[USER (data scientist)]: Awesome! Now, let's make some scatter plots to visualize the correlations. How about we start with loan approval rates and credit history? Please generate the code with the output in plot type.
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")   

fig, axes = create_subplots(2, 2, figsize=(12, 8))  
  
# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]  
</code1>
# YOUR SOLUTION END

# save data
save_plot('ref_result/scatterplot_1.png')
show_plots()

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sounds good! Let's create a scatter plot for loan approval rates vs. credit history using seaborn and matplotlib:

# MY SOLUTION BEGIN:
