[USER (data scientist)]: Nice work! Now, how about calculating the loan approval rates for each of these segments? Please generate the code with the output in [series, series, series] type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd   
import pickle
from decision_company import read_csv_file, logical_and, check_elements_in_list, filter_by_condition, count_unique_values, create_subplots, set_plot_split_title, set_layout, show_plots, save_plot, fetch_column

# 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_middle_aged)

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

print(approval_rates_stable_employment)

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

print(approval_rates_good_credit_history)

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: You got it! Let's calculate the approval rates for each customer segment:

# MY SOLUTION BEGIN:
