{"ID":2872774,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18124","arxiv_id":"2509.18124","title":"Prediction of Coffee Ratings Based On Influential Attributes Using SelectKBest and Optimal Hyperparameters","abstract":"This study explores the application of supervised machine learning algorithms to predict coffee ratings based on a combination of influential textual and numerical attributes extracted from user reviews. Through careful data preprocessing including text cleaning, feature extraction using TF-IDF, and selection with SelectKBest, the study identifies key factors contributing to coffee quality assessments. Six models (Decision Tree, KNearest Neighbors, Multi-layer Perceptron, Random Forest, Extra Trees, and XGBoost) were trained and evaluated using optimized hyperparameters. Model performance was assessed primarily using F1-score, Gmean, and AUC metrics. Results demonstrate that ensemble methods (Extra Trees, Random Forest, and XGBoost), as well as Multi-layer Perceptron, consistently outperform simpler classifiers (Decision Trees and K-Nearest Neighbors) in terms of evaluation metrics such as F1 scores, G-mean and AUC. The findings highlight the essence of rigorous feature selection and hyperparameter tuning in building robust predictive systems for sensory product evaluation, offering a data driven approach to complement traditional coffee cupping by expertise of trained professionals.","short_abstract":"This study explores the application of supervised machine learning algorithms to predict coffee ratings based on a combination of influential textual and numerical attributes extracted from user reviews. Through careful data preprocessing including text cleaning, feature extraction using TF-IDF, and selection with Sele...","url_abs":"https://arxiv.org/abs/2509.18124","url_pdf":"https://arxiv.org/pdf/2509.18124v1","authors":"[\"Edmund Agyemang\",\"Lawrence Agbota\",\"Vincent Agbenyeavu\",\"Peggy Akabuah\",\"Bismark Bimpong\",\"Christopher Attafuah\"]","published":"2025-09-10T20:00:36Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.AP\"]","methods":"[]","has_code":false}