Data splitting for a binary classification model
My 2 cents:
the number of records in the data set used here is very small. If we have a look into the data set we can see that the target variable split is exactly 50:50 which means the probability is half. Its like flipping a coin to get heads or tail.

The training set contains a known output and the model learns on this data in order to be generalized to other data later on. The dependent variables and the independent variable should be in splatted and  then do a train test fit.

You can use the library from scikit learn as well
from sklearn.model_selection import train_test_split