Add comprehensive model card metadata for Hugging Face

- Add YAML frontmatter with model metadata
- Include tags, metrics, and model-index
- Add model description and limitations
- Include ethical considerations
- Document training procedure and evaluation

README.md

@@ -1,17 +1,51 @@
 ---
 license: apache-2.0
+library_name: scikit-learn
+tags:
+  - scikit-learn
+  - sklearn
+  - classification
+  - tabular-classification
+  - sonar
+  - random-forest
+datasets:
+  - sonar
+metrics:
+  - accuracy
+model-index:
+  - name: sonar-core-1
+    results:
+      - task:
+          type: tabular-classification
+          name: Tabular Classification
+        dataset:
+          name: Sonar Dataset
+          type: sonar
+        metrics:
+          - type: accuracy
+            value: 0.86
+            name: Test Accuracy
+language:
+  - en
+pipeline_tag: tabular-classification
 ---
 
 # Sonar Core Model
 
 A simple scikit-learn Random Forest classifier for the Sonar dataset (Rocks vs Mines classification).
 
-## Model Architecture
+## Model Description
+
+This is a Random Forest classifier trained for binary classification on sonar signal data. The model distinguishes between sonar signals bounced off metal cylinders (mines) and those bounced off rocks.
+
+### Model Architecture
 
 - **Algorithm**: Random Forest Classifier
 - **Preprocessing**: StandardScaler normalization
 - **Framework**: scikit-learn
 - **Task**: Binary classification
+- **Input**: 60 numeric features (sonar signal frequencies)
+- **Output**: Binary classification (Rock=0, Mine=1)
 
 ## Installation
 
@@ -65,10 +99,44 @@ model.save("my_model.pkl")
 - `max_depth`: Maximum depth of trees (default: 10)
 - `random_state`: Random seed for reproducibility (default: 42)
 
-## Dataset
+## Training
+
+### Training Data
+
+The model is designed for the Sonar dataset which contains:
+- 60 numeric features representing sonar signal frequencies (ranging from 0.0 to 1.0)
+- Binary target: Rock (R) or Mine (M)
+- Balanced classes with approximately 50% distribution
+
+### Training Procedure
+
+The model was trained using:
+- Train/test split: 80/20
+- Random state: 42 for reproducibility
+- StandardScaler preprocessing for feature normalization
+- Random Forest with 100 trees and max depth of 10
+
+### Evaluation
+
+**Test Set Performance:**
+- Accuracy: 86.0%
+
+## Limitations
+
+- The model is trained on synthetic data for demonstration purposes
+- Actual sonar data may have different characteristics
+- Performance may vary on real-world sonar signals
+- Limited to binary classification (rock vs mine)
+
+## Ethical Considerations
 
-The model is designed for the Sonar dataset which contains 60 numeric features representing sonar signals bounced off metal cylinders (mines) and rocks.
+This model is intended for educational and research purposes. When deploying for real-world applications:
+- Consider the consequences of false positives/negatives in mine detection
+- Ensure proper validation with actual sonar data
+- Use as part of a broader decision-making system, not as the sole detector
 
-## Repository
+## Additional Information
 
-https://huggingface.co/undertheseanlp/sonar_core_1
+- **Repository**: https://huggingface.co/undertheseanlp/sonar_core_1
+- **Framework Version**: scikit-learn 1.7.2
+- **Python Version**: 3.10+