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README.md

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+---
+library_name: sklearn
+tags:
+- sklearn
+- scikit-learn
+- classification
+- wine
+- random-forest
+---
+
+# Wine Classification Model
+
+A RandomForestClassifier model trained on the UCI Wine dataset for wine classification.
+
+## Model Details
+
+- **Model Type**: RandomForestClassifier
+- **Dataset**: UCI Wine Dataset
+- **Number of Features**: 13
+- **Number of Classes**: 3
+- **Classes**: class_0, class_1, class_2
+
+## Model Parameters
+
+- `n_estimators`: 100
+- `max_depth`: 6
+- `random_state`: 42
+
+## Usage
+
+### Using Hugging Face Hub
+
+```python
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="alirisheh/test1", filename="model.joblib")
+model = joblib.load(model_path)
+
+# Make predictions
+predictions = model.predict(X_test)
+```
+
+### Using the Hugging Face Inference API
+
+You can also use this model with the Hugging Face Inference API once it's deployed.
+
+## Training
+
+The model was trained on the scikit-learn wine dataset with an 80/20 train/test split.
+
+## Evaluation
+
+The model achieves high accuracy on the test set. See `model_metadata.json` for detailed metrics.
+
+## Files
+
+- `model.joblib`: The trained scikit-learn model
+- `model_metadata.json`: Model metadata and training information
+- `sample_input.json`: Sample input for testing

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33969f544a65c85b6e962282b35e3c593d45452dcc2f620183b6c98cc5663a1c
+size 213681

model_metadata.json

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+{
+  "model_type": "RandomForestClassifier",
+  "n_estimators": 100,
+  "max_depth": 6,
+  "accuracy": 1.0,
+  "n_features": 13,
+  "n_classes": 3,
+  "class_names": [
+    "class_0",
+    "class_1",
+    "class_2"
+  ],
+  "feature_names": [
+    "alcohol",
+    "malic_acid",
+    "ash",
+    "alcalinity_of_ash",
+    "magnesium",
+    "total_phenols",
+    "flavanoids",
+    "nonflavanoid_phenols",
+    "proanthocyanins",
+    "color_intensity",
+    "hue",
+    "od280/od315_of_diluted_wines",
+    "proline"
+  ]
+}

sample_input.json

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+{
+  "sample": [
+    13.64,
+    3.1,
+    2.56,
+    15.2,
+    116.0,
+    2.7,
+    3.03,
+    0.17,
+    1.66,
+    5.1,
+    0.96,
+    3.36,
+    845.0
+  ],
+  "expected_class": 0,
+  "class_name": "class_0"
+}