SVM SVC: Metric for parameter optimization on imbalanced data
Using resampling methods to fix the imbalanced databases a good method. This can be completed by oversampling, downsampling or creating synthetic instances.

In this kind of problem, precision is not necessarily a good evaluation metric. Instead evaluating True Positives, True Negatives, False Positives and False Negatives is better way to evaluate this.

For this evaluation following techniques can be utilized from Sklearn library.


Recall score
Accuracy score 
F1 score
AUC Score


In Sklearn the method caled "classification_report" gives a summary of those evaluations (except AUC Score from the list). That can be a good evaluation metric to check the performance of the model.