Transform variables with zero-inflated values and positive skewness 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.

Whatever you do, if there is a spike at zero, there will be a spike when you transform it, regardless of whether the transform maps zero to zero or not.
A transformation might help to pull in the tail of high values, but none of the methods you mention as a goal require a transformation absolutely.
Square roots, cube roots, log1p meaning $\log (x + 1)$, and asinh can all cope with zeros.
A transformation may help in visualization even if it is a little arbitrary. The same doesn't necessarily apply to modelling.
Details:
I've never seen any transform based on (variable $+ 1$) called logistic. (One reference to "logistic" was fixed as a typo, but another remains.)
Box-Cox(*) in my experience is defined and implemented only for entirely positive variables.
(*) Initial capitals please.
