Detecting redundancy with Pearson correlation in continuous features
If the correlation between two features $x_1$ and $x_2$ is 1 that means that you can write $x_1 = c\cdot x_2 + a$. The only knew knowledge there is are those two constants, the individual values can be retrieved knowing this. I highly doubt there is anything a machine learning algorithm can learn from this and it is a fact that for some having this kind of correlation between features can hurt your performance quite a bit, so I would test it a bit but I would say it's very likely you can remove one of the two, and which one is not going to matter.