Dealing with Imbalanced Data in Machine Learning - KDnuggets

As an ML engineer or data scientist, sometimes you inevitably find yourself in a situation where you have hundreds of records for one class label and thousands of records for another class label. Upon training your model you obtain an accuracy above 90%. You then realize that the model is predicting everything as if it's in the class with the majority of records. Excellent examples of this are fraud detection problems and churn prediction problems, where the majority of the records are in the negative class. What do you do in such a scenario?