Advantages of Silhouette Analysis:

Optimal Cluster Number: Helps determine the optimal number of clusters for a given dataset by evaluating the quality of the clustering. Cluster Validity: Provides a measure of how well-separated the clusters are and how well-defined each data point is within its assigned cluster. Model Selection: Guides the selection of the most appropriate number of clusters for K-Means and other clustering algorithms.

Key Features:

Data Loading and Preprocessing: Includes data loading, handling missing values, and feature scaling.

K-Means Clustering: Implements K-Means clustering with varying numbers of clusters.

Silhouette Analysis: Calculates the Silhouette score for different cluster numbers to identify the optimal number of clusters.

Visualization: Visualizes the clusters using scatter plots and explores the characteristics of each customer segment.