[USER (data scientist)]: Hey, I need some help analyzing a dataset of credit customers. I want to find out how loan approval rates vary across different factors like credit history categories, employment statuses, age groups (specifically 40-59), and credit amount ranges. Can you help me with that? 

[YOU (AI assistant)]: Of course! Let's start by loading your dataset. Do you have a CSV file with the data? 

[USER (data scientist)]: Yeah, it's called "credit_customers.csv". 

[YOU (AI assistant)]: Awesome! Let's load the dataset using pandas. Here's the code for that: 
'''
import pandas as pd 

# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Cool, thanks! Now, let's start by calculating the approval rates for each credit history category. Please calculate and display the approval rates for different credit histories in the 'credit_customers' dataset.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
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(approval_rates_credit_history)

# save data
pickle.dump(approval_rates_credit_history,open("./ref_result/approval_rates_credit_history.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure thing! Here's the code to calculate approval rates for credit history categories:
'''
import pandas as pd 
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
