EVNow
Well-Known Member
I guess I'm using common industry terms.We're saying the same thing.
Basically, data prep is a big part of any data science venture. You acquire the data, cleanse it, transform it so that it becomes useable. Then you apply whatever models you have come up with (using clean data).
You can call cleansing & transformation part of the "algorithm" if you want. But the current formula they are using definitely calls for clean data - otherwise you would need a lot more parameters and a complex logic.