Data analysis of a customer personality segmentation and created business decisions accordingly.
This project applies unsupervised machine learning to segment customers into distinct groups based on demographic and behavioral data. Using K-Means clustering, the analysis explores optimal cluster selection through the Elbow Method and Silhouette Score (with Yellowbrick visualizations), followed by cluster profiling with boxplots, barplots, and summary statistics. Each cluster is translated into actionable business personas, with tailored marketing and product recommendations.
- 🔍 Data preprocessing & scaling for clean clustering results
- 📊 Cluster evaluation with Elbow Method & Silhouette Score
- 🎨 Visualization of cluster distributions (boxplots, barplots, heatmaps)
- 🧩 Customer profiling: translating clusters into real-world personas
- 💼 Business recommendations to support data-driven marketing strategies
This project demonstrates how unsupervised learning can uncover hidden patterns in customer behavior and guide smarter business decisions.