Regression (Linear & Ridge)
- Tax Data -

• Before Data Transformation Snapshot

• Tabular data listing tax attributes by State, Group, Item, and Value.

• Includes mixed formats (%, $, numbers) requiring cleaning before analysis.

• Each row represents one tax metric for a state-category (e.g., Colorado's Individual Taxes).

• After Data Transformation Snapshot

• Shows standardized features (X_scaled) used in regression: values centered around 0, range -2 to 2.

• Target (y) is State and Local Tax Burden; only UT - Individual Taxes stands out as significantly higher.

• Results

• Both Linear and Ridge Regression achieved R² = 1.0, indicating perfect prediction.

• Ridge selected alpha = 0.01, showing regularization was minimal.

• Predicted values closely matched actual tax burdens across all state-tax groups.

✅ Conclusion: Regression models were highly effective for predicting Tax Burden using tax indicators.