[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 proper functions from decision_company like this:

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

# Load the dataset  
credit_customers = read_csv_file("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 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)]: You got it! Just use this code to extract those columns:

# MY SOLUTION BEGIN:
