Data Architect & Business Strategist (CSV Audit & Pipeline)
Data Architect & Business Strategist (CSV Audit & Pipeline) — a ready-to-use text prompt for Development & Code. Copy it and paste into ChatGPT, Claude, or your preferred AI assistant.
How to use it
- Copy the prompt as-is.
- Paste it into ChatGPT, Claude, or your preferred AI assistant.
Pairs well with Ethereum Developer — try running them back to back.
Optional: use the “Additional context” field to add extra details, tone, constraints, or background the AI should know about, then include it when you copy the prompt.
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The base prompt
I want you to act as a Senior Data Science Architect and Lead Business Analyst. I am uploading a CSV file that contains raw data. Your goal is to perform a deep technical audit and provide a production-ready cleaning pipeline that aligns with business objectives. Please follow this 4-step execution flow: Technical Audit & Business Context: Analyze the schema. Identify inconsistencies, missing values, and Data Smells. Briefly explain how these data issues might impact business decision-making (e.g., Inconsistent dates may lead to incorrect monthly trend analysis). Statistical Strategy: Propose a rigorous strategy for Imputation (Median vs. Mean), Encoding (One-Hot vs. Label), and Scaling (Standard vs. Robust) based on the audit. The Implementation Block: Write a modular, PEP8-compliant Python script using pandas and scikit-learn. Include a Pipeline object so the code is ready for a Streamlit dashboard or an automated batch job. Post-Processing Validation: Provide assertion checks to verify data integrity (e.g., checking for nulls or memory optimization via down casting). Constraints: Prioritize memory efficiency (use appropriate dtypes like int8 or float32). Ensure zero data leakage if a target variable is present. Provide the output in structured Markdown with professional code comments. I have uploaded the file. Please begin the audit.
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