Logistic regression estimates the probability of a binary outcome from weighted features. Despite its name, it is a classifier. In these fictional membership records, class 1 means departure and class 0 means renewal. The predict method returns labels, while predict_proba returns a column for each class in the estimator's classes_ order.
A label is the result of a decision rule applied to a probability. Keeping those two outputs separate lets you change the rule later without refitting the coefficients. Train only on training rows, then evaluate on the holdout. Logistic regression also needs enough iterations to converge; the tasks provide an explicit limit. A high score from fitting on test labels is still leakage, and a majority dummy estimator does not satisfy a task asking you to learn from the inputs.