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Analysis

Run Exploratory Data Analysis

Paste in column names, types, and roughly 20 sample rows along with the role you're framing findings for — a product manager, a CFO — and this ready-made EDA prompt returns summary statistics for every numeric column, value counts for categorical columns, a missing-value breakdown, distribution shapes for key variables, the five most interesting anomalies, and ten business-relevant questions the data could answer, ranked by value. A solid default first pass on any dataset you haven't explored yet.

Prompt
Run an exploratory data analysis on this dataset:

{dataset_sample}

Deliver:
1. Summary statistics for every numeric column (mean, median, std, min, max, quartiles, skewness)
2. Value counts for each categorical column (top 10 values plus an "other" bucket)
3. A missing-value summary (count and percentage per column)
4. Distribution assessment for the key numeric columns (normal? skewed? bimodal?)
5. The 5 most interesting patterns or anomalies you spot
6. 10 questions this data could answer, ranked by business value

Emphasize findings that would surprise or worry a {business_role}.
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Build A Regression Prediction Model

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Build A Time-Series Forecast

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