[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 kick things off by figuring out the approval rates, you know, when the 'class' column is marked as 'good', for each credit history category. Please generate the code with the output in series 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:
