[USER (data scientist)]: Great! Please generate scatter plots showing the correlation between credit_history, employment, age, and credit_amount with loan approval rates (approval refers to column `class` is `good`). 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  
import seaborn as sns  
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE]
</code1>
# YOUR SOLUTION END

# Plot scatter plots  
fig, axes = plt.subplots(2, 2, figsize=(12, 8))  

# YOUR SOLUTION BEGIN:
<code2>
[COMPLETE YOUR CODE]
</code2>
# YOUR SOLUTION END

plt.savefig('pred_result/scatterplot.png')
plt.show()

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you with that.
'''
import pandas as pd  
import matplotlib.pyplot as plt  
import seaborn as sns  
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
