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---
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. |