i have a dataset of work period from a lot of people during multiple years and i would like to find the overlapping date between these periods, the data would look something like this:
Name;Start;End;Valid John Doe;2015-01-01;2017-01-01;Yes Jonh Doe;2016-02-03;2016-06-04;No Jane Doe;1980-01-01;2018-01-01;Yes
As you can see i can have multiple lines for the same person and are mapped as valid or not, there are a few more rules like if the person is a professor he can have overlapping work periods but the core is to find these wrong periods created by human error.
Since i am a beginner in machine learning i would to try to implement this as a learning project although i intend to use and expand in the future if all goes well, at least to my eyes seems like a somewhat easy task to do.
I would like your opinion in what kind of algorithm i should use, or what framework i should or not use, or even if this is not very cost effective to implement on ML.
I thought of somekind of supervised learning using tensorflow, maybe is too much?
Also any tips, tricks or tutorials would be much appreciate it.
EDIT: More Information At first i am providing the answer, which would be the "Yes" or "No" on Valid, but when i provide something like:
Row;ID;Name;Start;End; 1;1;John Doe;2015-01-01;2017-01-01; 2;1;Jonh Doe;2016-02-03;2016-06-04; 3;1;Jonh Doe;2017-06-02;2018-04-01; 4;1;Jonh Doe;1990-01-01;2017-07-01;
So i would like it to point out, hey, John Doe has 2 invalid periods, row 2 and 4, why should it give me that? because 2016-02-03 to 2016-06-04 is already covered by row 1 and row 4 overlap with row 1,2,3
At first i thought on making this with regular programming to check for everything, but there are some cases where human error make this quite a difficult task to do, thats why ml got in to my mind, the algorithm does not need to have really high accuracy, this is mostly a learning project.