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I have the following dataset:

Name of assignee:Alex
Time to start work on task: 10:00 17-01-2019
Time to finish work on task: 12:00 17-01-2019
Assessment results: A
Type: Article
URL: http://...
Action: to read page 5 to 15
Status: not done.
Timestamp of status "in process" change: 1547724627
Timestamp of status "done" change: 1547724938

Name of assignee:Alex
Time to start work on task: 15:00 17-01-2019
Time to finish work on task: 16:00 17-01-2019
Assessment results: A
Type: Book
URL: http://...
Action: to read page 17 to 43
Status: in process.
Timestamp of status "in process" change: 1547724627
Timestamp of status "done" change: 1547724938

Name of assignee:Alex
Time to start work on task: 13:00 17-01-2019
Time to finish work on task: 14:00 17-01-2019
Assessment results: B+
Type: Video
URL: http://youtube.com/..
Action: watch from start to 27.30 minutes.
Status: done.
Timestamp of status "in process" change: 1547724627
Timestamp of status "done" change: 1547724938

etc.

I'd like to predict how likely the specific user will or will not fit in the time pre-set by teacher. Analyze possible holes and reschedule tasks. Can you suggest which type of Neural network or any other classification algorithm is suitable for making prediction like that to make personified timetable? Tasks are usually to study online resources and non-location-dependent. Which additional parameters may improve the accuracy?

Thanks!

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