Different results for LogisticRegression on python 2.7 and 3 I have different results for the same kernel on python 2.7 (local machine) and python3 (the system running on kaggle) for LogisticRegression. How it is possible?

Here my results from my local machine:


Here from the kaggle notebook:


The amount of data is different, because I split a bit more for the train data, but the predictions are totally different.  Can this be because of the versions of python?
Assuming that you are using sklearn, if you go to the source in the github 
you will see that they do not differ. 

But you have variability elsewhere. For example train_test_split of sklearn also takes random seed, so it could be that your data is fundamentally different between these approaches (you could do cross valdiation for example to include all the data in train and test)