[USER (data scientist)]: Hi Tapilot, I have a dataset called "credit_customers" and I want to find the optimal number of customer segments based on the important columns: credit_history, age, employment, credit_amount, and savings_status. Can you help me extract these columns? Please generate a DataFrame containing selected columns from the dataset for clustering purposes.
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)]: Sure, I can help you with that. Here's the code to extract the important columns:
'''
import pandas as pd 
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
