University · Computer Science · Data Science and Big Data Technologies
Data Preprocessing: Cleaning, Transformation, and Feature Engineering
4 Abschnitte1 Karteikarten-Decks1 Quizze
A comprehensive treatment of data quality dimensions, missing value mechanisms, outlier detection, imputation strategies, numerical and categorical transformations, feature engineering, and techniques for handling class imbalance.
Inhaltsübersicht
- Data Quality: Missing Values, Outliers, and Duplicates
- Cleaning Strategies: Imputation, Winsorization, and Deduplication
- Data Transformation: Normalization, Standardization, and Encoding
- Feature Engineering and Handling Class Imbalance
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