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, let's start with the first question. I will generate the code to check for missing values in the credit_customers dataset and propose methods for handling them. Specifically, you can generate a dataframe and a pickle file to analyze and store the count of missing values per column in the 'credit_customers' dataset.

[YOU (AI assistant)]: Sure, I can help you :
'''
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)]: Great, since there are no missing values, we can move on to the second question. I will generate the code to identify categorical variables in the credit_customers dataset and suggest encoding methods for converting them to numerical variables. Specifically, you can generate a dataframe and a pickle file to visualize and store the process of identifying nominal categorical variables in the 'credit_customers' dataset, followed by applying one-hot encoding to these variables.

[YOU (AI assistant)]: Sure, I can help you :
'''
import pandas as pd  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Identify categorical variables  
categorical_columns = credit_customers.select_dtypes(include=['object']).columns  
  
# 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)  
  
# Suggest encoding methods  
print("Suggested encoding methods:")  
print("1. One-hot encoding for nominal categorical variables:")  
for col in nominal_categorical_columns:  
    print("   -", col)  
  
# Example code for one-hot encoding  
credit_customers_encoded = pd.get_dummies(credit_customers, columns=nominal_categorical_columns)  
print("\nData after one-hot encoding:\n", credit_customers_encoded.head())

'''

[USER (data scientist)]: Now that we've taken care of encoding the categorical variables, let's jump to question three. Can you create some code to figure out if we need to normalize the credit_customers dataset? Just see if the value goes beyond 1, and if it does, we should normalize it and generate the top 5 rows of normalized dataframe. Oh, and don't forget to recommend a normalization method if it's needed.

My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import numpy as np
from sklearn.preprocessing import StandardScaler 
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE]  
</code1>
# YOUR SOLUTION END

print("Data after Standard Scaling:\n", credit_customers_normalized.head())  

# save data
pickle.dump(credit_customers_normalized.head(),open("./pred_result/credit_customers_normalized_head.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you:
'''
import pandas as pd  
import numpy as np
from sklearn.preprocessing import StandardScaler 
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
