[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.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
import pickle 
from decision_company import read_csv_file, fetch_column, calculate_quantile, create_condition, logical_and, filter_by_condition, convert_to_tuples

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

# YOUR SOLUTION BEGIN:
<code1>
[GENERATE 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. 

# MY SOLUTION BEGIN:
