A data science team at your organization is tasked with creating a machine learning model to forecast the sale prices of houses using characteristics such as the home's square footage. However, approximately 10% of the entries in the modest-sized training dataset are missing the square footage attribute. Given the importance of model accuracy in your application, which approach should the team employ to handle missing values in the training data effectively?