[USER (data scientist)]: Yeah, that'd be great. Let's use StandardScaler for that. Please generate a NumPy array containing the scaled data for clustering.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
from sklearn.preprocessing import StandardScaler 
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("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! Let's normalize the data. Now, it's time to pick a clustering algorithm. Do you prefer K-means or hierarchical clustering?
'''
import pandas as pd 
from sklearn.preprocessing import StandardScaler 
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
