There is the data: credit_customers containing the following columns: ['checking_status', 'duration', 'credit_history', 'purpose', 'credit_amount', 'savings_status', 'employment', 'installment_commitment', 'personal_status', 'other_parties', 'residence_since', 'property_magnitude', 'age', 'other_payment_plans', 'housing', 'existing_credits', 'job', 'num_dependents', 'own_telephone', 'foreign_worker', 'class']. 
--- The description for each column this data is: 
Checking_status: Status of the applicant's checking account ("no checking": No checking account, "<0": Overdrawn or negative balance, "0<=X<200": Low positive balance, e.g., between $0 and $200, and ">=200": Healthy balance)
Duration: Duration of the loan or credit term (measured in months)
Credit_history: Applicant's past handling of credit ("no credits/all paid": No prior credit or all previous credits paid off, "existing paid": Current credits are being paid off diligently, "critical/other existing credit": Past or current credits with issues, like late payments, "delayed previously": History of delayed payments)
Purpose: Reason for the loan or credit application (purchasing a car, financing education, buying electronics ...etc)
Credit_amount: Amount of money requested in the loan or credit application
Savings_status: Amount of savings the applicant has ("no known savings": No savings account or negligible savings, "<100": Savings less than $100, "100<=X<500": Savings between $100 and $500, "500<=X<1000": Savings between $500 and $1000, and ">=1000": Savings of $1000 or more)
Employment: Applicant's employment status or the length of their current employment ("unemployed": No current employment, "<1": Employed for less than a year, "1<=X<4": Employed between 1 and 4 years, ">=7": Employed for 7 years or more)
Installment_commitment: Portion of the applicant's disposable income that is allocated for loan repayments (represented as a percentage or a fixed numerical value)
Personal_status: Details of the applicant's personal and demographic information ("male single": A single male, "female div/dep/mar": A divorced, separated, or married female, "male div/sep": A divorced or separated male, and "male mar/wid": A married or widowed male)
Other_parties: Any third parties involved in the credit agreement ("none": No other parties involved, "guarantor": A guarantor is present who guarantees repayment, and "co-applicant": Another individual is co-signing the loan)
Residence_since: Length of time the applicant has been living at their current residence (measured in years)
Property_magnitude: Type or value of property the applicant owns, serving as potential collateral ("real estate": Owning a house or land, "life insurance": Having a life insurance policy with a cash value, "car": Owning a car, and "no known property": No property that can be used as collateral)
Age: Age of the applicant
Other_payment_plans: Any other ongoing loans or financial obligations the applicant has outside of the loan in question ("none": No other payment plans or ongoing loans, "bank": The applicant has other loans or payment plans with a bank, and "stores": The applicant has credit obligations with stores or retail chains)
Housing: Living situation of the applicant ("own": The applicant owns their home, "rent": The applicant is renting their living space, and "for free": The applicant lives in a space without paying rent (e.g., with family))
Existing_credits: Number of current credit lines or loans that the applicant already has (represented numerically)
Job: Applicant's employment type ("skilled": Professionally skilled job or a white-collar occupation, "unskilled resident": Unskilled job and residing in the country, "high qualif/self emp/mgmt": Highly qualified, self-employed, or in management positions, and "unemp/unskilled non res": Unemployed, lacks professional or skilled training, and is not a resident of the country)
Num_dependents: Number of individuals financially dependent on the applicant
Own_telephone: Whether the applicant has a telephone
Foreign_worker: Whether the applicant is a foreign worker
Class: Target variable for a predictive model, indicating the risk category of the credit application ("good": Low risk, indicating a high likelihood of timely repayment, or "bad": High risk, suggesting potential difficulties in repaying the loan)
---
Considering contents from the dataset and requirements from user. Please note DO NOT CHANGE FILE AND VARIABLE NAMES THAT I HAVE SET!
Interactions begin:
[USER (data scientist)]: Hey there! I've got this dataset called credit_customers, and I wanna find clients with high credit amounts and long loan durations.

[YOU (AI assistant)]: Absolutely! Let's break it down. First, we need to calculate the 75th percentile for credit_amount and duration. Need a hand with that? 

[USER (data scientist)]: Yeah, show me how to get the 75th percentile for both of those. Please calculate and display the 75th percentile values for 'credit_amount' and 'duration' in the 'credit_customers' dataset.

[YOU (AI assistant)]: No problem! Here's how you can calculate the 75th percentile for credit_amount and duration :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
credit_amount_75th = credit_customers['credit_amount'].quantile(0.75)   
duration_75th = credit_customers['duration'].quantile(0.75) 
  
print(f"credit_amount_75th: {credit_amount_75th}, duration_75th: {duration_75th}")

'''

[USER (data scientist)]: Cool! Now, how do I filter the dataset to keep only clients with amounts and durations higher than these values? Please generate a DataFrame representing customers in the 'credit_customers' dataset with credit amounts and durations greater than their respective 75th percentile values.

[YOU (AI assistant)]: You got it! Just use this code to filter the dataset based on the calculated values :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
high_credit_long_duration = credit_customers[(credit_customers['credit_amount'] > credit_amount_75th) & (credit_customers['duration'] > duration_75th)]   
  
print(f"high_credit_long_duration: {high_credit_long_duration}")  

'''

[USER (data scientist)]: Nice one! But I only need the credit_amount and duration columns from this filtered data. How do I get those? Please generate a DataFrame representing a filtered subset of the 'high_credit_long_duration' group in the 'credit_customers' dataset, including only the 'credit_amount' and 'duration' columns.

[YOU (AI assistant)]: Easy peasy! Use this code to extract the columns you need :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
high_credit_long_duration_filtered = high_credit_long_duration[['credit_amount', 'duration']] 

print(f"high_credit_long_duration: {high_credit_long_duration_filtered}")  

'''

[USER (data scientist)]: Sweet! Last thing, can you help me turn the result into a list of tuples? Please generate a list of tuples representing rows from a filtered DataFrame.

[YOU (AI assistant)]: Of course! Here's the code to convert the result into a list of tuples :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
result_list = list(high_credit_long_duration_filtered.itertuples(index=True, name=None))

print(f"high_credit_long_duration: {result_list}")  

'''

[USER (data scientist)]: The dataset's called 'high_credit_long_duration', and you'll find the credit history in the 'credit_history' column. Let's first filter out the clients with a history of late payments. How many clients have a history of late payments?

A. 37
B. 21
C. 55
D. 27
E. None of above

[YOU (AI assistant)]: 