[USER (data scientist)]: Awesome! Finally, let's figure out the approval rates for various credit amount ranges. Just remember, we'll be using bins of 2000 for the credit amounts. Please categorize the 'credit_amount' into specified ranges in the 'credit_customers' dataset, calculate and display a Series of the approval rates for these credit amount ranges focusing on 'good' class outcomes.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
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

print(approval_rates_credit_amount)

# save data
pickle.dump(approval_rates_credit_amount,open("./pred_result/approval_rates_credit_amount.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure thing! Here's the code to calculate approval rates for credit amount ranges:
'''
import pandas as pd 
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:
