from autogluon.tabular import TabularPredictor, TabularDataset

# Load the dataset
train_df = TabularDataset('train.csv')

# Handle imbalanced datasets by specifying custom parameters
# AutoGluon can handle this internally but specifying here for clarity
hyperparameters = {
    'RF': {'n_estimators': 100, 'class_weight': 'balanced'},
    'GBM': {'num_boost_round': 200, 'scale_pos_weight': 2},
}

# Train the model with settings for handling imbalance
predictor = TabularPredictor(
    label='Target',
    eval_metric='accuracy',
    verbosity=2
).fit(
    train_data=train_df,
    hyperparameters=hyperparameters
)