[USER (data scientist)]: Yeah, that'd be great. Let's use StandardScaler for 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)]: You got it! Let's normalize the data. Now, it's time to pick a clustering algorithm. Do you prefer K-means or hierarchical clustering?

# MY SOLUTION BEGIN:
