[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 
from decision_company import read_csv_file

# Load the dataset   
credit_customers = read_csv_file("credit_customers.csv") 
'''

[USER (data scientist)]: Cool, thanks! Now, let's start by calculating the approval rates for each credit history category. Please generate the code with the output in dataframe type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd   
import pickle
from decision_company import read_csv_file, get_fig_from_df, count_unique_values, set_layout, bind_dataframe, pivot_a_level, fill_missing_values, create_subplots, show_plots, save_plot, fetch_column, logical_and, filter_by_condition

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

print(approval_rates_credit_history)

# save data
pickle.dump(approval_rates_credit_history,open("./pred_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:

# MY SOLUTION BEGIN:
