Interpreting random seed effect on correlation in regression with small sample I am working on a project with a sample size of 30. I have 7 features predicting a continuous variable where I am aiming to optimize the r-value. If I change the random seed of my train_test_split() 100 times, I get correlation coefficients that range between .6 and .9 with an occasional negative r-value. 

How should I interpret this? Is this variability normal for such a small data set and a random shuffling of my training data? Or is something else going on? How should I decided which seed to use?
This means that your model is not consistent. Your problem is hard because of the small sample size. I would recommend reducing the number of predictor variables.

7 predictors is quite a few for linear regression, even with a large sample size. Try and get the best results you can with 2 or 3 predictors.

If you can predict with 30% accurately all of the time it is better than 70% accuracy 10% of the time.