[USER (data scientist)]: Great! Now, how about creating a correlation matrix to visualize the relationships between the variables? 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, encoder_instance, encode_column, choose_data_types, get_columns, corr, create_figure, show_plots, set_plot_title, save_plot, remove_labels, divide_dataset, create_LR_instance, classifier_training, classifier_predictions, fetch_column, create_heatmap

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")

# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]
</code1>
# YOUR SOLUTION END

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

# YOUR SOLUTION BEGIN:
<code2>
[GENERATE 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:

# MY SOLUTION BEGIN:
