Wine Quality And Food Pairing Dataset

This dataset provides a comprehensive view of wine by combining measurable chemistry with human tasting judgment. It brings together physicochemical measurements from red wine samples (acidity, sugar, sulphates, alcohol, pH, sulfur compounds, density, etc.) alongside expert quality scores, and complements that with a rich catalogue of real-world wine–food pairing records (wine type/category food item/cuisine, numeric pairing_quality, text quality_label, and short description).

Because it couples laboratory-style features with culinary and semantic data, the collection is uniquely positioned for both rigorous scientific inquiry and creative product work. Data scientists can build reproducible regression and classification pipelines, interpret model behavior with feature importance or SHAP, and test robust trend or clustering methods on sensory signals. Meanwhile, product builders, sommeliers, and culinary creators can craft recommender systems, generate menu-aware pairing suggestions, mine tasting notes with NLP, or prototype consumer-facing guides and visual stories.

Practical, too: the files are tidy and easy to join (by wine type/category), so you can quickly move from exploratory plots to robust models or a polished dashboard. Whether your goal is to quantify what makes a wine score highly, to predict pairings for new dishes, or to tell beautiful data-driven tasting stories, this dataset supplies both the signal (chemical measurements) and the context (pairing judgments and descriptions) needed to create compelling, reproducible work.

Jump in to ask questions like: Which chemical traits most reliably predict perceived quality? How do pairing preferences vary by cuisine or wine style? Can measured wine features improve automated pairing recommendations? This bundle invites curiosity, experimentation, and storytelling — perfect for teaching, portfolio projects, competitions, or production prototypes.

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