Update README.md

README.md

@@ -1,3 +1,116 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+pipeline_tag: tabular-classification
+tags:
+- classification
+- crop-health
+---
+
+# Model Card for Infinitode/PSPM-OPEN-ARC
+
+Repository: https://github.com/Infinitode/OPEN-ARC/
+
+## Model Description
+
+OPEN-ARC-PSP is a straightforward XGBClassifier model developed as part of Infinitode's OPEN-ARC initiative. It was designed to potentially identify plants experiencing high stress caused by external factors.
+
+**Architecture**:
+
+- **XGBClassifier**: `n_estimators=100`, `learning_rate=0.1`, `max_depth=6`, `subsample=0.8`, `colsample_bytree=0.8`, `random_state=42`.
+- **Framework**: XGBoost
+- **Training Setup**: Trained with the default training params.
+
+## Uses
+
+- Identifying crops experiencing significant stress.
+- Improving crop production by mitigating major stressors affecting plants.
+- Performing experimental studies on plant behavior and yield outcomes influenced by stress levels.
+
+## Limitations
+
+- May generate implausible or inappropriate results when influenced by extreme outlier values.
+- Could provide inaccurate plant stress levels; caution is advised when relying on these outputs.
+
+## Training Data
+
+- Dataset: Plant-Health-Data dataset from Kaggle.
+- Source URL: https://www.kaggle.com/datasets/ziya07/plant-health-data
+- Content: Soil characteristics, moisture levels, and various agricultural metrics, combined with the anticipated stress level of the plant.
+- Size: 1200 entries of plant stress levels.
+- Preprocessing: Dropped unnecessary features like the `Timestamp` and `Plant_ID`. Stress levels were manually mapped to three distinct numerical values.
+
+## Training Procedure
+
+- Metrics: accuracy, precision, recall, F1
+- Train/Testing Split: 80% train, 20% testing.
+
+## Evaluation Results
+
+| Metric | Value |
+| ------ | ----- |
+| Testing Accuracy | 99.1% |
+| Testing Weighted Average Precision | 99% |
+| Testing Weighted Average Recall | 99% |
+| Testing Weighted Average F1 | 99% |
+
+## How to Use
+
+```python
+import random
+
+def test_random_samples(model, X_test, y_test, n_samples=5):
+    """
+    Selects random samples from the test set, makes predictions, and compares with actual values.
+    
+    Parameters:
+    - model: Trained XGBoost classifier.
+    - X_test: Feature set for testing.
+    - y_test: True labels for testing.
+    - n_samples: Number of random samples to test.
+    
+    Returns:
+    None
+    """
+    # Convert X_test and y_test to DataFrame for easier indexing
+    X_test_df = X_test.reset_index(drop=True)
+    y_test_df = y_test.reset_index(drop=True)
+
+    # Pick random indices
+    random_indices = random.sample(range(len(X_test)), n_samples)
+    
+    print("Testing on Random Samples:")
+    for idx in random_indices:
+        sample = X_test_df.iloc[idx]
+        true_label = y_test_df.iloc[idx]
+        
+        # Predict using the model
+        prediction = model.predict(sample.values.reshape(1, -1))
+
+        # Reverse the health mapping
+        reverse_health_mapping = {v: k for k, v in health_mapping.items()}
+
+        # Map true and predicted labels
+        true_label_description = reverse_health_mapping[true_label]
+        predicted_label_description = reverse_health_mapping[prediction[0]]
+        
+        # Output results
+        print(f"Sample Index: {idx}")
+        print(f"Features: {sample.values}")
+        print(f"True Label: {true_label}, Predicted Label: {prediction[0]}")
+        print(f"True Label (Description): {true_label_description}, Predicted Label (Description): {predicted_label_description}")
+        print("-" * 40)
+
+# Example usage
+test_random_samples(xgb, X_test, y_test)
+```
+
+## Contact
+
+For questions or issues, open a GitHub issue or reach out at https://infinitode.netlify.app/forms/contact.