In handling an imbalanced dataset, I'd explore techniques such as resampling either by over-sampling the minority class or under-sampling the majority class. Additionally, I'd consider assigning weights to classes and focus on evaluation metrics like precision, recall, and F1-score to ensure a comprehensive assessment of model performance in real-world scenarios.