[USER (data scientist)]: I will generate the code to identify customer segments in the credit_customers dataset that have a good credit history, are in the age group of 25-45, and have stable employment status. The result type will be a list of target customer segments meeting the specified criteria. Specifically, you can generate a list of target customer segments, which are determined by applying K-means clustering with 4 clusters on the preprocessed credit_customers DataFrame.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
from sklearn.preprocessing import StandardScaler, LabelEncoder  
from sklearn.cluster import KMeans  
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("target_customer_segments:\n", target_customer_segments)  

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you:
'''
import pandas as pd  
from sklearn.preprocessing import StandardScaler, LabelEncoder  
from sklearn.cluster import KMeans  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
