# Express Company Predictor

This script predicts the CHINESE express company based on a given tracking number. It uses a pre-trained model and a vectorizer to convert the tracking number into numeric features and then predicts the company using the model.

- Note: The trained model is based on 630,000 tracking number data in my database. Only for checking the Chinese courier company's tracking number.

## Requirements

- Python 3.x
- scikit-learn
- joblib

To install the dependencies, run:

```bash
pip install scikit-learn joblib
```

## Sample code
```python
import pickle
import sys

def predict_express_company(tracking_number):
    # Load the trained model and vectorizer
    with open("model.pkl", "rb") as model_file:
        classifier = pickle.load(model_file)
    with open("vectorizer.pkl", "rb") as vectorizer_file:
        vectorizer = pickle.load(vectorizer_file)

    # Convert the input tracking number into numeric features
    tracking_number_vec = vectorizer.transform([tracking_number])

    # Use the model for prediction
    predicted_company = classifier.predict(tracking_number_vec)

    return predicted_company[0]

if __name__ == "__main__":
    if len(sys.argv) > 1:
        tracking_number = sys.argv[1]
        result = predict_express_company(tracking_number)
        print(f"The predicted express company is: {result}")
    else:
        print("Please enter a tracking number.")
```
{}
region:us
TAGS
#region-us 

# Express Company Predictor

This script predicts the CHINESE express company based on a given tracking number. It uses a pre-trained model and a vectorizer to convert the tracking number into numeric features and then predicts the company using the model.

- Note: The trained model is based on 630,000 tracking number data in my database. Only for checking the Chinese courier company's tracking number.

## Requirements

- Python 3.x
- scikit-learn
- joblib

To install the dependencies, run:



## Sample code

# Express Company Predictor

This script predicts the CHINESE express company based on a given tracking number. It uses a pre-trained model and a vectorizer to convert the tracking number into numeric features and then predicts the company using the model.

- Note: The trained model is based on 630,000 tracking number data in my database. Only for checking the Chinese courier company's tracking number. ## Requirements

- Python 3.x
- scikit-learn
- joblib

To install the dependencies, run: ## Sample code
TAGS
#region-us 
 # Express Company Predictor

This script predicts the CHINESE express company based on a given tracking number. It uses a pre-trained model and a vectorizer to convert the tracking number into numeric features and then predicts the company using the model.

- Note: The trained model is based on 630,000 tracking number data in my database. Only for checking the Chinese courier company's tracking number. ## Requirements

- Python 3.x
- scikit-learn
- joblib

To install the dependencies, run: ## Sample code
TAGS
#region-us 
# Express Company Predictor

This script predicts the CHINESE express company based on a given tracking number. It uses a pre-trained model and a vectorizer to convert the tracking number into numeric features and then predicts the company using the model.

- Note: The trained model is based on 630,000 tracking number data in my database. Only for checking the Chinese courier company's tracking number.## Requirements

- Python 3.x
- scikit-learn
- joblib

To install the dependencies, run:## Sample code