Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+# TPnet-baseline
+
+TPnet-baseline is a Random Forest classifier trained on smart mobility and traffic features to predict traffic congestion levels (Low, Medium, High) in urban environments.
+
+## Model Details
+
+- **Model type**: Random Forest Classifier
+- **Input features**: 20 numerical features including vehicle count, road occupancy, weather, traffic light status, time-of-day, and more
+- **Output**: Multiclass classification – `High`, `Medium`, `Low` traffic congestion
+- **License**: MIT
+- **Trained on**: Smart Mobility Traffic Dataset from Kaggle
+
+## Training Details
+
+- Train/test split: 80/20
+- Accuracy (test): 99.9%
+- F1 Score: 0.999
+- Class-balanced via stratified sampling
+- No overfitting observed
+
+## Evaluation
+
+| Metric     | Value  |
+|------------|--------|
+| Accuracy   | 99.9%  |
+| F1 Score   | 0.999  |
+| Model Size | ~1.2MB |
+
+Confusion matrix and full report are available in the repository.
+
+## How to Use
+
+```python
+import pickle
+
+with open("traffic_predictor_rf.pkl", "rb") as f:
+    model = pickle.load(f)
+
+y_pred = model.predict(X_test)  # where X_test is a [n_samples, 20] array
+```
+
+## Limitations
+
+- Does not account for live data
+    
+- Designed for offline batch inference
+    
+- Assumes all 20 features are properly preprocessed and scaled
+    
+
+## Authors
+
+- Created by [@Yukin3](https://github.com/Yukin3)
+