Customer Segmentation using K-Means Clustering

Project Overview This project focuses on customer segmentation using K-Means clustering. The goal is to group customers of a retail store based on their age, annual income, and spending score to identify different purchasing patterns and customer types. The project uses the Mall Customers dataset and applies the K-Means algorithm to form meaningful clusters. It includes visualization of clusters in 2D and 3D, evaluation using WCSS and Silhouette Score, and interpretation of customer behavior across segments.

Steps Involved

Data loading and exploration of the Mall Customers dataset Selection of important features (Age, Annual Income, Spending Score) Application of the K-Means clustering algorithm Determination of the optimal number of clusters using the Elbow Method Evaluation using WCSS and Silhouette Score Visualization of clusters in both 2D and 3D views Technologies Used

Python Pandas NumPy Scikit-Learn Matplotlib Seaborn Output Segmented customers into meaningful clusters Plotted 2D and 3D visualizations of customer groups Calculated clustering metrics for evaluation Learning Outcome Through this task, I learned how to: Apply the K-Means clustering algorithm for unsupervised learning Determine the optimal number of clusters Visualize and interpret customer segments effectively