Wine Clustering Analysis
GMM vs K-Means comparison achieving 0.898 ARI with automatic cluster selection and uncertainty quantification
Technology Stack
Scikit-learnGMMK-MeansDBSCANPCAt-SNEPandasMatplotlibSeaborn
ML Frameworks
Deep Learning
NLP
Deployment
ROI
Product segmentation automation
Accuracy
0.898 ARI (K-Means)
Key Performance Indicators
GMM ARI
0.880Silhouette Score
0.285Chemical Features
13Business Impact Summary
| Return on Investment | Product segmentation automation |
| Model Accuracy | 0.898 ARI (K-Means) |
| GMM ARI | 0.880 |
| Silhouette Score | 0.285 |
| Chemical Features | 13 |
Overview
Production-grade clustering analysis comparing Gaussian Mixture Models and K-Means on UCI Wine Dataset. Features automatic cluster selection (Elbow, Silhouette, AIC, BIC), uncertainty quantification, and comprehensive visualizations.