How can you prevent data leakage during model development?

Example Concept: Data leakage occurs when information from outside the training dataset is used to create the model, leading to overly optimistic performance metrics. To prevent this, ensure that any preprocessing steps, such as scaling or feature selection, are applied only to the training data and then consistently applied to the validation and test datasets. This can be managed by using pipelines in libraries like sklearn, which encapsulate the entire modeling process and ensure that transformations are applied correctly and consistently across different data splits.