Apply data transformations before splitting the dataset and employ cross-validation to ensure that transformations are consistently applied across training and test sets, thereby minimizing discrepancies.

Normalize the training and test datasets independently to maintain the integrity of each dataset's distribution, potentially improving model generalization.

Increase the size of the test set to ensure it is more representative of the overall data distribution, aiming for a more reliable evaluation of the model's performance.

Implement a time-based split for the training and test data instead of a random split to prevent data leakage and ensure the model is evaluated on future, unseen data, mimicking real-world deployment scenarios.

Combine options A and D, applying data transformations before splitting and using a time-based split to both prevent data leakage and ensure uniform transformation application.