[USER (data scientist)]: Thanks! Now, I want to normalize the data using Standard Scaling. Can you help me with that? Please generate the code with the output in NumPy array type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
from sklearn.preprocessing import StandardScaler
import pickle
from decision_company import read_csv_file, create_standard_scaler, fit_transform_standard_scaler

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]  
</code1>
# YOUR SOLUTION END

print("data_for_clustering_scaled:\n", data_for_clustering_scaled)    

# save data
pickle.dump(data_for_clustering_scaled,open("./pred_result/data_for_clustering_scaled.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Absolutely! Here's the code to normalize the data using Standard Scaling:

# MY SOLUTION BEGIN:
