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README.md

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+# AutoML Regression Model for Shoe Dataset
+
+## Model Summary
+This model was trained using **AutoGluon Tabular (v1.4.0)** on the dataset [maryzhang/hw1-24679-tabular-dataset](https://huggingface.co/datasets/maryzhang/hw1-24679-tabular-dataset).  
+The task is **regression**, predicting the **actual measured shoe length (mm)** from shoe attributes.
+
+- **Best Model**: `CatBoost_r177_BAG_L1` (stacked ensemble of CatBoost models)  
+- **Test R² Score**: **0.8904**  
+- **Validation R² Score**: 0.8049  
+- **Pearson correlation**: 0.9473  
+- **RMSE**: 1.80 mm  
+- **MAE**: 1.10 mm  
+- **Median AE**: 0.68 mm  
+
+---
+
+## Leaderboard (Top 5 Models)
+| Rank | Model                  | Test R² | Val R²  | Pred Time (s) | Fit Time (s) |
+|------|------------------------|---------|---------|---------------|--------------|
+| 1    | CatBoost_r177_BAG_L1   | 0.8994  | 0.8049  | 0.0293        | 27.14        |
+| 2    | LightGBMLarge_BAG_L2   | 0.8971  | 0.7995  | 0.7011        | 238.93       |
+| 3    | CatBoost_BAG_L2        | 0.8939  | 0.8405  | 0.6155        | 276.40       |
+| 4    | CatBoost_r9_BAG_L1     | 0.8917  | 0.7889  | 0.0606        | 53.87        |
+| 5    | WeightedEnsemble_L3    | 0.8904  | 0.8500  | 0.9871        | 333.68       |
+
+---
+
+## Dataset
+- **Source**: [maryzhang/hw1-24679-tabular-dataset](https://huggingface.co/datasets/maryzhang/hw1-24679-tabular-dataset)  
+- **Size**: 338 samples (30 original, 308 augmented)  
+- **Features**:
+  - US size (numeric)  
+  - Shoe size (mm) (numeric)  
+  - Type of shoe (categorical)  
+  - Shoe color (categorical)  
+  - Shoe brand (categorical)  
+
+---
+
+## Intended Use
+- Educational use only  
+- Demonstration of **AutoML for regression** in CMU course 24-679  
+
+Not suitable for real-world footwear sizing or medical/orthopedic applications.  
+
+---
+
+## License
+- Dataset: MIT  
+- Model: MIT  
+
+---
+
+## Acknowledgments
+- Dataset by **Mary Zhang (CMU 24-679)**  
+- AutoML training performed by **Yash Sakhale** using AutoGluon v1.4.0  
+

learner.pkl

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metadata.json

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