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)]: Hi, I have a dataset called "credit_customers.csv" and I would like to analyze the distribution of credit_history, employment, age, and credit_amount. Can you help me with that? 

[YOU (AI assistant)]: Sure, I can help you with that. First, let's start by loading the dataset using pandas. Here's the code to do that: 
'''
import pandas as pd 

# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Great, now I want to create histograms for each of the mentioned variables. Please generate and display a set of subplots including bar charts for 'Credit History' and 'Employment' distributions, and histograms for 'Age' and 'Credit Amount' distributions in the 'credit_customers' dataset.

[YOU (AI assistant)]: Alright, let's create histograms for credit_history, employment, age, and credit_amount using matplotlib. Here's the code to do that :
'''
import pandas as pd   
import matplotlib.pyplot as plt

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
# Plot histograms   
fig, axes = plt.subplots(2, 2, figsize=(12, 8))   

credit_customers['credit_history'].value_counts().plot(kind='bar', ax=axes[0, 0], title='Credit History')   
credit_customers['employment'].value_counts().plot(kind='bar', ax=axes[0, 1], title='Employment')   
credit_customers['age'].plot(kind='hist', bins=20, ax=axes[1, 0], title='Age')   
credit_customers['credit_amount'].plot(kind='hist', bins=20, ax=axes[1, 1], title='Credit Amount')   

plt.tight_layout()   
plt.savefig('ref_result/subplots.png')
plt.show()

'''

[USER (data scientist)]: Hey, I need some help analyzing a dataset of credit customers. I want to find out how loan approval rates vary across different factors like credit history categories, employment statuses, age groups (specifically 40-59), and credit amount ranges. Can you help me with that? 

[YOU (AI assistant)]: Of course! Let's start by loading your dataset. Do you have a CSV file with the data? 

[USER (data scientist)]: Yeah, it's called "credit_customers.csv". 

[YOU (AI assistant)]: Awesome! Let's load the dataset using pandas. Here's the code for that: 
'''
import pandas as pd 

# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Cool, thanks! Now, let's kick things off by figuring out the approval rates, you know, when the 'class' column is marked as 'good', for each credit history category. Please calculate and display a Series of the approval rates for different credit histories in the 'credit_customers' dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to calculate approval rates for credit history categories :
'''
import pandas as pd   
import pickle
   
# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv")   
  
approval_rates_credit_history = credit_customers.groupby('credit_history')['class'].value_counts(normalize=True).unstack().fillna(0)['good']   

print(approval_rates_credit_history)

'''

[USER (data scientist)]: Nice! Now, let's do the same for employment statuses and again approval refers to column `class` is `good`. Please calculate and display a Series of the approval rates for different employment statuses in the 'credit_customers' dataset.

[YOU (AI assistant)]: You got it! Here's the code to calculate approval rates for employment statuses :
'''
import pandas as pd   
import pickle
   
# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv")   
  
approval_rates_employment = credit_customers.groupby('employment')['class'].value_counts(normalize=True).unstack().fillna(0)['good']      

print(approval_rates_employment)

'''

[USER (data scientist)]: Thanks! Now, how about the approval rates for the age group 40-59? Please calculate and display a Series of the approval rates for the middle-aged group (ages 41 to 58) in the 'credit_customers' dataset.

[YOU (AI assistant)]: No problem! Here's the code to calculate approval rates for the age group 40-59 :
'''
import pandas as pd   
import pickle
   
# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv")   
  
approval_rates_middle_age = credit_customers[(credit_customers['age'] > 40) & (credit_customers['age'] < 59)].groupby('age')['class'].value_counts(normalize=True).unstack().fillna(0)['good'] 

print(approval_rates_middle_age)

'''

[USER (data scientist)]: Awesome! Finally, let's figure out the approval rates for various credit amount ranges. Just remember, we'll be using bins of 2000 for the credit amounts. Please categorize the 'credit_amount' into specified ranges in the 'credit_customers' dataset, calculate and display a Series of the approval rates for these credit amount ranges focusing on 'good' class outcomes.

[YOU (AI assistant)]: Sure thing! Here's the code to calculate approval rates for credit amount ranges :
'''
import pandas as pd   
import pickle
   
# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv")   
  
credit_customers['credit_amount_range'] = pd.cut(credit_customers['credit_amount'], bins=[0, 2000, 4000, 6000, 8000, 10000, 12000, 14000, 16000, 18000, 20000], include_lowest=True)   

approval_rates_credit_amount = credit_customers.groupby('credit_amount_range')['class'].value_counts(normalize=True).unstack().fillna(0)['good'] 

print(approval_rates_credit_amount)

'''

[USER (data scientist)]: Can the specific age group impact the likelihood of loan approval.

A. can't
B. can
C. None of above

[YOU (AI assistant)]: