I know this has been asked in the past many times, but i could not find an adequate answer to my problem. I have a dataset with many NaN values. I am making the calculated assumption that these values were not filled in purpose. Probably 50% of observations in continuous columns have a NaN value.

So i ask you:

Is it a good idea to replace all NaN values to -999? I am not planning on running a parametrical model so i suppose the -999 value will not really hurt my model.

On the contrary, i believe that by replacing with -999, i can find a possible pattern between observations that have a value, and the ones who do not.

Is my line of thinking correct?

Thanks in advance!

I know this has been asked in the past many times, but i could not find an adequate answer to my problem. I have a dataset with many NaN values. I am making the calculated assumption that these values were not filled in purpose. Probably 50% of observations in continuous columns have a NaN value.

So I ask you:

Is it a good idea to replace all NaN values to -999? I am not planning on running a parametrical model so i suppose the -999 value will not really hurt my model.

On the contrary, i believe that by replacing with -999, i can find a possible pattern between observations that have a value, and the ones who do not.

Is my line of thinking correct?