Explanation

Answer Example

To handle an imbalanced dataset, I would first explore the distribution of the target variable and perform resampling techniques like oversampling the minority class using SMOTE or undersampling the majority class. I would also consider using algorithms that handle imbalance well, such as XGBoost with class weights. Additionally, I would focus on evaluation metrics like precision, recall, and F1-score rather than accuracy. For example, when working on a fraud detection model, I used SMOTE to balance the dataset and optimized the model based on recall to minimize false negatives.