Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+# TPnet-small
+
+TPnet-small is a lightweight deep neural network (DNN) designed to predict traffic congestion using tabular smart mobility features. It serves as a compact yet powerful alternative to tree-based models.
+
+## Model Details
+
+- **Model type**: Feedforward Deep Neural Network (3 hidden layers)
+- **Architecture**: [128, 64, 32] with ReLU activations and Dropout
+- **Output**: 3-class Softmax output (`High`, `Medium`, `Low`)
+- **Trained on**: Smart Mobility Traffic Dataset from Kaggle
+
+## Training Details
+
+- Optimizer: Adam
+- Loss: Sparse Categorical Crossentropy
+- Epochs: 50
+- Batch size: 32
+- Training time: ~6 seconds on CPU
+- Train Accuracy: 99.1%
+- Val Accuracy: 94.6%
+
+## Evaluation
+
+| Metric   | Value |
+|----------|-------|
+| Accuracy | 94.5% |
+| F1 Score | 0.944 |
+| Parameters | 13,123 |
+| Model Size | ~156 KB (.h5 format) |
+
+Includes training trajectory and confusion matrix plots.
+
+## How to Use
+
+```python
+from tensorflow.keras.models import load_model
+
+model = load_model("traffic_predictor_dnn.h5")
+y_pred = model.predict(X_test)  # X_test must be scaled [n_samples, 20]
+```
+
+## Limitations
+
+- Performance limited by small dataset size and feature coverage
+    
+- Currently optimized for CPU inference, not edge deployment
+    
+
+## Authors
+
+- Created by [@Yukin3](https://github.com/Yukin3)
+