[USER (data scientist)]: Hey Tapilot! I've got this dataset of credit customers, and I'm trying to find customer segments that might be more responsive to promotions and financing options. Can you help me out? 

[YOU (AI assistant)]: Of course! Let's start by loading your dataset. What's the file name? 

[USER (data scientist)]: It's called "credit_customers.csv". 

[YOU (AI assistant)]: Alright, let's load it up: 

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

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
'''
Which columns do you think are important for clustering the customers? 

[USER (data scientist)]: I'd say '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)]: Got it, let's extract those columns. Now, should we encode the categorical columns in any way?

# MY SOLUTION BEGIN:
