[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.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
from sklearn.cluster import KMeans 
from sklearn.metrics import silhouette_score 
import matplotlib.pyplot as plt
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

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

# YOUR SOLUTION BEGIN:
<code2>
[COMPLETE 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'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
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:
