Data Normalization Tool

Purpose

Use this skill when the task is specifically about scaling or normalizing features before fitting a model.

When to Use

Use this skill when:

• Choosing between standardization, min-max, robust scaling, or log transforms
• Normalizing train/test data without contaminating evaluation
• Preparing numeric features for distance-based or gradient-based models

Not For / Boundaries

• Broader feature creation or encoding: use

  engineering-features-for-machine-learning

• Leakage review of the whole preprocessing chain: use

  ml-data-leakage-guard

• Final model evaluation: use

  evaluating-machine-learning-models

Typical Outputs

• Normalization strategy recommendations
• Train-only fit / test-only transform workflow
• Warnings about skew, outliers, and metric sensitivity

Related Skills

• engineering-features-for-machine-learning

  for the surrounding preprocessing plan

• ml-data-leakage-guard

  to verify the normalization order is safe