Data Preparation

Data preparation is where most of the quiet work happens. Raw records are cleaned, joined, filtered, aggregated, and reshaped into features that match the question.

Good preparation is disciplined. It preserves the meaning of the original data, records assumptions, and avoids using information that would not be known at the moment of prediction.

The useful output is a reproducible dataset with a clear target, clear features, and enough checks that the team can trust what happens next.

• Cleaning away inconvenient cases that the model will still face later.
• Leaking future information into the training data.
• Creating a dataset nobody can reproduce.