[USER (data scientist)]: Thanks! Now, let's normalize the data using Standard Scaling. Please generate a NumPy array of the standardized data suitable for clustering.
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, 

# please import the necessary private functions from decision_company first

# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE 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)]: You got it! Here's how to normalize the data with Standard Scaling:
'''
import pandas as pd 
from sklearn.preprocessing import StandardScaler 
import pickle
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first

# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
