Data Preparation / data-cleaning

Data cleaning

Missing-value strategies, outlier handling, clipping, denoising, scaling, log transforms, and rare categories.

shellbackend needed later
Focus
  • imputation
  • outliers
  • normalization
  • rare category handling
Future feature idea

Missing-value and outlier strategy lab with before/after metric changes.

Self-check
  1. What is the core job of "Data cleaning"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.