Data Preparation & Preprocessing
- Data Cleaning: Resolve missing, duplicate, and inconsistent data; correct outliers.
- Data Transformation: Normalize, scale, and structure data for ML compatibility.
- Feature Engineering: Develop and refine features to improve predictive accuracy.
- Data Annotation & Labeling: Prepare labeled data for supervised learning and specialized tasks.
- Dimensionality Reduction: Simplify datasets to enhance efficiency and reduce costs.
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