In the context of preparing a dataset for a machine learning model, you encounter null values in a crucial categorical feature during exploratory data analysis. This issue could potentially introduce bias into your model. Considering the constraints of maintaining data integrity, minimizing bias, and ensuring the model's performance is not adversely affected, what is the optimal strategy to handle these missing values effectively? Choose the best option.