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 Tapilot, I need your help to analyze the credit_customers dataset. Let's start with the first question. Specifically, you can generate a set of subplots (datatype: matplotlib Figure) to visualize the distribution of 'credit_history', 'employment', 'age', and 'credit_amount' within the 'credit_customers' dataframe.

[YOU (AI assistant)]:Sure, I can help you with that. To generate histograms for the specified columns, you can use the following code :
'''
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)]:Alright. Can you whip up some bar charts that show loan approval rates, you know, when the 'class' column is marked as 'good', for each credit history category, employment status, the middle-age group from 40 to 59 years old, and credit amount range? Don't forget, we're using bins of 2000 for the credit amounts. Specifically, you can generate a series of DataFrame visualizations to display the relationship between loan approval rates and various factors such as credit history, employment status, age group (specifically 40-59), and credit amount range in the 'credit_customers' dataset.

[YOU (AI assistant)]:Sure, I can help you with that. To generate bar charts for loan approval rates based on the specified columns and conditions, you can use the following code :
'''
import pandas as pd  
import matplotlib.pyplot as plt  
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Calculate approval rates  
approval_rates_credit_history = credit_customers.groupby('credit_history')['class'].value_counts(normalize=True).unstack().fillna(0)['good']  
approval_rates_employment = credit_customers.groupby('employment')['class'].value_counts(normalize=True).unstack().fillna(0)['good']  
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']  
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']  
  
# Plot bar charts  
fig, axes = plt.subplots(2, 2, figsize=(12, 8))  
  
approval_rates_credit_history.plot(kind='bar', ax=axes[0, 0], title='Approval Rates by Credit History')  
approval_rates_employment.plot(kind='bar', ax=axes[0, 1], title='Approval Rates by Employment Status')  
approval_rates_middle_age.plot(kind='bar', ax=axes[1, 0], title='Approval Rates for Middle-Age Group (40-59)')  
approval_rates_credit_amount.plot(kind='bar', ax=axes[1, 1], title='Approval Rates by Credit Amount Range')  
  
plt.tight_layout()  
plt.savefig('ref_result/bar_chart.png')
plt.show()  
'''

[USER (data scientist)]: Great! Please generate scatter plots showing the correlation between credit_history, employment, age, and credit_amount with loan approval rates (approval refers to column `class` is `good`). Specifically, you can generate scatter plots as Figure objects to visualize the relationships between loan approval rates ('class_binary') and various numerical features ('credit_history', 'employment', 'age', 'credit_amount') in the numerically encoded 'credit_customers_numerical' DataFrame.

[YOU (AI assistant)]: Sure, I can help you with that. :
'''
import pandas as pd  
import matplotlib.pyplot as plt  
import seaborn as sns  
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Convert 'class' column to binary values  
credit_customers['class_binary'] = credit_customers['class'].apply(lambda x: 1 if x == 'good' else 0)  
  
# Create a new DataFrame with numerical values for credit_history and employment  
credit_customers_numerical = credit_customers.copy()  
credit_customers_numerical['credit_history'] = credit_customers_numerical['credit_history'].astype('category').cat.codes  
credit_customers_numerical['employment'] = credit_customers_numerical['employment'].astype('category').cat.codes  
  
# Plot scatter plots  
fig, axes = plt.subplots(2, 2, figsize=(12, 8))  
  
sns.scatterplot(data=credit_customers_numerical, x='credit_history', y='class_binary', ax=axes[0, 0])  
axes[0, 0].set_title('Loan Approval Rates vs. Credit History')  
  
sns.scatterplot(data=credit_customers_numerical, x='employment', y='class_binary', ax=axes[0, 1])  
axes[0, 1].set_title('Loan Approval Rates vs. Employment Status')  
  
sns.scatterplot(data=credit_customers_numerical, x='age', y='class_binary', ax=axes[1, 0])  
axes[1, 0].set_title('Loan Approval Rates vs. Age')  
  
sns.scatterplot(data=credit_customers_numerical, x='credit_amount', y='class_binary', ax=axes[1, 1])  
axes[1, 1].set_title('Loan Approval Rates vs. Credit Amount')  
  
plt.tight_layout()  
plt.savefig('ref_result/scatterplot.png')
plt.show() 
'''

[USER (data scientist)]:Great! Please create pie charts that display loan approval rates, which is calculated as the proportion of "good" decisions in each customer segment, for the specified customer segments: middle-aged customers aged 40-59, those with a stable employment history, and those with a good or excellent credit history. Consider customers with a credit history of "existing paid," "no credits/all paid," or "all paid" as having good or excellent credit history.

And answer my question: What is the ratio of loan approval rates for customers without a stable employment history compared to those with a stable employment history?
A. 0.33 %
B. 0.25 %
C. 0.45 %
D. None of above

[YOU (AI assistant)]:
