[USER (data scientist)]: Great! Please create pie charts that display loan approval rates for specific customer segments: middle-aged customers aged 40-59, those with stable employment, and those with good or excellent credit history. Loan approval rates are determined by the percentage of good decisions in each customer segment. For stable employment, consider customers who have been employed for 7 years or more, or those with an employment duration between 4 and 7 years. As for good or excellent credit history, this includes customers with a credit history of 'existing paid', 'no credits/all paid', or 'all paid'. Specifically, you can generate scatter plots as Figure objects to visualize the relationships between loan approval rates ('class_binary') and various numerical features ('credit_history', 'employment', 'age', 'credit_amount') in the numerically encoded 'credit_customers_numerical' DataFrame.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import matplotlib.pyplot as plt  
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

# Plot pie charts      
fig, axes = create_subplots(1, 3, figsize=(18, 6))  
  
# YOUR SOLUTION BEGIN:
<code2>
[COMPLETE YOUR CODE]
</code2>
# YOUR SOLUTION END

# save data
save_plot('pred_result/pieplot.png')     
show_plots()

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you with that.
'''
import pandas as pd  
import matplotlib.pyplot as plt  
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:
