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Spark ML feature pipelines: VectorAssembler, StringIndexer, OneHotEncoder, and the Pipeline API
Machine learning models rarely consume raw tables directly. Before training, data usually needs cleaning, categorical encoding, feature assembly, and a consistent transformation path for future predic