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, 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 the 'StandardScaler' from the 'sklearn.preprocessing' module. Here's the code to do that :
'''
import pandas as pd  
from sklearn.preprocessing import StandardScaler   
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Normalize the data using Standard Scaling   
if normalization_needed:   
    scaler = StandardScaler()   
    credit_customers_normalized = credit_customers.copy()   
    credit_customers_normalized[numerical_columns] = scaler.fit_transform(credit_customers[numerical_columns])   
    print("Data after Standard Scaling:\n", credit_customers_normalized.head())   

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

if normalization_needed:   
    print("credit_customers_normalized:\n", credit_customers_normalized)

'''

[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 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
important_columns = ['credit_history', 'age', 'employment', 'credit_amount', 'savings_status'] 
data_for_clustering = credit_customers[important_columns].copy() 

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 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
le_savings_status = LabelEncoder() 
le_employment = LabelEncoder() 
data_for_clustering['savings_status'] = le_savings_status.fit_transform(data_for_clustering['savings_status']) 
data_for_clustering['employment'] = le_employment.fit_transform(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.

[YOU (AI assistant)]: Sure, here's the code to apply One-Hot Encoding to the 'credit_history' column :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
data_for_clustering = pd.get_dummies(data_for_clustering, columns=['credit_history'], drop_first=True) 

print("data_for_clustering:\n", data_for_clustering)    

'''

[USER (data scientist)]: Thanks! Now, I want to normalize the data using Standard Scaling. Can you help me with that? Please generate a numpy array of the scaled data for clustering.

[YOU (AI assistant)]: Absolutely! Here's the code to normalize the data using Standard Scaling :
'''
import pandas as pd  
from sklearn.preprocessing import StandardScaler
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
scaler = StandardScaler() 
data_for_clustering_scaled = scaler.fit_transform(data_for_clustering) 

print("data_for_clustering_scaled:\n", data_for_clustering_scaled)    

'''

[USER (data scientist)]: Now that the data is preprocessed, I want to determine the optimal number of clusters using the silhouette score. Can you help me with that? Please generate a plot displaying the silhouette scores for different numbers of clusters in the KMeans clustering of the dataset.

[YOU (AI assistant)]: Sure, I'll calculate the silhouette scores for different numbers of clusters and plot the results. Here's the code for that :
'''
import pandas as pd  
from sklearn.cluster import KMeans 
from sklearn.metrics import silhouette_score 
import matplotlib.pyplot as plt
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
silhouette_scores = [] 
for cluster_num in range(2, 15):  # Starting from 2 because silhouette score is not defined for 1 cluster 
    kmeans = KMeans(n_clusters=cluster_num, random_state=42) 
    cluster_labels = kmeans.fit_predict(data_for_clustering_scaled) 
    silhouette_avg = silhouette_score(data_for_clustering_scaled, cluster_labels) 
    silhouette_scores.append(silhouette_avg) 

# Plot silhouette scores 
plt.figure(figsize=(10, 6)) 
plt.plot(range(2, 15), silhouette_scores, marker='o', linestyle='--') 
plt.title('Silhouette Scores for Different Number of Clusters') 
plt.xlabel('Number of Clusters') 
plt.ylabel('Silhouette Score') 
plt.grid(True) 
plt.savefig('ref_result/Silhouette_Scores.png') 
plt.show() 
'''

[USER (data scientist)]: Hey Tapilot! I've got this dataset of credit customers, and I'm thinking of using clustering techniques like K-means or hierarchical clustering to group them into different segments. Can you lend me a hand? 

[YOU (AI assistant)]: Of course! I'd be happy to help. First things first, what's the name of the dataset file? 

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

[YOU (AI assistant)]: Got it! Let's start by loading the dataset using pandas: 
'''
import pandas as pd 
import pickle 
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
Now, which columns should we use for clustering? 

[USER (data scientist)]: Let's go with 'credit_history', 'age', 'employment', 'credit_amount', and 'savings_status'. Please generate a DataFrame containing selected columns for clustering purposes.

[YOU (AI assistant)]: Alrighty, let's extract those columns. And are there any categorical columns in there that need encoding? :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
important_columns = ['credit_history', 'age', 'employment', 'credit_amount', 'savings_status']   
data_for_clustering = credit_customers[important_columns].copy()   

print("data_for_clustering:\n", data_for_clustering)    

'''

[USER (data scientist)]: Yep, I'd like to encode 'savings_status' and 'employment' using LabelEncoder, and create dummy variables for 'credit_history'. Please generate a DataFrame of the processed data suitable for clustering, with label encoding and one-hot encoding applied.

[YOU (AI assistant)]: No problem! Let's encode those categorical variables. And should we normalize the data before clustering? :
'''
import pandas as pd  
from sklearn.preprocessing import LabelEncoder 
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
data_for_clustering['savings_status'] = LabelEncoder().fit_transform(data_for_clustering['savings_status'])   
data_for_clustering['employment'] = LabelEncoder().fit_transform(data_for_clustering['employment'])   
data_for_clustering = pd.get_dummies(data_for_clustering, columns=['credit_history'], drop_first=True)

print("data_for_clustering:\n", data_for_clustering)    

'''

[USER (data scientist)]: Yeah, that'd be great. Let's use StandardScaler for that. Please generate a NumPy array containing the scaled data for clustering.

[YOU (AI assistant)]: You got it! Let's normalize the data. Now, it's time to pick a clustering algorithm. Do you prefer K-means or hierarchical clustering? :
'''
import pandas as pd  
from sklearn.preprocessing import StandardScaler 
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

data_for_clustering_scaled = StandardScaler().fit_transform(data_for_clustering)  

print("data_for_clustering_scaled:\n", data_for_clustering_scaled)    

'''

[USER (data scientist)]: Let's go with K-means clustering. 

[YOU (AI assistant)]: Sounds good! How many clusters are you aiming for? 

[USER (data scientist)]: I think 4 clusters should do the trick. Please generate a NumPy array of cluster labels from the KMeans algorithm applied to the scaled dataset.

[YOU (AI assistant)]: Alright, let's perform K-means clustering with 4 clusters :
'''
import pandas as pd  
from sklearn.cluster import KMeans 
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

kmeans = KMeans(n_clusters=4, random_state=42)   
cluster_labels = kmeans.fit_predict(data_for_clustering_scaled) 

print("cluster_labels:\n", cluster_labels)    

'''

[USER (data scientist)]: Now, let's add the cluster labels back to the original dataset: Please generate a DataFrame with assigned cluster labels for each customer.

[YOU (AI assistant)]: Here's the code. And do you need final result for presentation? :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

credit_customers['cluster'] = cluster_labels 

print("credit_customers:\n", credit_customers)    

'''

[USER (data scientist)]: Sure, show me the final code snippet with the clustering algorithm, parameters, and resulting customer segments: Please generate a tuple containing the K-means clustering parameters and a subset of the dataset.

[YOU (AI assistant)]: Here's the code :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

result = ("K-means", {"n_clusters": 4, "random_state": 42}, credit_customers[['credit_history', 'age', 'employment', 'credit_amount', 'savings_status', 'cluster']])   

print("result:\n", result)    

'''

[USER (data scientist)]: Hey Tapilot! I've got this dataset of credit customers, and I'm trying to find customer segments with good credit history, ages between 25 and 45, and stable jobs. Can you help me out? 

[YOU (AI assistant)]: Absolutely! Let's start by loading your dataset. Is it in a CSV file? 

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

[YOU (AI assistant)]: Cool! To load the dataset, you can use pandas like this: 
'''
import pandas as pd 
import pickle 
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Nice, now I need to pick out some important columns. I'm thinking 'credit_history', 'age', 'employment', 'credit_amount', and 'savings_status'. How do I do that? Please generate a DataFrame containing selected columns for clustering analysis.

[YOU (AI assistant)]: You got it! Just use this code to extract those columns :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
important_columns = ['credit_history', 'age', 'employment', 'credit_amount', 'savings_status']   
data_for_clustering = credit_customers[important_columns].copy() 

print("data_for_clustering:\n", data_for_clustering)    

'''

[USER (data scientist)]: Thanks! Now, I've got to preprocess the data. Can you help me encode 'savings_status' and 'employment'? Please generate a DataFrame with encoded categorical columns for clustering purposes.

[YOU (AI assistant)]: Sure thing! You can encode those columns with LabelEncoder from sklearn like this :
'''
import pandas as pd  
from sklearn.preprocessing import LabelEncoder 
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
data_for_clustering['savings_status'] = LabelEncoder().fit_transform(data_for_clustering['savings_status'])   
data_for_clustering['employment'] = LabelEncoder().fit_transform(data_for_clustering['employment'])

print("data_for_clustering:\n", data_for_clustering)    

'''

[USER (data scientist)]: Awesome! Now, I need to make dummy variables for 'credit_history'. What's the best way to do that? Please generate a DataFrame with one-hot encoded columns for 'credit_history' in the dataset.

[YOU (AI assistant)]: No problem! You can create dummy variables using pandas' "get_dummies" function like this :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

data_for_clustering = pd.get_dummies(data_for_clustering, columns=['credit_history'], drop_first=True) 

print("data_for_clustering:\n", data_for_clustering)    

'''

[USER (data scientist)]: Sweet! Now, how about normalizing the data? Please generate a NumPy array of the standardized values for the clustering dataset.

[YOU (AI assistant)]: You can normalize the data using StandardScaler from sklearn. Here's how :
'''
import pandas as pd  
from sklearn.preprocessing import StandardScaler 
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

data_for_clustering_scaled = StandardScaler().fit_transform(data_for_clustering)

print("data_for_clustering_scaled:\n", data_for_clustering_scaled)    

'''

[USER (data scientist)]: I want to do K-means clustering with 4 clusters. Can you show me how? Please generate a NumPy array of cluster labels from K-means clustering on the scaled dataset.

[YOU (AI assistant)]: Of course! You can perform K-means clustering with 4 clusters using sklearn's KMeans class like this :
'''
import pandas as pd  
from sklearn.cluster import KMeans 
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

kmeans = KMeans(n_clusters=4, random_state=42)   
cluster_labels = kmeans.fit_predict(data_for_clustering_scaled) 

print("cluster_labels:\n", cluster_labels)    

'''

[USER (data scientist)]: Cool, now how do I add those cluster labels back to the original dataset? Please generate a DataFrame by adding cluster labels to the credit customers dataset.

[YOU (AI assistant)]: You can add the cluster labels to the original dataset with this code :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

credit_customers['cluster'] = cluster_labels 

print("credit_customers:\n", credit_customers)    

'''

[USER (data scientist)]: Alright, now I need to filter the dataset based on good credit history, age group, and stable employment. How do I do that? Please generate a DataFrame of customers with good credit history, age between 25 and 45, and stable employment.

[YOU (AI assistant)]: You can filter the dataset using the following code :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

good_credit_history = credit_customers['credit_history'].isin(['existing paid', 'all paid'])     
age_group = (credit_customers['age'] >= 25) & (credit_customers['age'] <= 45)     
stable_employment = credit_customers['employment'].isin(['>=7', '4<=X<7'])    
target_customers = credit_customers[good_credit_history & age_group & stable_employment] 

print("target_customers:\n", target_customers)    

'''

[USER (data scientist)]: Finally, how do I get the target customer segments? Please generate a list of unique customer segments from the target customers dataset.

[YOU (AI assistant)]: You can extract the target customer segments with this code :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

target_customer_segments = target_customers['cluster'].unique().tolist()

print("target_customer_segments:\n", target_customer_segments)    

'''

[USER (data scientist)]: Hey Tapilot! I've got this dataset of credit customers, and I'm trying to find customer segments that might be more responsive to promotions and financing options. Can you help me out? 

[YOU (AI assistant)]: Of course! Let's start by loading your dataset. What's the file name? 

[USER (data scientist)]: It's called "credit_customers.csv". 

[YOU (AI assistant)]: Alright, let's load it up: 
'''
import pandas as pd 
import pickle 
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
Which columns do you think are important for clustering the customers? 

[USER (data scientist)]: I'd say 'credit_history', 'age', 'employment', 'credit_amount', and 'savings_status'. Please generate a DataFrame containing selected columns for clustering analysis.

[YOU (AI assistant)]: Got it, let's extract those columns. Now, should we encode the categorical columns in any way? :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
important_columns = ['credit_history', 'age', 'employment', 'credit_amount', 'savings_status']   
data_for_clustering = credit_customers[important_columns].copy() 

print("data_for_clustering:\n", data_for_clustering)    

'''

[USER (data scientist)]: Yeah, let's use Label Encoding for 'savings_status' and 'employment', and One-Hot Encoding for 'credit_history'. Please generate a DataFrame that applies Label Encoding to 'savings_status' and 'employment', and One-Hot Encoding to 'credit_history'.

[YOU (AI assistant)]: Sounds good, let's apply the encodings. And do you want me to normalize the data? If so, which method should I use? :
'''
import pandas as pd  
from sklearn.preprocessing import LabelEncoder 
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Preprocess the data by applying Label Encoding to 'savings_status' and 'employment'   
data_for_clustering['savings_status'] = LabelEncoder().fit_transform(data_for_clustering['savings_status'])   
data_for_clustering['employment'] = LabelEncoder().fit_transform(data_for_clustering['employment'])   

# Apply One-Hot Encoding to 'credit_history'   
data_for_clustering = pd.get_dummies(data_for_clustering, columns=['credit_history'], drop_first=True)   

print("data_for_clustering:\n", data_for_clustering)    

'''

[USER (data scientist)]: Yeah, let's go with Standard Scaling. Please generate a NumPy array representing the scaled data for clustering.

[YOU (AI assistant)]: Alright, let's normalize the data using Standard Scaling. And how many clusters do you want to use for clustering? :
'''
import pandas as pd  
from sklearn.preprocessing import StandardScaler 
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

# Normalize the data using Standard Scaling   
data_for_clustering_scaled = StandardScaler().fit_transform(data_for_clustering)   

print("data_for_clustering_scaled:\n", data_for_clustering_scaled)    

'''

[USER (data scientist)]: Let's try 4 clusters. Please generate a DataFrame with added K-means clustering labels.

[YOU (AI assistant)]: Okay, let's perform K-means clustering with 4 clusters :
'''
import pandas as pd  
from sklearn.cluster import KMeans 
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

# Perform K-means clustering with 4 clusters   
kmeans = KMeans(n_clusters=4, random_state=42)   
cluster_labels = kmeans.fit_predict(data_for_clustering_scaled)   

# Add the cluster labels to the original dataset   
credit_customers['cluster'] = cluster_labels   

print("credit_customers:\n", credit_customers)    

'''

[USER (data scientist)]: I'm thinking we can filter customers with a good credit history and a high credit amount. What do you think? Please generate a list identifying unique customer segments with good credit history and high credit amounts.

[YOU (AI assistant)]: Great, let's filter those customers. And do you have any target customer segments that you want to exclude? :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

# Identify additional customer segments with potential for responsiveness to promotions and financing options   
# Criteria: customers with a good credit history and a high credit amount 
good_credit_history = credit_customers['credit_history'].isin(['no credits/all paid'])   
high_credit_amount = credit_customers['credit_amount'] > credit_customers['credit_amount'].quantile(0.75)   

# Combine the filters and apply them to the dataset   
potential_customers = credit_customers[good_credit_history & high_credit_amount]   

# Extract the additional customer segments   
additional_customer_segments = potential_customers['cluster'].unique().tolist() 

print("additional_customer_segments:\n", additional_customer_segments)    

'''

[USER (data scientist)]: Yeah, let's exclude segments 1 and 2. Please generate a list of customer segments that are not part of the target segments.

[YOU (AI assistant)]: Alright, let's exclude those segments and get the additional customer segments :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  

target_customer_segments = [1, 2]  # Replace this list with the target customer segments from the previous step   
additional_customer_segments = [segment for segment in additional_customer_segments if segment not in target_customer_segments] 

print("additional_customer_segments:\n", additional_customer_segments)    

'''

[USER (data scientist)]: Hey Tapilot! I've got this dataset of credit customers, and I want to analyze the key characteristics of each target customer segment. Can you help me load the dataset? 

[YOU (AI assistant)]: Absolutely! To load the dataset, just use this code: 
'''
import pandas as pd 
import pickle 
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Cool, now I want to grab some columns for clustering. Let's go with 'credit_history', 'age', 'employment', 'credit_amount', and 'savings_status'. Please generate a list of specific column names for analysis from the dataset.

[YOU (AI assistant)]: Sure thing! Here's how you can extract those columns :
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
important_columns = ['credit_history', 'age', 'employment', 'credit_amount', 'savings_status']   
data_for_clustering = credit_customers[important_columns].copy()   

print("important_columns:\n", important_columns)    

'''

[USER (data scientist)]: Awesome! Now, I need to preprocess the categorical columns. Can you apply Label Encoding to 'savings_status' and 'employment', and One-Hot Encoding to 'credit_history'? Please generate a DataFrame of the preprocessed dataset with Label Encoding applied to 'savings_status' and 'employment' columns and One-Hot Encoding to 'credit_history'.

My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
from sklearn.preprocessing import LabelEncoder 
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_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)]: Of course! Here's the code to preprocess the categorical columns:
'''
import pandas as pd 
from sklearn.preprocessing import LabelEncoder 
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
