I have a fairly large (>100K rows) dataset with multiple (daily) measurements per individual, for a few thousand individuals. The number of measurements per individual vary, and there are many null values (that is, one row may have missing values for certain variables/measurements, but not for all). I also have a daily outcome (extrapolated, but let's assume it's fair to do so, so there is a binary outcome for each day when measurements are taken).
My question goal is to model the outcome, such that I can predict daily outcomes for new individuals.
My background is in research, and I am familiar with some statistics and ML, and overall still fairly new to data science. I am wondering if there are any particular known ML algorithms that can be used to model such data. I am cautious about using logistic regression from something like python's scikit learn because the observations are not independent (they are highly correlated on an individual level). From my knowledge, these kind of data are well-suited for a mixed effects logistic regression or longitudinal logistic regression. However, I haven't been able to find any widely used ML algorithms for it, and I would like to pursue an ML approach rather than fitting a statistical model using something like lme4 package in R.
Could someone recommend an available ML algorithm to model such data?
PS: I did some research and found a few research articles on the topic but nothing widely used or clearly implemented. The structure of the data I am working with strikes me as very common, so I thought I'd ask.