Create README.md

README.md

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+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+library_name: sklearn
+tags:
+- Traffic
+- ML
+- Random-forest
+- Classification
+- Scikit-learn
+---
+# Traffic Prediction Model  
+
+## Model Description  
+This model is a **Random Forest Classifier** trained to predict **traffic conditions** based on various input features.  
+It helps estimate traffic congestion levels using structured data such as **time of day, weather, and historical patterns**.  
+
+## Training Details  
+- **Algorithm**: Random Forest Classifier  
+- **Dataset**: Custom traffic dataset  
+- **Preprocessing**: Label encoding for categorical variables  
+- **Framework**: scikit-learn  
+
+## How to Use  
+To use this model, install the required libraries and download the model from Hugging Face.  
+
+To load and use the model:
+```python
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(repo_id="AhaseesAI/traffic-prediction", filename="traffic_classifier.pkl")
+encoder_path = hf_hub_download(repo_id="AhaseesAI/traffic-prediction", filename="target_encoder.pkl")
+
+# Load model
+model = joblib.load(model_path)
+target_encoder = joblib.load(encoder_path)
+
+# Example prediction
+sample_data = [[value1, value2, value3, ...]]  # Replace with actual feature values
+prediction = model.predict(sample_data)
+
+# Convert prediction to original label
+predicted_label = target_encoder.inverse_transform(prediction)
+print(f"Predicted Traffic Status: {predicted_label[0]}")