CRM-OPEN-ARC

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

How to Use

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.