Yoda Response: Given a dataset with missing values and outliers, careful preprocessing is required. First, I would identify and handle the outliers. Use methods like Z-score or IQR to detect them, and either remove or transform them to reduce their impact. For missing values, I would analyze the pattern of missingness. If appropriate, fill missing values with the mean, median, or mode, depending on the data type and distribution. Alternatively, use techniques like forward or backward filling for time-series data. Ensuring the dataset is clean and consistent is crucial before applying a machine learning model.