Prediction of Wine Quality Using ML
Alt Text: Machine Learning-Based Wine Quality Prediction
Title: Prediction of Wine Quality Using Machine Learning
Caption: ML-driven approach for accurate wine quality assessment
Description: This paper presents a Machine Learning-based approach to predicting wine quality using key features such as pH value, temperature, alcohol content, Brix, gravity, and fixed acidity, leveraging the Random Forest algorithm for precise predictions.
Keywords: wine quality prediction, machine learning, Random Forest algorithm, pH level, temperature, alcohol content, Brix, gravity, fixed acidity
International Journal of Engineering and Techniques – Volume 10 Issue 3, May 2024
Ms. Puneetha M R1, Ananya P2, Ashritha M3, Bhavana HS4, Chethana B M5
1Assistant Professor, Department of Computer Science and Engineering, KS School of Engineering and Management, Bangalore, Karnataka, India
2-5Students, Department of Computer Science and Engineering, KS School of Engineering and Management, Bangalore, Karnataka, India
Abstract
This project proposes a Machine Learning-based approach to predict wine quality. Using a dataset containing various wine characteristics and their sample values, ML algorithms analyze the intricate relationships between input features and wine quality. The system extracts essential parameters such as pH value, temperature, alcohol content, Brix, gravity, and fixed acidity using hardware instruments like pH meters, thermometers, and hydrometers. The Random Forest algorithm is employed due to its robustness in handling complex datasets. This model enables winemakers to make informed decisions early in the production process by providing accurate predictions of wine quality.
Keywords
Wine quality prediction, Machine Learning, Random Forest algorithm, pH level, temperature, alcohol content, Brix, gravity, fixed acidity
How to Cite
Puneetha M R, Ananya P, Ashritha M, Bhavana HS, Chethana B M, “Prediction of Wine Quality Using Machine Learning,” International Journal of Engineering and Techniques, Volume 10, Issue 3, 2024. ISSN 2395-1303.
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