[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. 
My template of code snippet is:

---BEGIN CODE TEMPLATE---

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 
from decision_company import read_csv_file, col_copy, create_standard_scaler, fit_transform_standard_scaler, create_label_encoder, fit_transform_label_encoder, get_dummies, create_kmeans, fit_predict_kmeans, get_silhouette_score, show_plots, create_figure, set_plot_title, set_yaxis_label, set_plot_xlabel, save_plot, plot, grid

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

# Plot silhouette scores
create_figure(figsize=(10, 6))  

# YOUR SOLUTION BEGIN:
<code2>
[GENERATE YOUR CODE]
</code2>
# YOUR SOLUTION END

# save data
save_plot('pred_result/Silhouette_Scores.png') 
show_plots()

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you:

# MY SOLUTION BEGIN:
