Hello friend,
It looks like your data is categorical, as you have multiple distinct event "categories." Predict thinks your problem is continuous (all events live on a spectrum), thus producing "2.5".
For categorical data, you probably want to use the Classify function.
But it might help to think more about the problem. The results will likely not be realistic since all it has as input is the event's time, and it is trying to prevent the event's occurrence. This is a very tricky problem with very little given information about the days.
You might have more success treating it as a SequencePredict problem, where you have a binary sequence for each event category on each day (true or false). This would likely be better than Classify, but it is really just forming some random transition probability based on past events (a Markov model).
You could also try SequencePredict with a list of events that occurred each day, but that could cause weird results depending on how the Markov model is configured and how much data you have of combinations of different events.
Ultimately, there is not much machine learning you can do with only this data. It might help to look for additional, domain-related information about those days that the events occurred on. I am not sure what domain your data is from, but maybe some easy-to-get data like the weather for those dates could be used to augment your current dataset.
Best of luck!
