Update README.md

README.md

@@ -1,3 +1,91 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: tabular-classification
+tags:
+- recommendation
+- classification
+- crop-recommendation
+---
+
+# Model Card for Infinitode/CRM-OPEN-ARC
+
+Repository: https://github.com/Infinitode/OPEN-ARC/
+
+## Model Description
+
+OPEN-ARC-CR is a straightforward XGBClassifier model developed as part of Infinitode's OPEN-ARC initiative. It was trained to recommend crops that will thrive under specific environmental constraints and variables.
+
+**Architecture**:
+
+- **XGBClassifier**: Default XGB hyperparams
+- **Framework**: XGBoost
+- **Training Setup**: Trained with `use_label_encoder=False` and used `eval_metric='mlogloss'`.
+
+## Uses
+
+- Identifying appropriate crops for specific environmental conditions.
+- Enhancing crop production by determining optimal environments for growth.
+- Investigating factors that influence crop yields and those that limit productivity.
+
+## Limitations
+
+- Potentially generates implausible or inappropriate recommendations when influenced by extreme outlier values.
+- May provide inaccurate recommendations; exercise caution when relying on these outputs.
+
+## Training Data
+
+- Dataset: Crop Recommendation Dataset from Kaggle.
+- Source URL: https://www.kaggle.com/datasets/varshitanalluri/crop-recommendation-dataset
+- Content: Soil properties, rainfall patterns, and other agricultural metrics, coupled with the recommended crop.
+- Size: 2200 entries of crop recommendations.
+- Preprocessing: Label-encoded target `Crop` using `sklearn's LabelEncoder`.
+
+## Training Procedure
+
+- Metrics: accuracy
+- Train/Testing Split: 80% train, 20% testing.
+
+## Evaluation Results
+
+| Metric | Value |
+| ------ | ----- |
+| Train Accuracy | not used |
+| Testing Accuracy | 98.6% |
+
+## How to Use
+
+```python
+def test_random_samples(model, X_test, y_test, le, n_samples=6):
+    # Select 6 random indices
+    random_indices = random.sample(range(X_test.shape[0]), n_samples)
+    
+    # Extract the random samples
+    X_sample = X_test.iloc[random_indices, :]
+    y_true_sample = y_test.iloc[random_indices]
+    
+    # Predict crop recommendations
+    y_pred_sample = model.predict(X_sample)
+    
+    # Decode the predictions and ground truth back to crop names
+    crops_pred = le.inverse_transform(y_pred_sample)
+    crops_true = le.inverse_transform(y_true_sample)
+    
+    # Display the results
+    for i in range(n_samples):
+        print(f"Sample {i+1}:")
+        print(f"Features: \n{X_sample.iloc[i]}")
+        print(f"Predicted Crop: {crops_pred[i]}")
+        print(f"Ground Truth: {crops_true[i]}")
+        print("-" * 30)
+
+# Test the function with random samples
+test_random_samples(model, X_test, y_test, le)
+```
+
+## Contact
+
+For questions or issues, open a GitHub issue or reach out at https://infinitode.netlify.app/forms/contact.