[USER (data scientist)]: And if I do need to normalize it, how can I use Standard Scaling for that? Please note that Standard Scaling is commonly used and will be effective in this case, where each feature is centered around 0 with a standard deviation of 1. Please generate a DataFrame of the normalized dataset using Standard Scaling.
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

if normalization_needed: 
 print("Data after Standard Scaling:\n", get_first_n_rows(credit_customers_normalized)) 
 
# save data
pickle.dump(get_first_n_rows(credit_customers_normalized), open("./pred_result/credit_customers_normalized_head.pkl", "wb")) 
else:
 print("Normalization is not needed.")

---END CODE TEMPLATE---

[YOU (AI assistant)]: If normalization is needed, you can use proper functions from decision_company. Here's the code to do that:
'''
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:
