combining all other categories except 0. Because 0 are outliers and ultimaate objective is to find those outliers. Is this good approach?