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 with analyzing a dataset called "credit_customers.csv". Can you help me load it using pandas? 

[YOU (AI assistant)]: Sure, I can help you with that. Here's the code to load the dataset using pandas: 
'''
import pandas as pd 
import pickle

# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Thanks! Now, I want to check if there are any missing values in the dataset. Can you help me with that? Please generate a Series that shows the number of missing values in each column of the dataset.

[YOU (AI assistant)]: Of course! You can use the following code to check for missing values in the dataset :
'''
import pandas as pd  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Check for missing values  
missing_values = credit_customers.isnull().sum()  

print("Missing values per column:\n", missing_values)

'''

[USER (data scientist)]: Hey Tapilot, I've got this dataset called "credit_customers.csv" and I need to preprocess it for a machine learning model. Can you help me load it using pandas? 

[YOU (AI assistant)]: Absolutely! Here's the code to load your dataset using pandas: 
'''
import pandas as pd 
import pickle 
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Awesome, thanks! Now, I need to figure out which variables are categorical. Can you help me with that? Please generate a Series listing the categorical columns in the dataset.

[YOU (AI assistant)]: Of course! Here's the code to identify the categorical variables in your dataset :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
categorical_columns = credit_customers.select_dtypes(include=['object']).columns

print("categorical_columns:\n", categorical_columns)    

'''

[USER (data scientist)]: Great, that's what I needed! Now, I want to make a list of nominal categorical columns from those variables. Can you help me with that? Please generate a list of nominal categorical columns from the dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to create a list of nominal categorical columns :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Initialize an empty list to store nominal categorical columns   
nominal_categorical_columns = []   

# Iterate through the categorical columns and append them to the nominal_categorical_columns list   
for col in categorical_columns:   
    nominal_categorical_columns.append(col)   

print("nominal_categorical_columns:\n", nominal_categorical_columns)    

'''

[USER (data scientist)]: Thanks a bunch! Now, I'm wondering what encoding methods I should use for these nominal categorical variables. Any suggestions? Please generate a Series listing the nominal categorical columns suitable for one-hot encoding in the dataset.

[YOU (AI assistant)]: Absolutely! I suggest using one-hot encoding for nominal categorical variables. Here's the code to display the suggested encoding method :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Suggest encoding methods   
print("Suggested encoding methods:")   
print("1. One-hot encoding for nominal categorical variables:")   
for col in nominal_categorical_columns:   
    print("   -", col)   

print("nominal_categorical_columns:\n", nominal_categorical_columns)    

'''

[USER (data scientist)]: Perfect! Now, let's go ahead and apply one-hot encoding to those variables. Can you help me with that? Please generate a DataFrame by encoding the nominal categorical columns of the dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to apply one-hot encoding to the nominal categorical variables :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
credit_customers_encoded = pd.get_dummies(credit_customers, columns=nominal_categorical_columns)  

print("credit_customers_encoded:\n", credit_customers_encoded)    

'''

[USER (data scientist)]: Hey Tapilot, I need some help with preprocessing a dataset. Can you give me a hand? 

[YOU (AI assistant)]: Of course! I'd be happy to help. What's the name of the dataset you're working with? 

[USER (data scientist)]: It's called "credit_customers.csv". Let's start by loading it using pandas. 

[YOU (AI assistant)]: Sure thing! Here's how you can load the dataset using pandas: 
'''
import pandas as pd 
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Awesome, now I'd like to know which columns are numerical. Please generate a Series containing the names of numerical columns in the dataset.

[YOU (AI assistant)]: No problem! You can use the 'select_dtypes()' function to identify the numerical columns. Here's how :
'''
import pandas as pd  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Identify numerical variables  
numerical_columns = credit_customers.select_dtypes(include=['number']).columns 
  
print("numerical_columns:\n", numerical_columns)

'''

[USER (data scientist)]: Cool, now let's calculate the range for each of those numerical columns. Please generate a dictionary that contains the range of values for each numerical column in the dataset.

[YOU (AI assistant)]: You got it! You can use a for loop to iterate through the numerical columns and calculate the range. Here's the code :
'''
import pandas as pd  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
ranges = {}   
for col in numerical_columns:   
    ranges[col] = credit_customers[col].max() - credit_customers[col].min()  
  
print("ranges:\n", ranges)

'''

[USER (data scientist)]: Thanks! Now, how can I check if I need to normalize this dataset? please note that if the value is more than 1, then it's needed to be normalized. Please generate a boolean value indicating if normalization is needed for the dataset.

[YOU (AI assistant)]: To check if normalization is necessary, you can see if any of the ranges are greater than 1. Here's how you can do that :
'''
import pandas as pd  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Check if normalization is necessary   
normalization_needed = False   
for col, range_ in ranges.items():   
    if range_ > 1:   
        normalization_needed = True   
        break 
  
print("normalization_needed:\n", normalization_needed)

'''

[USER (data scientist)]: And if I do need to normalize it, can you recommend a normalization method?

A. Min-Max
B. No Need
C. Standard Scaling
D. None of Above

[YOU (AI assistant)]: 