How to handle missing data for machine learning
There are three main approaches to handling missing data.


Impute - use some method to fill in the missing values with reasonable guesses. You could interpolate between two time points, take the average value over all time points, or use a variety of other techniques leveraging co-occurrence of other variables to get a reasonable estimate.
Ignore - some methods can just ignore missing data, and not use it in the model at all
Utilize - for cases where data is not missing-at-random, missingness itself can be an informative feature. You could include missing values as another data point to model your output.