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Analysis

Find The Top Drivers Of X

Feed in a target variable, its type, a list of candidate predictor columns, and the dataset's row and column counts, and get back a rigorous drivers-of-X analysis instead of a hand-wavy guess. The prompt runs univariate correlation, mutual information, tree-based feature importance, and permutation importance side by side, explains why the four rankings disagree, flags features that are likely proxies for the same underlying driver, and closes with a plain-language summary naming the top three drivers and what they mean for the business.

Prompt
I want to understand what actually drives {target_variable} in my dataset.

Target variable: {target_details}
Potential predictors: {predictor_columns}
Dataset size: {dataset_size}

Assess feature importance with several approaches:
1. Univariate analysis: each feature's correlation or association with the target
2. Mutual information: non-linear relationships between features and target
3. Tree-based importance: feature importances from random forest or gradient boosting
4. Permutation importance: which features matter most for prediction accuracy

For each approach:
- Rank features from most to least important
- Explain why the rankings won't fully agree across methods
- Point out features that stay important across all methods
- Flag features that may just be proxies for the same real driver

Wrap up with: "The top 3 drivers of {target_variable} are X, Y, Z, and here's what that means for the business."
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