- Built an end-to-end ML pipeline on the 178-sample, 13-feature Wine dataset using train/test splitting and StandardScaler normalization to prepare data for PCA and classification.
- Interpreted principal components via loading heatmaps and biplots, identifying Flavanoids and OD280 as top PC1 drivers and Color Intensity and Alcohol as top PC2 drivers.
- Benchmarked Logistic Regression, SVM (RBF), and KNN (k=5) on PCA-reduced features, achieving 97.2% test accuracy and ˜97% 10-fold CV mean accuracy across all three models.
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Wine Classification
Wine classifier pipeline using Logistic Regression
- Python
- Machine Learning
- Logistic Regression
- PCA