Customer Segmentation using K-Means Clustering (Unsupervised ML)

Project Overview

This project implements an Unsupervised Machine Learning model to categorize customers into distinct groups based on their Annual Income and Spending Score. By using clustering, businesses can identify target segments and optimize their marketing strategies.

Key Features

• Data Normalization: Utilized StandardScaler to ensure that all features are on the same scale, preventing bias during the clustering process.
• K-Means Algorithm: Applied the K-Means++ initialization method to identify 3 unique customer clusters.
• Centroid Analysis: Calculated and visualized the cluster centroids (center points) to represent the average behavior of each segment.

Tech Stack

• Language: Python
• Libraries: Scikit-Learn (ML), Pandas (Data), Matplotlib (Visualization), NumPy (Logic)
• Environment: Pydroid 3

Insights from Clusters

• Cluster 1: Budget-Conscious Customers.
• Cluster 2: High-Value Target Customers.
• Cluster 3: High-Income Low-Spenders.

Project Output

(Note: The 'X' markers represent the centroids of each customer segment)