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

# Plot silhouette scores
plt.figure(figsize=(10, 6))

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

plt.savefig('pred_result/Silhouette_Scores.png') 
plt.show()

---END CODE TEMPLATE---

[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")  
  
# YOUR SOLUTION BEGIN:
