For projects involving customer data analysis, how do you balance data transformation steps (like aggregation, enrichment and deduplication) to maintain both data quality and model performance, especially when using tools like Alteryx before feeding the data into machine learning models?
Projects PX
Dash
3 days ago
Focus on minimal, impactful transformations to preserve data integrity while optimizing for model performance:
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Aggregation: Keep it meaningful—aggregate only when it reduces noise without losing key patterns.
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Enrichment: Add only relevant external data (e.g., demographics) that directly improves predictive power.
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Deduplication: Critical for accuracy—remove exact duplicates, but validate fuzzy matches to avoid over-cleaning.
Tools like Alteryx: Use its profiling tools to track how each step affects distributions/outcomes. Test model performance on raw vs. transformed data to find the right balance.
Key: Transform just enough to improve quality without distorting the underlying trends your model needs.
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