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20256 posts
October
September
- Stages IV and V: anomalies and housekeepingOutlier detection during EDA, then the end of the pipeline: PCA-reduced subsets, end reports and what the user sees next.
- Stage II: missing dataDiagnosing MCAR, MAR and MNAR missingness per data field, a decision tree for it, and the agents that pick a handling strategy.
- Stage I: defining and understanding the datasetWhat makes data unclean, why LLMs label data types inconsistently, and swapping prompts for sentence-transformers and cosine similarity.
- Gaby's data-cleaning pipelineThe objective, user inputs and five general stages of Gaby's self-orchestrated data-cleaning workflow.
August