[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 
 
# Load the dataset 
credit_customers = pd.read_csv("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 a DataFrame containing selected columns for clustering analysis.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
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("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?
'''
import pandas as pd 
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
