Machine Learning • K-Means Clustering • K = 2
The graph shows the two K-Means centroids and the new customer's position.
Annual Income: 79.60 k$
Spending Score: 50.13
Higher-income customer group with an average spending score.
Annual Income: 37.29 k$
Spending Score: 50.29
Lower-income customer group with an average spending score.
This project uses the K-Means clustering algorithm to segment mall customers into two groups. The selected features are Annual Income (k$) and Spending Score (1-100). Since K = 2, the algorithm forms two customer groups based on similarity to their cluster centroids.