Predicting Wine Quality Using Machine Learning: A Comparative Analysis of Classification Algorithms

Predicting wine quality is an important task in food assiduity as it helps winemakers and consumers make informed opinions grounded on the chemical parcels of wine. Here we present a machine literacy grounded approach to prognosticate wine quality using a dataset of physicochemical parcels of red wine samples. The dataset contains features similar as acidity, alcohol content, pH, residual sugar, and sulfur dioxide content and corresponding quality scores ranging from 3 to 8. We first apply data preprocessing ways similar as point scaling and marker garbling to break the class imbalance problem and ameliorate the model performance. We classified wines into" high quality" or" low quality" using colourful algorithm of machine learning similar as logistic retrogression, arbitrary timber, support vector machine (SVM), K- nearest neighbour (KNN), and grade boosting. After training and assessing the models, we set up that ensemble ways similar as grade boosting and arbitrary timber outperformed naive algorithms in terms of delicacy, perfection, recall, and ROC- AUC. These results demonstrate the effectiveness of our machine learning model in prognosticating wine quality, with implicit operations in wine product, quality control, and consumer recommendation systems. Our study highlights the significance of data preprocessing, including regularization and class balancing, to ameliorate model performance. Likewise, we suggest approaches for unborn exploration, similar as exploring deep literacy ways and applying hyperparameter tuning to further ameliorate vaticination delicacy.