I'm confused on how to go about an issue.
I'm trying to implement an unsupervised model, using a dataset that is essentially a log file. This dataset contains a variety of features, but most importantly, it contains a column that identifies the individual that "created" the log entry. It also contains features such as a timestamp, and more regarding that specific event.
I want to be able to detect if a sample is an anomaly for that specific individual. So while the dataset might contain any number of individuals, if
person A does something, I want to compare that to
person A:s previous events, and not against the entire dataset of samples.
The answer might be obvious, but I'm just stumped.
While I could simply just create a model for each person, that feels kinda dumb. However, that might be the simplest option, by training a separate model for each person and then just loading the appropriate model when a sample occurs.
I don't know, I'm stuck. Please help.