Your industrial company has developed a machine learning model to predict the optimal purchase price for goods, taking into account various factors such as quantity, quality, and product-specific variables. The current model is a linear regression that performs adequately but you are tasked with improving its efficiency and accuracy. The company operates under strict budget constraints and requires the solution to be scalable across different product lines. Additionally, the solution must comply with data privacy regulations. Considering these constraints, which of the following techniques would be most effective for optimizing the model's performance? (Choose two correct options)