[USER (data scientist)]: Alright, let's tackle the second question now. Can you whip up some bar charts that show loan approval rates, you know, when the 'class' column is marked as 'good', for each credit history category, employment status, the middle-age group from 40 to 59 years old, and credit amount range? Don't forget, we're using bins of 2000 for the credit amounts.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import matplotlib.pyplot as plt  
import seaborn as sns  
from decision_company import read_csv_file, create_subplots, df_copy, cast_to_a_dtype, categoricalIndex, categorical_codes, scatter_fig_instance, 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

# Plot scatter plots  
fig, axes = create_subplots(2, 2, figsize=(12, 8))  

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

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you with that.

# MY SOLUTION BEGIN:
