[USER (data scientist)]: 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. 

[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, df_copy, encoder_instance, encode_column, corr, avg, fetch_column, bind_dataframe

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# Calculate the correlation matrix for the encoded dataset  
correlation_matrix = corr(stable_employment)  
  
# Analyze the relationships between variables  
credit_history_duration_relationship = avg(fetch_column(bind_dataframe(stable_employment, 'credit_history'), 'duration'))  
employment_duration_relationship = avg(fetch_column(bind_dataframe(stable_employment, 'employment'), 'duration')) 

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

# save data
pickle.dump(correlation_matrix,open("./ref_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("./ref_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("./ref_result/employment_duration_relationship.pkl","wb"))
  
# Note: You may want to further explore the relationships between other variables or visualize the data using plots to gain more insights. 
'''
[USER (data scientist)]: But there is an error siad: "ValueError: could not convert string to float: '<0'"
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 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.
'''
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:
