[USER (data scientist)]: Now that we've taken care of encoding the categorical variables, let's jump to question three. Can you create some code to figure out if we need to normalize the credit_customers dataset? Just see if the value goes beyond 1, and if it does, we should normalize it and generate the top 5 rows of normalized dataframe. Oh, and don't forget to recommend a normalization method if it's needed.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import numpy as np
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 after Standard Scaling:\n", credit_customers_normalized.head())  

# save data
pickle.dump(credit_customers_normalized.head(),open("./pred_result/credit_customers_normalized_head.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you:
'''
import pandas as pd  
import numpy as np
from sklearn.preprocessing import StandardScaler 
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
