[USER (data scientist)]:Hi Tapilot, let's start by answering the first question. We will find clients with high credit amounts and longer loan durations. We can consider high credit amounts as those above the 75th percentile and longer loan durations as those above the 75th percentile as well. Please provide the result type as a list of client IDs and their respective credit amounts and loan durations. Specifically, you can generate a list of tuples from the dataframe, where each tuple contains the index, credit amount, and duration for customers in the 'credit_customers' dataset who have both credit amounts and durations above the 75th percentile.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
import pickle 
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  

# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE] 
</code1>
# YOUR SOLUTION END

print(result_list) 

# save data
pickle.dump(result_list,open("./pred_result/result_list_1.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]:Sure, I can help you with that. 
'''
import pandas as pd 
import pickle 
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  

# YOUR SOLUTION BEGIN:
