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Data Preparation for Machine Learning: Best Practices

Master data cleaning, feature engineering, and preprocessing techniques for better ML models.

Rottawhite Team14 min readNovember 26, 2024
Data PreparationFeature EngineeringData Quality

The Foundation of ML

Data preparation typically takes 60-80% of ML project time. Quality preparation directly impacts model performance.

Data Quality Issues

Missing Values

  • Types: MCAR, MAR, MNAR
  • Detection methods
  • Handling strategies

    • Outliers

  • Statistical detection
  • Domain-based rules
  • Handling approaches

    • Inconsistencies

  • Format variations
  • Duplicate detection
  • Data conflicts

    • Cleaning Techniques

    Missing Data

  • Deletion (listwise, pairwise)
  • Imputation (mean, median, mode)
  • Advanced (KNN, model-based)

    • Outlier Handling

  • Removal
  • Capping
  • Transformation
  • Robust methods

    • Normalization

  • Min-max scaling
  • Standardization
  • Log transformation

    • Feature Engineering

    Numerical Features

  • Binning
  • Polynomial features
  • Interactions

    • Categorical Features

  • One-hot encoding
  • Target encoding
  • Embeddings

    • Text Features

  • Tokenization
  • TF-IDF
  • Embeddings

    • Date/Time

  • Components extraction
  • Cyclical encoding
  • Lag features

    • Data Splitting

  • Train/validation/test
  • Cross-validation
  • Time-based splits
  • Stratification

    • Tools

  • Pandas
  • scikit-learn
  • Feature-engine
  • Great Expectations

    • Conclusion

    Thorough data preparation is essential for building effective ML models.

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