This project applies unsupervised machine learning techniques to perform customer segmentation using the K-Means clustering algorithm. We use the well-known Mall Customers Dataset, which contains information about customers such as Customer ID, Gender, Age, Annual Income (k$), and Spending Score (1-100). The main objective is to group customers into distinct clusters based on their purchasing behavior and income levels—insights that can be valuable for business marketing strategies and personalized services.
Key Steps: Preprocessing & scaling
Elbow Method for optimal clusters
K-Means clustering
Cluster visualization using PCA
Cluster interpretation
Tools: Python, Pandas, Scikit-learn, Matplotlib, Seaborn