"Wine Quality Clustering: Finding Hidden Patterns in Wine Chemistry"#10
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tare-degi wants to merge 1 commit intosoftwareWCU:mainfrom
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"Wine Quality Clustering: Finding Hidden Patterns in Wine Chemistry"#10tare-degi wants to merge 1 commit intosoftwareWCU:mainfrom
tare-degi wants to merge 1 commit intosoftwareWCU:mainfrom
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name = tarekegn |
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This project uses unsupervised machine learning to analyze the "chemical DNA" of thousands of wine samples. By ignoring labels like "red" or "white" and focusing purely on measurements like acidity, alcohol, and sugar, the model discovers natural groupings within the data. Through K-Means and Hierarchical Clustering, we reveal how specific chemical fingerprints correlate with wine types and overall quality ratings, proving that data alone can distinguish wine varieties.