Prediction of Wine Quality Using Machine Learning Algorithms
- 1 Department of Statistics, Truman State University, Kirksville, MO, USA
- 2 Department of Physics, Virginia Union University, Richmond, VA, USA
- 3 Department of Physics, Virginia Commonwealth University, Richmond, VA, USA
- 4 Department of Physics, The Catholic University of America, Washington D. C., USA
Abstract
As a subfield of Artificial Intelligence (AI), Machine Learning (ML) aims to understand the structure of the data and fit it into models, which later can be used in unseen data to achieve the desired task. ML has been widely used in various sectors such as in Businesses, Medicine, Astrophysics, and many other scientific problems. Inspired by the success of ML in different sectors, here, we use it to predict the wine quality based on the various parameters. Among various ML models, we compare the performance of Ridge Regression (RR), Support Vector Machine (SVM), Gradient Boosting Regressor (GBR), and multi-layer Artificial Neural Network (ANN) to predict the wine quality. Multiple parameters that determine the wine quality are analyzed. Our analysis shows that GBR surpass es all other models’ performance with MSE, R, and MAPE of 0.3741, 0.6057, and 0.0873 respectively. This work demonstrate s, how statistical analysis can be used to identify the components that mainly control the wine quality prior to the production. This will help wine manufacturer to control the quality prior to the wine production .
- Li, H., Zhang Z. and Liu, Z.J. (2017) Application of Artificial Neural Networks for Catalysis: A Review. Catalysts, 7, 306. https://doi.org/10.3390/catal7100306
- Shanmuganathan, S. (2016) Artificial Neural Network Modelling: An Introduction. In: Shanmuganathan, S. and Samarasinghe, S. (Eds.), Artificial Neural Network Modelling, Springer, Cham, 1-14. https://doi.org/10.1007/978-3-319-28495-8_1
- Jr, R.A., de Sousa, H.C., Malmegrim, R.R., dos Santos Jr., D.S., Carvalho, A.C.P.L.F., Fonseca, F.J., Oliveira Jr., O.N. and Mattoso, L.H.C. (2004) Wine Classification by Taste Sensors Made from Ultra-Thin Films and Using Neural Networks. Sensors and Actuators B: Chemical, 98, 77-82. https://doi.org/10.1016/j.snb.2003.09.025
- Cortez, P., Cerdeira, A., Almeida, F., Matos, T. and Reis, J. (2009) Modeling Wine Preferences by Data Mining from Physicochemical Properties. Decision Support Systems, Elsevier, 47, 547-553. https://doi.org/10.1016/j.dss.2009.05.016
- Larkin, T. and McManus, D. (2020) An Analytical Toast to Wine: Using Stacked Generalization to Predict Wine Preference. Statistical Analysis and Data Mining: The ASA Data Science Journal, 13, 451-464. https://doi.org/10.1002/sam.11474
- Lin, E.B., Abayomi, O., Dahal, K., Davis, P. and Mdziniso, N.C. (2016) Artifact Removal for Physiological Signals via Wavelets. Eighth International Conference on Digital Image Processing, 10033, Article No. 1003355. https://doi.org/10.1117/12.2244906
- Dahal, K.R. and Mohamed, A. (2020) Exact Distribution of Difference of Two Sample Proportions and Its Inferences. Open Journal of Statistics, 10, 363-374. https://doi.org/10.4236/ojs.2020.103024
- Dahal, K.R., Dahal, J.N., Goward, K.R. and Abayami, O. (2020) Analysis of the Resolution of Crime Using Predictive Modeling. Open Journal of Statistics, 10, 600-610, https://doi.org/10.4236/ojs.2020.103036
- Crookston, N.L. and Finley, A.O. (2008) yaImpute: An R Package for kNN Imputation. Journal of Statistical Software, 23, 1-16. https://doi.org/10.18637/jss.v023.i10
- Dahal, K.R. and Gautam, Y. (2020) Argumentative Comparative Analysis of Machine Learning on Coronary Artery Disease. Open Journal of Statistics, 10, 694-705. https://doi.org/10.4236/ojs.2020.104043
- Caruana, R. and Niculescu-Mizil, A. (2006) An Empirical Comparison of Supervised Learning Algorithms. Proceedings of the 23rd International Conference on Machine Learning, June 2006, 161-168. https://doi.org/10.1145/1143844.1143865