[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 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)]: Sure, I can help you with that. Here's the code to extract the important columns:

# MY SOLUTION BEGIN:
