MACHINE LEARNING ALGORITHM: WINE QUALITY PREDICTION

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Published Sep 8, 2021
Prateek Singhal Pawan Singh Bramah Hazela Vineet Singh Vikrant Singh

Abstract

Wine classification may be a difficult task since taste is that the least understood of the human senses. A good wine quality prediction is often very useful within the certification phase, since currently the sensory analysis is performed by human tasters, being clearly a subjective approach. An automatic predictive system is often integrated into a choice network, helping the speed and quality of the performance. Furthermore, a feature selection process can help to research the impact of the analytical tests. If it is concluded that several input variables are very relevant to predict the wine quality, since within the production process some variables are often controlled, this information could be used to improve the wine quality.

How to Cite

Singhal, P., Singh, P. ., Hazela, B. ., Singh, V., & Singh, V. (2021). MACHINE LEARNING ALGORITHM: WINE QUALITY PREDICTION. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/167
Abstract 20 |

Article Details

Keywords

Machine Learning, Data Classification, Confusion matrix, classification model

References
1. Legin, Rudnitskaya, Luvova, Vlasov, Natale and D'Amico. (2003) “Evaluation of Italian wine by the electronic tongue: recognition, quantitative analysis and correlation with human sensory perception”. Analytica Chimica Acta 484 (1)
2. Sun, Danzer and Thiel. (1997) “Classification of wine samples by means of artificial neural networks and discrimination analytical methods”. Fresenius Journal of Analytical Chemistry 359 (2)
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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
10. 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
11. 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 K. R. Dahal et al. DOI: 10.4236/ojs.2021.112015 289 Open Journal of Statistics
12. 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
13. 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
14. Singhal, P., Sharma, P., & Hazela, B. (2019). End-to-end message authentication using CoAP over IoT. InInternational Conference on Innovative Computing and
Communications (pp. 279-288). Springer, Singapore.
15. Singhal, P., Sharma, P., & Rizvi, S. (2019). Thwarting Sybil Attack by CAM Method in WSN using Cooja Simulator Framework.International Journal of Engineering &
Technology, 8(1.5), 116-125.
16. Singhal, P., Sharma, P., & Arora, D. (2018). An approach towards preventing iot based sybil attack based on contiki framework through cooja simulator. International Journal of Engineering & Technology, 7(2.8), 261-267.
17. Singhal, P., & Vidyarthi, P. S. A. (2020). Interpretation and localization of Thorax diseases using DCNN in Chest X-Ray.Journal of Informatics Electrical and Elecrtonics
Engineering, 1(1), 1.
18. Turian,J.P.,Bergstra,J.andBengio,Y.(2009)QuadraticFeaturesandDeepArchitectures for Chunking. Human Language Technologies Conference of the North American Chapter of the Association of Computational Linguistics, Boulder, Colorado, 31 May-5 June 2009, 245-248.
19. Chen, C.M. Liang, C.C. and Chu, C.P. (2020) Long-Term Travel Time Prediction Using Gradient Boosting. Journal of Intelligent Transportation Systems, 24, 109- 124. https://doi.org/10.1080/15472450.2018.1542304
Section
GE3- Computers & Information Technology