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)]: Alright, we've encoded the categorical variables, so let's tackle question three. Could you whip up some code to figure out if we need to normalize the credit_customers dataset? Just check if the value is over 1, and if it is, we should normalize it and generate the top 5 rows of normalized dataframe. By the way, Standard Scaling works well for this - it centers each feature around 0 with a standard deviation of 1, which is pretty popular and effective. Specifically, you can generate a dataframe showing the first few rows of the normalized 'credit_customers' dataset, after applying standard scaling to its numerical columns.

[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")  
  
# Identify numerical variables  
numerical_columns = credit_customers.select_dtypes(include=['number']).columns  
  
# Calculate the range of each numerical column  
ranges = {}  
for col in numerical_columns:  
    ranges[col] = credit_customers[col].max() - credit_customers[col].min()  
  
# Check if normalization is necessary  
normalization_needed = False  
for col, range_ in ranges.items():  
    if range_ > 1:  
        normalization_needed = True  
        break  
  
# It's true:  
# Normalize the data using Standard Scaling  
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())

'''

[USER (data scientist)]: Great, we have normalized the data using Standard Scaling. Now, let's move on to the fourth question. Please generate the code to determine the optimal number of customer segments in the credit_customers dataset based on the important columns (credit_history, age, employment, credit_amount, and savings_status) using clustering evaluation metrics such as the silhouette score. Specifically, you can generate a plot (datatype: matplotlib figure) to visualize the silhouette scores for different numbers of clusters within the 'credit_customers' dataset, following data preprocessing and feature encoding.

[YOU (AI assistant)]: Sure, I can help you :
'''
import pandas as pd   
from sklearn.preprocessing import StandardScaler, LabelEncoder
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")  
  
# Extract the important columns
important_columns = ['credit_history', 'age', 'employment', 'credit_amount', 'savings_status']
data_for_clustering = credit_customers[important_columns].copy()

# Apply Label Encoding to 'savings_status' and 'employment'
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'])

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

# Normalize the data using Standard Scaling
scaler = StandardScaler()
data_for_clustering_scaled = scaler.fit_transform(data_for_clustering)
# Determine the sum of squared distances for different number of clusters
# Calculate silhouette scores for different number of clusters
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)]: Let's move on to the fifth question. I will generate the code to use clustering techniques like K-means or hierarchical clustering to group customers in the credit_customers dataset into different segments based on the optimal number of clusters you identified in the previous step. You should return Tuple containing the clustering algorithm used (string), parameters chosen (dictionary), and resulting customer segments (DataFrame with cluster labels). Specifically, you can generate a DataFrame showcasing the customer segments created by applying K-means clustering with 4 clusters on the 'credit_customers' dataset

[YOU (AI assistant)]: Sure, I can help you :
'''
import pandas as pd  
from sklearn.preprocessing import StandardScaler, LabelEncoder  
from sklearn.cluster import KMeans  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Extract the important columns  
important_columns = ['credit_history', 'age', 'employment', 'credit_amount', 'savings_status']  
data_for_clustering = credit_customers[important_columns].copy()  
  
# Preprocess the data  
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)  
  
# Normalize the data  
data_for_clustering_scaled = StandardScaler().fit_transform(data_for_clustering)  
  
# Perform K-means clustering  
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  
  
# Return the clustering algorithm used, parameters chosen, and resulting customer segments  
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)]: I will generate the code to identify customer segments in the credit_customers dataset that have a good credit history, are in the age group of 25-45, and have stable employment status. The result type will be a list of target customer segments meeting the specified criteria. Specifically, you can generate a list of target customer segments, which are determined by applying K-means clustering with 4 clusters on the preprocessed credit_customers DataFrame.

[YOU (AI assistant)]: Sure, I can help you :
'''
import pandas as pd  
from sklearn.preprocessing import StandardScaler, LabelEncoder  
from sklearn.cluster import KMeans  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Extract the important columns  
important_columns = ['credit_history', 'age', 'employment', 'credit_amount', 'savings_status']  
data_for_clustering = credit_customers[important_columns].copy()  
  
# Preprocess the data  
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)  
  
# Normalize the data  
data_for_clustering_scaled = StandardScaler().fit_transform(data_for_clustering)  
  
# Perform K-means clustering  
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  
  
# Filter the dataset based on the specified criteria    
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'])    
  
# Combine the filters and apply them to the dataset    
target_customers = credit_customers[good_credit_history & age_group & stable_employment]    
  
# Extract the target customer segments    
target_customer_segments = target_customers['cluster'].unique().tolist()    
  
# Return the list of target customer segments     
print("target_customer_segments:\n", target_customer_segments)

'''

[USER (data scientist)]: I'll cook up some code to find other customer segments in the credit_customers dataset that might be keen on promotions and financing options. We'll end up with a list of extra customer segments that could be responsive to those deals. Just keep in mind that 'existing paid', 'all paid', and 'no credits/all paid' are already considered potential segments for responsiveness to promotions and financing options. Specifically, you can generate a list of additional customer segments by preprocessing and clustering a DataFrame from the 'credit_customers.csv' dataset, applying various encoding and scaling techniques, and finally identifying specific segments based on credit history and credit amount criteria.

[YOU (AI assistant)]: Sure, I can help you :
'''
import pandas as pd  
import numpy as np
from sklearn.preprocessing import StandardScaler, LabelEncoder  
from sklearn.cluster import KMeans  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Extract the important columns  
important_columns = ['credit_history', 'age', 'employment', 'credit_amount', 'savings_status']  
data_for_clustering = credit_customers[important_columns].copy()  
  
# 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)  
  
# Normalize the data using Standard Scaling  
data_for_clustering_scaled = StandardScaler().fit_transform(data_for_clustering)  
  
# 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  
     
# 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()  
  
# Exclude the target customer segments identified in the previous step  
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]  
  
# Return the list of additional customer segments     
print("additional_customer_segments:\n", additional_customer_segments)

'''

[USER (data scientist)]: I will create a code to outline the main features of each target customer group in the credit_customers dataset. The output will be a summary of these main features for each group. Since you've already worked on this, could you please answer the following related question: What is the savings status for the majority (60.73%) of customers in this segment?

A. less than 100
B. less than 200
C. greater than 100
D. equal to 100
E. None of above

[YOU (AI assistant)]: