[USER (data scientist)]: Hey Tapilot! I've got this dataset of credit customers, and I'm trying to find customer segments with good credit history, ages between 25 and 45, and stable jobs. Can you help me out? 

[YOU (AI assistant)]: Absolutely! Let's start by loading your dataset. Is it in a CSV file? 

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

[YOU (AI assistant)]: Cool! To load the dataset, you can use pandas like this: 
'''
import pandas as pd 
import pickle 
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Nice, now I need to pick out some important columns. I'm thinking 'credit_history', 'age', 'employment', 'credit_amount', and 'savings_status'. How do I do that? 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)]: You got it! Just use this code to extract those columns:
'''
import pandas as pd 
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
