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Analysis

Analyze Dataset Correlations And Risks

Goes past a raw correlation matrix to flag the relationships in a dataset that actually matter before modeling starts. - Inputs: key variables of interest plus dataset columns, types, and row count - Analysis: correlation matrix, non-obvious pairings, multicollinearity risk, categorical-to-numeric associations, and time-lagged correlations - Warns: spurious correlations that look causal but aren't - Output: plain-language findings plus runnable Python code

Prompt
Analyze the correlations present in my dataset:

Key variables I care about: {key_variables}

Dataset info: {dataset_info}

Deliver:
1. A correlation matrix for all numeric variables (highlight strong correlations above 0.5)
2. Non-obvious correlations worth investigating for causation
3. Variables that are strongly correlated with each other (multicollinearity risk for modeling)
4. Associations between categorical variables and numeric outcomes (using appropriate methods)
5. Time-based correlations where dates exist (lagged correlations, seasonal patterns)
6. Spurious correlations to watch out for (correlation-isn't-causation flags)

Include the Python code and explain each finding in plain terms.
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