Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+- roc_auc
+pipeline_tag: tabular-classification
+tags:
+- classification
+- traffic
+---
+
+# Model Card for Infinitode/TAPM-OPEN-ARC
+
+Repository: https://github.com/Infinitode/OPEN-ARC/
+
+## Model Description
+
+OPEN-ARC-TAP is a straightforward XGBClassifier model developed as part of Infinitode's OPEN-ARC initiative. It was developed to assess the probability of traffic accidents by analyzing various external factors.
+
+**Architecture**:
+
+- **XGBClassifier**: `random_state=42`, `use_label_encoder=False`, `eval_metric='logloss'`, `colsample_bytree=0.8`, `learning_rate=0.01`, `max_depth=5`, `n_estimators=100`, `scale_pos_weight=1`, `subsample=0.8`.
+- **Framework**: XGBoost
+- **Training Setup**: Trained without extra training params.
+
+## Uses
+
+- Identifying potential accident-prone or high-risk areas.
+- Enhancing preventive measures for traffic accidents and improving road safety.
+- Researching traffic safety.
+
+## Limitations
+
+- May produce implausible or inappropriate results when affected by extreme outlier values.  
+- Might offer inaccurate predictions regarding the likelihood of an accident; caution is recommended when interpreting these outputs.
+
+## Training Data
+
+- Dataset: Traffic Accident Prediction 💥🚗 dataset from Kaggle.
+- Source URL: https://www.kaggle.com/datasets/denkuznetz/traffic-accident-prediction
+- Content: Weather conditions, road types, time of day, and other factors, along with the occurrence or absence of an accident.
+- Size: 798 entries of traffic data.
+- Preprocessing: Mapped all string values to numeric values and dropped missing values. SMOTE was used to balance class imbalances.
+
+## Training Procedure
+
+- Metrics: accuracy, precision, recall, F1, ROC-AUC
+- Train/Testing Split: 80% train, 20% testing.
+
+## Evaluation Results
+
+| Metric | Value |
+| ------ | ----- |
+| Testing Accuracy | 85.2% |
+| Testing Weighted Average Precision | 87% |
+| Testing Weighted Average Recall | 85% |
+| Testing Weighted Average F1 | 85% |
+| Testing ROC-AUC | 82.5% |
+
+## 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))
+        
+        # Output results
+        print(f"Sample Index: {idx}")
+        print(f"Features: {sample.values}")
+        print(f"True Label: {true_label}, Predicted Label: {prediction[0]}")
+        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.