This project is an analysis using machine learning algorithms to predict wine quality. Data were visualised and analysed to study the effects of various physical and chemical properties on wine quality. Models such as Random Forest, Decision Tree and Logistic Regression were used for classification and their performance was compared. Model evaluation metrics include accuracy, precision, recall, F1 score and ROC AUC.
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This project uses machine learning techniques to predict the quality of red wines based on their chemical properties. Together with data analysis and visualisation, the project aims to discover the factors that influence wine quality.
ozerzeynep/RedWineQualityAnalysis
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This project uses machine learning techniques to predict the quality of red wines based on their chemical properties. Together with data analysis and visualisation, the project aims to discover the factors that influence wine quality.
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