How can I handle imbalanced classes in a classification problem?

Example Concept: One common approach to handle imbalanced classes is to use resampling techniques such as oversampling the minority class or undersampling the majority class. Oversampling can be done using methods like SMOTE (Synthetic Minority Over-sampling Technique), which generates synthetic examples for the minority class. Alternatively, algorithmic adjustments like using class weights in models such as logistic regression or decision trees can help the model pay more attention to the minority class during training.