K_mean Cluster

Dataset Description The dataset contains 10,000 customer records collected from a hypothetical retail company. Each record represents an individual customer and includes demographic characteristics, purchasing behavior, and loyalty-related information. The dataset is intended for educational purposes and provides a realistic scenario for applying unsupervised learning techniques, particularly the K-Means clustering algorithm. By analyzing these variables, students can discover natural customer segments, evaluate clustering performance, and develop data-driven marketing strategies. Dataset Variables Variable Description CustomerID Unique identifier assigned to each customer. Annual Income Annual customer income, expressed in thousands of U.S. dollars (USD). SpendingScore A numerical score (1–100) representing the customer's spending behavior, where higher values indicate greater purchasing activity. Age Customer age in years. Online Purchases Total number of online purchases made by the customer during the observation period. Store Visits Number of visits made to physical retail stores. LoyaltyScore A score ranging from 0 to 100 that reflects the customer's loyalty based on purchase frequency and engagement. RegionCode Numerical code identifying the customer's geographic region (1–5). This dataset is suitable for performing clustering analysis, data visualization, feature scaling, cluster evaluation, and customer segmentation using machine learning techniques. It can also be extended for exploring dimensionality reduction, anomaly detection, and other unsupervised learning methods.