[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 the code with the output in dataframe 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, df_copy, get_first_n_rows, create_standard_scaler, fit_transform_standard_scaler, col_assign_val

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

# MY SOLUTION BEGIN:
