# Prediction approach on unique data or progressive data

In a employee attrition analysis with a table having rowwise data for a (employee like Id, name, Date_Join Date_Relieving Dept Role etc)

eID eName    Joining Releiving Dept Married Experience
123 John Doe 10Oct15 12Oct16   HR   No      12
234 Jen Doee 01jan16 -NA-      HR   No      11         (ie she is available)


I can run regression on this data to find the beta coefficients

eID eName    Joining Releiving Dept Married Experience
123 John Doe 10Oct15 12Oct16   HR   No      12
234 Jen Doee 01jan16 -NA-      HR   No      11


But I've seen other approach too.. where employee have multiple entries depending on their difference between joining date and current month or relieving month(say Employee A joined in Jan and Left in Dec so he'll have 12 entries updating corresponding columns like experience and marriage etc)

eID eName    Dept Married Experience
123 John Doe HR   No      0
123 John Doe HR   No      1
123 John Doe HR   Yes     2
123 John Doe HR   Yes     3


can someone tell what differentiate two approaches.. and what are the benefits of this second approach.

• IMO, First approach seems like used for predicting a new employee's experience or releiving date, given other info. Or even classification whether the new employee is married or not, given other data. But the second approach seems like predicting the current employee's future data whether he will remain married or will his experience improve. Nov 9 '16 at 9:04
• Thanks, @null_9 I'm trying to understand for same output, to check whether employee will stay or leave the company, Nov 9 '16 at 18:35