Project Overview

This project applies K-Means clustering and Principal Component Analysis (PCA) to segment customers based on their purchasing behavior and demographic characteristics. The goal is to identify distinct customer groups that can help businesses understand their audience better and tailor marketing strategies accordingly.

Techniques Used

• K-Means Clustering: A popular unsupervised learning algorithm for grouping similar customers.
• Principal Component Analysis (PCA): Used to reduce dimensionality while retaining essential variance in the data.
• Data Preprocessing: Handling missing values, scaling data, and preparing it for analysis.
• Visualization: Cluster representation using 2D and 3D plots to interpret the results effectively.