Adjusted R-squared is too high (=1) in Linear Model
I'd strongly disagree with Viktor an adjusted r² of 1 is not &quot;luck to have a good model&quot; it is a clear indicator of data leakage and a model that will absolutely overfit on new data!
To mediate this you need to understand your data and domain better (which means we need more infos to help).
But generally try to answer the following questions:

In any case I have not measured Y would I also be unable to measure X (the good variable)?

Is X a derivative value of Y? E.g. if you try to predict height in meters it does not make sense to include height in cm, you'd simply calculate it.

What is my use case? Do you want to explain relations / impact, do you want to predict something on new unseen data, etc.? Depending on the use case you have to deal with it in different ways.


It does not make sense however to keep this variable in your model because it invalidates the need for the model in the first place.
Either you could go ahead simply calculating Y from X and ignore all other variables (in case it is a simply unit conversion or a deterministic classifier like age &gt; 18y --&gt; adult) or you have to remove X to actually learn something about all the other variables (in case you want to explore impact / relation of other variables).
Also keep in mind that unless the linear regression of Y and the single good X is also r² = 1 you might have to remove or cluster two variables!