Update README.md

README.md

@@ -1,5 +1,4 @@
-## **Tabular Dataset README**
-```markdown
+
 ---
 dataset_info:
   features:
@@ -73,17 +72,26 @@ paperswithcode_id: null
 ### Summary Statistics (Original Data)
 | Statistic | US Size | Shoe Size (mm) | Actual Length (mm) |
 |-----------|---------|----------------|-------------------|
-| Mean | 9.5 | 275 | 279 |
-| Std Dev | 1.8 | 16 | 17 |
-| Min | 6 | 240 | 243 |
-| Max | 13 | 310 | 315 |
-
-### Correlation Matrix
-|  | US Size | Shoe Size (mm) | Actual Length |
-|--|---------|----------------|---------------|
-| US Size | 1.000 | 0.982 | 0.978 |
-| Shoe Size (mm) | 0.982 | 1.000 | 0.995 |
-| Actual Length | 0.978 | 0.995 | 1.000 |
+| count | 30.0 | 30.0 | 30.0 |
+| mean | 5.8 | 227.7 | 230.6 |
+| std | 0.7 | 5.8 | 6.2 |
+| min | 5.0 | 218.0 | 222.0 |
+| 25% | 5.0 | 223.0 | 225.0 |
+| 50% | 6.0 | 230.0 | 230.0 |
+| 75% | 6.0 | 230.0 | 235.0 |
+| max | 7.5 | 240.0 | 240.0 |
+
+### Visualizations
+![EDA Plots](eda_plots.png)
+
+### Key Findings
+- Strong positive correlation (>0.85) between all size measurements
+- Distribution centered around US size 6 (women's sizing)
+- No missing values in dataset
+- Size range: US 5.0 to 7.5 (women's shoe sizes)
+- Most common brands: Nike (5), Steve Madden (4), Adidas (4)
+- Shoe types: Sneakers (10), Slippers (9), Heels (6), Loafers (3), Boots (2)
+- Colors: Black (9), White (6), Pink (2), Grey (2), others (11)
 
 ## Dataset Composition
 
@@ -229,27 +237,6 @@ score = model.score(X_test, y_test)
 print(f"R² Score: {score:.3f}")
 ## Exploratory Data Analysis
 ```
-
-### Summary Statistics (Original Data)
-| Statistic | US Size | Shoe Size (mm) | Actual Length (mm) |
-|-----------|---------|----------------|-------------------|
-| count | 30.0 | 30.0 | 30.0 |
-| mean | 9.5 | 275.0 | 279.0 |
-| std | 1.8 | 16.0 | 17.0 |
-| min | 6.0 | 240.0 | 243.0 |
-| 25% | 8.0 | 265.0 | 268.0 |
-| 50% | 9.5 | 275.0 | 278.5 |
-| 75% | 11.0 | 285.0 | 289.0 |
-| max | 13.0 | 310.0 | 315.0 |
-
-### Visualizations
-![EDA Plots](eda_plots.png)
-
-### Key Findings
-- Strong positive correlation (>0.97) between all size measurements
-- No missing values in dataset
-- Balanced representation across shoe types and brands
-
 ## Citation
 bibtex@dataset{zhang2025shoe,
   author = {Mary Zhang},