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)]: Hey, I've got this dataset of clients and their credit history. I need to find out who's been late on payments or defaulted before. Can you help me out? 

[YOU (AI assistant)]: Of course! I'd be happy to help. Just let me know the name of the dataset and which column has the credit history info. 

[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 which means who has been late on payments refers to 'credit_history' equals to 'delayed previously'. Please generate a DataFrame containing only the customers with a history of delayed payments.

[YOU (AI assistant)]: Got it, thanks! Here's the code for that. And do you want me to grab the client IDs for these folks? :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
late_payments = high_credit_long_duration[high_credit_long_duration['credit_history'] == 'delayed previously'] 
  
print(f"late_payments: {late_payments}")

'''

[USER (data scientist)]: Yeah, that'd be great. Please generate an Index object containing the IDs of customers with late payments.

[YOU (AI assistant)]: No problem! Here's the code to get those client IDs :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
result_IDs = late_payments.index

print(f"result_IDs: {result_IDs}")

'''

[USER (data scientist)]: A list would be perfect. Please generate a list from the 'result_IDs' series in the dataset.

[YOU (AI assistant)]: You got it! Here's the code to turn the result into a list :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
result_list = list(result_IDs)

print(f"result_list: {result_list}")

'''

[USER (data scientist)]: I've got this dataset called "high_credit_long_duration" and I need to find clients with multiple existing credits and high installment commitments. We also assume that `high` installment commitments are those above the `median` value of the 'installment_commitment' column. Can you help me out? Please generate a DataFrame of clients with multiple existing credits and higher than median installment commitments.

[YOU (AI assistant)]: Sure! You can use the following code to find clients with multiple existing credits and high installment commitments :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
# Filter the clients with multiple existing credits and high installment commitments
multiple_credits_high_commitment = high_credit_long_duration[(high_credit_long_duration['existing_credits'] > 1) & (high_credit_long_duration['installment_commitment'] > credit_customers['installment_commitment'].median())]
  
print(f"multiple_credits_high_commitment: {multiple_credits_high_commitment}")

'''

[USER (data scientist)]: Great! Can you give me the client IDs with multiple existing credits and high installment commitments? Please generate an Index object containing the indices of customers with multiple credits and high commitment.

[YOU (AI assistant)]: No problem! Here's the code to get those client IDs :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
# Extract the client IDs
multiple_credits_high_commitment_idx = multiple_credits_high_commitment.index

print(f"multiple_credits_high_commitment_idx: {multiple_credits_high_commitment_idx}")

'''

[USER (data scientist)]: That looks good! But, a list would be perfect. Please generate a list containing specific indices or elements from the dataset.

[YOU (AI assistant)]: You got it! Here's the code to turn the result into a list :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
result_list = list(multiple_credits_high_commitment_idx)

print(f"result_list: {result_list}")

'''

[USER (data scientist)]: Hey, I've got this dataset called "high_credit_long_duration" and I need to filter out some clients based on their age. Can you help me with that?

[YOU (AI assistant)]: Of course! Let's start by setting a lower age limit. What's the youngest age you want to include? 

[USER (data scientist)]: I'm thinking 25 years old should be the minimum. Please generate a DataFrame of clients aged 25 and above.

[YOU (AI assistant)]: Alright, let's filter out clients who are at least 25 years old. Here's the code for that :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
clients_min_age = high_credit_long_duration[high_credit_long_duration['age'] >= 25] 
  
print(f"clients_min_age: {clients_min_age}")

'''

[USER (data scientist)]: Cool, now I also want to set an upper limit. Let's say no one older than 55 years. Please generate a DataFrame of clients aged between 25 to 55 years.

[YOU (AI assistant)]: Got it! Let's modify the previous code to include clients aged between 25 and 55 :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
clients_25_to_55 = high_credit_long_duration[(high_credit_long_duration['age'] >= 25) & (high_credit_long_duration['age'] <= 55)] 

print(f"clients_25_to_55: {clients_25_to_55}")

'''

[USER (data scientist)]: Nice! So, how many clients do we have in this filtered dataset? Please generate a value indicating the number of rows in a specific subset of the dataset.

[YOU (AI assistant)]: You can find the number of clients by using the 'shape' attribute of the filtered DataFrame. Here's how you can do it :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
result_count = clients_25_to_55.shape[0] 

print(f"result_count: {result_count}")

'''

[USER (data scientist)]: Hey, I've got this dataset called "high_credit_long_duration" and I need to find clients with stable jobs. Can you help me out? Please generate a DataFrame of customers with stable employment (employment duration of 4 to 7 years or more than 7 years).

[YOU (AI assistant)]: Of course! Let's filter those clients for you. Here's the code :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
stable_employment = high_credit_long_duration[(high_credit_long_duration['employment'] == '4<=X<7') | (high_credit_long_duration['employment'] == '>=7')] 
  
print(f"stable_employment: {stable_employment}")

'''

[USER (data scientist)]: Awesome, thanks! Now, how about getting their client IDs? Please extract and save the index of clients with stable employment as a Series in a pickle file..

[YOU (AI assistant)]: No problem! You can extract the client IDs like this :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
# Extract the client IDs
stable_employment_idx = stable_employment.index 

print(f"stable_employment_idx: {stable_employment_idx}")

'''

[USER (data scientist)]: Sweet! And can I get that as a list? Please generate a list from a selected index in the dataset.

[YOU (AI assistant)]: Absolutely! Here's how you can convert the result into a list :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
result_list = list(stable_employment_idx)

print(f"result_list: {result_list}")

'''

[USER (data scientist)]: Hey, I've got this dataset called "stable_employment" and I need to do some filtering based on the 'housing' column. Can you help me out with filtering for clients who have 'rent' as their housing type? Please generate a DataFrame of customers living in rented housing.

[YOU (AI assistant)]: Absolutely! To filter the clients with 'rent' as their housing type, you can use this code :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
rented_housing = stable_employment[(stable_employment['housing'] == 'rent')] 
  
print(f"rented_housing: {rented_housing}")

'''

[USER (data scientist)]: Cool, thanks! Now, what about those with 'own' as their housing type? Please generate a DataFrame of customers who own their housing from the dataset.

[YOU (AI assistant)]: No problem! Here's the code to filter the clients with 'own' as their housing type :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
owned_housing = high_credit_long_duration[high_credit_long_duration['housing'] == 'own'] 

print(f"owned_housing: {owned_housing}")

'''

[USER (data scientist)]: Great, now I need to put these two together. How do I do that? Please generate a combined DataFrame of rented and owned housing data.

[YOU (AI assistant)]: You can combine the rented_housing and owned_housing datasets using this code :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
rented_owned_housing = pd.concat([rented_housing, owned_housing]) 

print(f"rented_owned_housing: {rented_owned_housing}")

'''

[USER (data scientist)]: Awesome! Lastly, I need to know how many clients are in this combined dataset. Can you help me with that? Please generate a value representing the number of entries in a specific subset of the dataset.

[YOU (AI assistant)]: Sure thing! To get the count of clients in the combined dataset, use this code :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
result_count = rented_owned_housing.shape[0]

print(f"result_count: {result_count}")

'''

[USER (data scientist)]: Hey, I've got this dataset of clients with stable jobs, and I wanna figure out some common traits of those who could benefit from debt consolidation. Can you help me out? 

[YOU (AI assistant)]: Absolutely! Let's start by finding the average credit amount for these clients. What's the name of the DataFrame with the data? 

[USER (data scientist)]: Oh, it's called 'stable_employment'. Please calculate and save the average credit amount as a numerical value in a pickle file.

[YOU (AI assistant)]: Cool, let's calculate the average credit amount for the clients in 'stable_employment'. Here's the code :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
average_credit_amount = stable_employment['credit_amount'].mean() 
  
print(f"average_credit_amount: {average_credit_amount}")

'''

[USER (data scientist)]: Nice one! What about the average loan duration? Please generate a value representing the average loan duration from the specified subset of customers.

[YOU (AI assistant)]: Sure thing! Here's the code to find the average loan duration for the clients in 'stable_employment' :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
average_loan_duration = stable_employment['duration'].mean() 

print(f"average_loan_duration: {average_loan_duration}")

'''

[USER (data scientist)]: Awesome! Now, I'm curious about the most common job status among them. Can you find that for me? Please generate a value representing the most common employment status in the dataset.

[YOU (AI assistant)]: Of course! Here's the code to get the most common employment status in the 'stable_employment' DataFrame :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
most_common_employment = stable_employment['employment'].mode().iloc[0]

print(f"most_common_employment: {most_common_employment}")

'''

[USER (data scientist)]: Hey AI, I've got this "stable_employment" dataset, and I'm trying to figure out if there's any connection between credit history, loan duration, and employment status. Can you lend me a hand? 

[YOU (AI assistant)]: Absolutely! First, let's make sure all the columns are numeric. Do you need help with that? 

[USER (data scientist)]: Yeah, how do I go about doing that? Please generate a DataFrame by applying label encoding to non-numeric columns of the stable_employment dataset.

[YOU (AI assistant)]: No problem! We can use label encoding to convert non-numeric columns to numeric values. Here's how you can do it :
'''
import pandas as pd
from sklearn.preprocessing import LabelEncoder    
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
# Create a copy of the stable_employment DataFrame   
stable_employment_encoded = stable_employment.copy() 

# Initialize the label encoder   
encoder = LabelEncoder()

# Convert non-numeric columns to numeric values using label encoding   
for column in stable_employment_encoded.columns:   
    if stable_employment_encoded[column].dtype == 'object':   
        stable_employment_encoded[column] = encoder.fit_transform(stable_employment_encoded[column]) 
  
print(f"stable_employment_encoded: {stable_employment_encoded}")

'''

[USER (data scientist)]: Cool, now I want to see how these variables are correlated. Please generate a DataFrame showing the correlation matrix of the encoded dataset.

[YOU (AI assistant)]: Sure thing! You can calculate the correlation matrix for the encoded dataset like this :
'''
import pandas as pd   
from sklearn.preprocessing import LabelEncoder
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
# Calculate the correlation matrix for the encoded dataset   
correlation_matrix = stable_employment_encoded.corr()   

# Print the correlation matrix   
print("Correlation matrix:\n", correlation_matrix)

'''

[USER (data scientist)]: Thanks! What about the relationship between credit history and loan duration? Please generate a Series showing the average loan duration for each credit history category in the dataset.

[YOU (AI assistant)]: To check that out, you can use this code :
'''
import pandas as pd   
from sklearn.preprocessing import LabelEncoder
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
# Analyze the relationships between variables   
credit_history_duration_relationship = stable_employment.groupby('credit_history')['duration'].mean()   
  
# Print the relationship   
print("\nRelationship between credit history and loan duration:\n", credit_history_duration_relationship)

'''

[USER (data scientist)]: Could you provide information about the employment status and loan duration? Additionally, could you clarify how a client's checking status affects their creditworthiness?

A. Insignificant predictor
B. Moderate predictor
C. Unreliable predictor
D. Significant predictor
E. None of above

[YOU (AI assistant)]: