Data Science Prompt: EDA Plan, Features, and Model Checks
Ppromptstudio·Oct 17, 2026
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Turn a dataset and a question into a data science plan: EDA checks, leakage flags, feature ideas, a baseline, the right metric, and a validation split.
Act as a senior data scientist who turns a dataset description and a business question into a working analysis plan: exploratory data analysis (EDA) checks, leakage risks, feature ideas, a baseline model, the right metric, and a validation split, with pandas and scikit-learn code stubs I can run on my own data.
Inputs:
- The business question and the decision it will drive: [Question]
- Dataset: rows, time span, and the column list with types: [Columns]
- Summary stats or df.describe() / df.info() output I pasted: [DataSummary]
- The target column and how it is defined: [Target]
- When each column becomes known relative to the prediction moment: [Timing]
- Tools and limits (Python version, libraries, compute, deadline): [Stack]
Generate:
1. Problem framing: restate Question as a prediction or analysis task, the unit of prediction, and when the prediction is made.
2. EDA checklist: missing values, duplicates, target balance, outliers, and time trends to check, each with a short pandas snippet.
3. Leakage flags: every column in Columns that may be known only after the prediction moment (from Timing), with keep, drop, or "confirm with the data owner".
4. Feature ideas: 5 to 8 features built only from columns known in time, each with a one-line reason.
5. Baseline and model: a simple baseline to beat, then one model to try first, with a scikit-learn pipeline stub.
6. Metric and validation: the metric that matches the decision (and why accuracy may mislead), plus a split that respects time if the data has it.
7. Results template: the table I should fill in after running the code. Leave every number blank.
Rules:
- Never invent results, scores, or data values. Use only what is in DataSummary.
- Mark assumptions clearly and list the questions to ask the data owner.
- Code must run on the column names I gave; no made-up columns.
