How to find the right regression model for my project
This answer is based on additional information regarding the data provided in a comment to the question:


  [...] So it's about delivery The
  first and second are Id corresponding to cities, the 3rd is a date,
  and the last is an id corresponding to the truck that is changing (i
  sorted it that's why its the same in the first 3 columns). And the
  target Y is delay.


If you just add the time column in a unformated way to the regression, the method(s) will not be able to digest the information in a good way. Think about your problem. Delivery may be delayed because of trafic conditions (time, or the day of the week). I suggest that you encode time e.g. in hour of day, day of week, week of year (or so) and add all these (possibly one-hot encoded) variables/features to your model (together with the remaining features). You can use regularization (e.g. lasso) to "shrink" unnecessary features. This should give you an okay fit. 

Essentially you need to really make sure you get as much information as possible out of the "date" column. In Python you can encode dates by predefined functions, e.g.

import datetime
Todays date datetime.datetime.today()
Day of week datetime.datetime.today().weekday()
Week of year datetime.datetime.today().isocalendar()[1]


Find an example of how to apply lasso here.