Handling imbalanced datasets can be done in several ways:
Resampling:
a. Upsampling the minority class to match the number of samples in the majority class.
b. Downsampling the majority class to match the number of samples in the minority class.
Ensemble methods:
Using techniques like random under-sampling or over-sampling with different models to combine their results.
Cost-sensitive learning:
Modifying the loss function to give more weight to samples from the minority class.
Generative Adversarial Networks (GANs):
Synthesizing new samples of the minority class.
Anomaly detection techniques:
Treating the minority class as anomalies.