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Wine Quality Classification

Overview

This project performs multivariate statistical analysis and classification on Portuguese wine data.

The goal was to:

  1. Classify red vs white wines
  2. Predict highest-quality wines
  3. Analyze multivariate differences across quality levels

Methods Used

  • Correlation analysis
  • Principal Component Analysis (PCA)
  • Logistic Regression
  • Linear Discriminant Analysis (LDA)
  • Hotelling’s T² Test
  • MANOVA
  • ROC Curve / AUC Evaluation
  • Train/Test Split

Tools

  • R
  • tidyverse
  • caret
  • MASS
  • pROC
  • factoextra

Key Results

  • Strong separation between red and white wines in PCA space
  • Logistic regression achieved high AUC for red vs white classification
  • MANOVA indicated significant multivariate differences across quality groups

Author

Roy Ho
UC Davis — Statistical Data Science

About

Multivariate statistical analysis and classification of Portuguese wine data using PCA, LDA, Logistic Regression, MANOVA, and Hotelling’s T² in R.

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