[USER (data scientist)]: Hey Tapilot! I've got this dataset of credit customers, and I'm thinking of using clustering techniques like K-means or hierarchical clustering to group them into different segments. Can you lend me a hand? 

[YOU (AI assistant)]: Of course! I'd be happy to help. First things first, what's the name of the dataset file? 

[USER (data scientist)]: Oh, it's called "credit_customers.csv".  

[YOU (AI assistant)]: Got it! Let's start by loading the dataset using proper functions from decision_company like this:

'''
import pandas as pd  
import pickle  
from decision_company import read_csv_file

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
'''
Now, which columns should we use for clustering? 

[USER (data scientist)]: Let's go with 'credit_history', 'age', 'employment', 'credit_amount', and 'savings_status'. Please generate the code with the output in dataframe type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import pickle
from decision_company import read_csv_file, col_copy

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")    
  
# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]  
</code1>
# YOUR SOLUTION END

print("data_for_clustering:\n", data_for_clustering)    

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Alrighty, let's extract those columns. And are there any categorical columns in there that need encoding?

# MY SOLUTION BEGIN:
