Test set larger than train set
Do I correctly understand, that the test data is the whole dataset, whereas training is only a subset of it? Training and test data must not overlap. The test is a measure of quality on unseen, unfamiliar data.
In the case of inbalanced data and two class classification the naive classifier, predicting always the most probable class has the quality 891 / 1121. Any sensbible model should beat this score.
To handle inbalanced data you can use several approaches - undersampling the majority class, oversampling the minority https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/. Also many classifiers have the attribute weights, which can be set to balanced - this would penalize larger for mistakes on minority class.
In the case of imbalanced data measure not only accuracy, but precision and recall as well https://en.wikipedia.org/wiki/Precision_and_recall