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from sklearn.utils import class_weight

# Calculate class weights to handle imbalance
class_weights = class_weight.compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)
class_weights_dict = dict(enumerate(class_weights))

# Train the model with class weights
model.fit(X_train, y_train, epochs=15, batch_size=32, validation_data=(X_val, y_val), class_weight=class_weights_dict)