[USER (data scientist)]: Great! Now, how about creating a correlation matrix to visualize the relationships between the variables? Please generate and display a heatmap visualizing the correlation matrix for the 'credit_customers' dataset.
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, 

# 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

# Distribution plot for age 
plt.figure(figsize=(12, 10)) 

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

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: You got it! We can use the seaborn library to create a heatmap of the correlation matrix. Here's the code for that:
'''
import pandas as pd 
import matplotlib.pyplot as plt 
import seaborn as sns 
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:
