[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 
import pickle  
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")

# Calculate the correlation matrix for the dataset
correlation_matrix = stable_employment.corr()
# Print the correlation matrix
print("Correlation matrix:\n", correlation_matrix)
# Analyze the relationships between variables
credit_history_duration_relationship = stable_employment.groupby('credit_history')['duration'].mean()
employment_duration_relationship = stable_employment.groupby('employment')['duration'].mean()
# Print the relationships between variables  
print("\nRelationship between credit history and loan duration:\n", credit_history_duration_relationship)  

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

[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 
  
# Load the dataset  
credit_customers = pd.read_csv("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 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")

# YOUR SOLUTION BEGIN:
