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I'm having trouble finding a good place to begin with this. I'm just looking for a name or point to start researching a
Let's say I have 1000 records. 10 of these records are only 90% complete. The other 990 are 100% complete.
What ML algorithm (or type of algorithm) would be able to predict values for the "holes" in the data?
The data is no sequential but rather transactional, so the positioning of a record in relation to other records is inconsequential.
In stats I think they'd say "imputation". CS theorists might model this as "matrix completion" (if you make it a matrix), "collaborative filtering" (like in the Netflix challenge). Maybe others know of more keywords.
Your question is underspecified. If you just want to fill the gaps, put some fixed arbitrary value there.
To make the question interesting you have to specific some condition for preferring one way of filling the gaps vs. another one.
Essentially what metric are you trying to optimize? E.g. are you assuming that your data is a sample coming from some distribution and trying to fill the gap to maximize the likelihood of the the resulting sample? ...
Once you turn the question into an optimization question you can ask for algorithms, you would probably have more luck on stats.stackexchange.com than here.
