Enhancing red wine quality prediction through Machine Learning approaches with Hyperparameters optimization technique
- Title
- Enhancing red wine quality prediction through Machine Learning approaches with Hyperparameters optimization technique
- Creator
- Amzad Basha M.S.; Desai K.; Christina S.; Sucharitha M.M.; Maheshwari A.
- Description
- In light of the intricacy of the winemaking process and the wide variety of elements that could affect the taste and quality of the finished product, predicting red wine quality is difficult. ML methods have been widely used in forecasting red wine quality from its chemical characteristics in recent years. This Paper evaluated the comparison of classification and regression methods to predict the quality of red wine and performed the initial data analysis and exploratory data analysis on the data. This study implemented different Classifiers and Regressors that were trained and tested. Contrasted and Comparative analysis of the accuracies of eight models with hyperparameter tuning optimization, including Logistic Regression, Gradient Boosting, Extra Tree, Ada Boost, Random Forest, Support Vector Classifier, and Decision Tree, Knn and measured the classification report with F1, Accuracy, and Recall Scores. For Imbalance data, SMOTE Classifier was used. This study performed the Cross-validation technique, such as Grid search and with the best hyperparameters tuning. The study's findings demonstrated that the Gradient Boosting technique accurately predicted red wine quality. This research shows the promising results of Gradient Boosting for predicting red wine quality and adds important context to the usage of machine learning classifiers for this challenge. 2023 IEEE.
- Source
- 2023 2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Grid search; Hyperparameter tuning; Machine learning classifiers; Red wine; Regressors
- Coverage
- Amzad Basha M.S., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Desai K., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Christina S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Maheshwari A., School of Commerce, Finance, Christ (Deemed to Be University), Department of Commerce, Ghaziabad, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835039763-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Amzad Basha M.S.; Desai K.; Christina S.; Sucharitha M.M.; Maheshwari A., “Enhancing red wine quality prediction through Machine Learning approaches with Hyperparameters optimization technique,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19924.