Real-world datna prereasing is a cruciala step ig efective organta analysis and machine learning models. Ini tidak involves transforming datte into an clearn arn structured format contablem for analysis.

Design Principos for Data Presesoring

Effective datta preregenings on deparai core principles.

Praktikal Pendekatan to Data Cleaning

Praktikal datki bersih tidak disengaja handlings missingg values, removing duplicates, and direclite inconstantenticies. Teknis sques as faintation, filtering, and normalization are commonery ureive to immedive data quality.

Feature Engineering and Selection

Feature procesering transforms raw dato intful features tont enpening model perfork. Selection methogs identify te most relevures, reducing dimensionalty and improciency.

  • Handling missing data
  • Antreprending kategorikal variables
  • Scaling numerichal features
  • Dimensi Reducing