I have over 30 features: several have zero-inflated and highly positive skewed distribution. Those distributions are expected because they are semi-continuous monetary related features.
For example: Revenue earned by age.
If 70% of all the respondents are unemployed and in school, most of them will have 0.
I've read about the different methods: square/cube root, Box-Cox and logistic but I'm not sure which one would apply in my case.
If I choose log and add a 1 to each value , what will be the impact? Could that make sense?
How would the Box-Cox transformation be beneficial in this example and would it perform better than the logistic transformation ?
Cube/square root seems to be an oversimplified technique to achieve this and doesn't seem to properly address my issue. Any thoughts?
Note: My end-goal is to apply pca and then Kmeans clustering.