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Synopsis
In this project, a Wine Quality Prediction System was developed to predict the quality of wine based on various physicochemical properties. The system leverages Python programming and the Scikitlearn library for implementing machine learning algorithms. A Random Forest Classifier was employed to achieve accurate and efficient predictions. The project utilizes Pandas and NumPy for data preprocessing and exploratory data analysis, while Matplotlib and Seaborn were used for visualization of trends and patterns in the dataset. The dataset used for this project was sourced from the UCI Machine Learning Repository. The development process followed the Iterative life cycle model of software development, ensuring systematic implementation and testing of features. The project is structured using Modular Design Principles, enhancing maintainability and scalability.
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Predictive Viticulture: A Machine Learning Approach to Wine Quality
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LAP Lambert Academic Publishing, 2026
ISBN 10: 6630168879
ISBN 13: 9786630168877
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Predictive Viticulture: A Machine Learning Approach to Wine Quality
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LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630168879
ISBN 13: 9786630168877
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Predictive Viticulture: A Machine Learning Approach to Wine Quality
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LAP LAMBERT Academic Publishing Jul 2026, 2026
ISBN 10: 6630168879
ISBN 13: 9786630168877
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Predictive Viticulture (Paperback)
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LAP Lambert Academic Publishing, 2026
ISBN 10: 6630168879
ISBN 13: 9786630168877
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Paperback. Condition: new. Paperback. In this project, a Wine Quality Prediction System was developed to predict the quality of wine based on various physicochemical properties. The system leverages Python programming and the Scikitlearn library for implementing machine learning algorithms. A Random Forest Classifier was employed to achieve accurate and efficient predictions. The project utilizes Pandas and NumPy for data preprocessing and exploratory data analysis, while Matplotlib and Seaborn were used for visualization of trends and patterns in the dataset. The dataset used for this project was sourced from the UCI Machine Learning Repository. The development process followed the Iterative life cycle model of software development, ensuring systematic implementation and testing of features. The project is structured using Modular Design Principles, enhancing maintainability and scalability. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786630168877
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Predictive Viticulture: A Machine Learning Approach to Wine Quality
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LAP LAMBERT Academic Publishing Jul 2026, 2026
ISBN 10: 6630168879
ISBN 13: 9786630168877
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Predictive Viticulture: A Machine Learning Approach to Wine Quality
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LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630168879
ISBN 13: 9786630168877
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Predictive Viticulture: A Machine Learning Approach to Wine Quality
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LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630168879
ISBN 13: 9786630168877
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Taschenbuch. Condition: Neu. Predictive Viticulture: A Machine Learning Approach to Wine Quality | Imran Siraj (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630168877 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 136014211
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