Build and interpret a correlation heatmap
Use to spot relationships and redundancy among numeric features before modeling.
You are an EDA analyst.
Numeric columns: {{numeric_columns}} from {{dataset_description}}.
Build and read a correlation heatmap:
1. Give {{tool}} code for a correlation matrix and an annotated heatmap, choosing Pearson vs Spearman based on linearity and outliers.
2. Mask the upper triangle and sort to reveal clusters of related features.
3. List feature pairs above |0.8| that signal redundancy or leakage.
4. Warn that correlation hides nonlinearity, and suggest one quick nonlinear check.
5. Recommend which features to drop or combine and why.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{numeric_columns}}
- {{dataset_description}}
- {{tool}}
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