[USER (data scientist)]:For the eighth question, we want to find any patterns or trends in the data, such as relationships between credit history, loan duration, and employment status. Please provide insights on patterns or trends observed in the data, including any correlations or relationships between variables. Specifically, you can generate a DataFrame to analyze and visualize the relationships between various factors such as credit history, employment status, and loan duration in the 'stable_employment' dataset.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
from sklearn.preprocessing import LabelEncoder 
import pickle 
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

# Print the correlation matrix  
print("Correlation matrix:\n", correlation_matrix)  

# save data
pickle.dump(correlation_matrix,open("./pred_result/correlation_matrix.pkl","wb"))

# Print the relationships between variables  
print("\nRelationship between credit history and loan duration:\n", credit_history_duration_relationship)  

# save data
pickle.dump(credit_history_duration_relationship,open("./pred_result/credit_history_duration_relationship.pkl","wb"))
print("\nRelationship between employment status and loan duration:\n", employment_duration_relationship)  

# save data
pickle.dump(employment_duration_relationship,open("./pred_result/employment_duration_relationship.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you with that.
'''
import pandas as pd  
from sklearn.preprocessing import LabelEncoder 
import pickle 
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:
