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 proper functions from decision_company like this:
'''
import pandas as pd 
import pickle
from decision_company import read_csv_file
 
# Load the dataset using the custom function 
credit_customers = read_csv_file("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
from decision_company import read_csv_file, is_null, sum_up
  
# Load the dataset using the custom function  
credit_customers = read_csv_file("credit_customers.csv")  
  
# Check for missing values using the custom functions  
missing_values = is_null(credit_customers)  
print("Missing values per column:\n", sum_up(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 proper functions from decision_company like this:
'''
import pandas as pd 
import pickle
from decision_company import read_csv_file

# Load the dataset 
credit_customers = read_csv_file("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
from decision_company import read_csv_file, select_data_types, get_columns

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# Identify categorical variables  
categorical_columns = get_columns(select_data_types(credit_customers,['object']))  

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 
from decision_company import read_csv_file

# Load the dataset  
credit_customers = read_csv_file("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 
from decision_company import read_csv_file

# Load the dataset  
credit_customers = read_csv_file("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 
from decision_company import read_csv_file, get_dummies
  
# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
credit_customers_encoded = 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 proper functions from decision_company like this:
'''
import pandas as pd 
import pickle
from decision_company import read_csv_file

# Load the dataset 
credit_customers = read_csv_file("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 proper functions from decision_company to identify the numerical columns. Here's how :
'''
import pandas as pd  
import pickle
from decision_company import read_csv_file, select_data_types, get_columns

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# Identify numerical variables  
numerical_columns = get_columns(select_data_types(credit_customers, ['number']))   
  
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
from decision_company import read_csv_file, get_min_value, get_max

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# Calculate the range of each numerical column  
ranges = {}  
for col in numerical_columns:  
    col_data = credit_customers[col] 
    ranges[col] = get_max(col_data) - get_min_value(col_data)  
  
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
from decision_company import read_csv_file

# Load the dataset  
credit_customers = read_csv_file("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, how can I use Standard Scaling for that? Please note that Standard Scaling is commonly used and will be effective in this case, where each feature is centered around 0 with a standard deviation of 1. Please generate a DataFrame of the normalized dataset using Standard Scaling.

[YOU (AI assistant)]: If normalization is needed, you can use proper functions from decision_company. Here's the code to do that :
'''
import pandas as pd  
from sklearn.preprocessing import StandardScaler   
import pickle
from decision_company import read_csv_file, df_copy, get_first_n_rows, create_standard_scaler, fit_transform_standard_scaler, col_assign_val

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# It's true:  
# Normalize the data using Standard Scaling  
if normalization_needed: 
    scaler = create_standard_scaler()  
    credit_customers_normalized = df_copy(credit_customers)  
    col_assign_val(credit_customers_normalized, numerical_columns, fit_transform_standard_scaler(scaler, credit_customers[numerical_columns]))
  
    print("Data after Standard Scaling:\n", get_first_n_rows(credit_customers_normalized))  

else:
    print("Normalization is not needed.")

'''

[USER (data scientist)]: Hi Tapilot, I have a dataset called "credit_customers" and I want to find the optimal number of customer segments based on the important columns: credit_history, age, employment, credit_amount, and savings_status. Can you help me extract these columns? Please generate a DataFrame containing selected columns from the dataset for clustering purposes.

[YOU (AI assistant)]: Sure, I can help you with that. Here's the code to extract the important columns :
'''
import pandas as pd  
import pickle 
from decision_company import read_csv_file, col_copy
  
# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# Extract the important columns  
important_columns = ['credit_history', 'age', 'employment', 'credit_amount', 'savings_status']  
data_for_clustering = col_copy(credit_customers, important_columns)  

print("data_for_clustering:\n", data_for_clustering)    

'''

[USER (data scientist)]: Thanks! Now, I need to preprocess the data. Can you help me apply Label Encoding to 'savings_status' and 'employment' columns? Please generate a DataFrame of the encoded dataset for clustering purposes.

[YOU (AI assistant)]: Of course! Here's the code to apply Label Encoding to 'savings_status' and 'employment' :
'''
import pandas as pd  
from sklearn.preprocessing import LabelEncoder 
import pickle 
from decision_company import read_csv_file, create_label_encoder, fit_transform_label_encoder, col_assign_val
  
# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# Apply Label Encoding to 'savings_status' and 'employment'  
le_savings_status = create_label_encoder()  
le_employment = create_label_encoder()  
  
col_assign_val(data_for_clustering, 'savings_status', fit_transform_label_encoder(le_savings_status, data_for_clustering['savings_status']))  
col_assign_val(data_for_clustering, 'employment', fit_transform_label_encoder(le_employment, data_for_clustering['employment']))

print("data_for_clustering:\n", data_for_clustering)    

'''

[USER (data scientist)]: Great, now I need to apply One-Hot Encoding to the 'credit_history' column. Can you help me with that? Please generate a DataFrame with one-hot encoded features for clustering.

My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
import pickle
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first

# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE] 
</code1>
# YOUR SOLUTION END

print("data_for_clustering:\n", data_for_clustering) 

# save data
pickle.dump(data_for_clustering,open("./pred_result/data_for_clustering.pkl","wb"))

---END CODE TEMPLATE---

Please note that you have to generate the WHOLE python code instead of code segments based on the code snippet using Pandas library 2.0.3 version and Matplotlib library 3.7.4 version. You must keep all comments in code snippet unchanged.

[YOU (AI assistant)]: Sure, here's the code to apply One-Hot Encoding to the 'credit_history' column:
'''
import pandas as pd 
import pickle
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first

# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
