Red Wine Quality Prediction Using Machine Learning Techniques

Kumar, Sunil; Agrawal, K.C.; Mandan, Nelshan

doi:10.1109/iccci48352.2020.9104095

Cited by 39 publications

(19 citation statements)

References 8 publications

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“…where 𝑓 𝑖 𝑖 , 𝑛 𝑖 𝑗 are the importance of feature 𝑖, the importance of node 𝑗 respectively. Then with dividing by the sum of all feature importance values, they can be normalized to a value given in (11) between 0 and 1…”

Section: Random Forestmentioning

confidence: 99%

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Wine Type Classification Using Random Forest Model

Cao¹,

Chen²,

Lin³

2022

HSET

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Wine Type Classification indicates that its indexes can ascertain the wine category. Therefore, it can be applied in modern industrial wine production and identification to reduce the rates of inferior products or to terminate the sale of homemade hooch or watered-down cheap alcohol. This paper explores Random Forest to classify wine. Since there are null values in the data, we first input the wine quality dataset and drop out the null values. Standard scaling is ignored because it expands the differences of data and the original datas are special for its distribution to deviation. Then, principal components analysis (PCA) is applied to reduce the dimensions of variable attributes. Finally, we perform random forest to the dataset to see the precision and F1 scores. We compare our methods with logistic regression, SVM, and naive Bayes model. The accuracies of these methods are 0.884375, 0.88125, and 0.884375, respectively. Our result shows that the random forest strategy generates promising accuracy of wine classification. Therefore, Random Forest can predict the industrial product quality and even can recognize the wine type with a high precision rate.

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Section: Random Forestmentioning

confidence: 99%

“…To verify the accuracy of our chosen model Random Forest, we need to compare the obtained accuracy, precision, F1 score, confusion matrix, and AUC with other models. Here we apply Logistic regression [9], SVM [10], and NaiveBayes [11] as the baselines.…”

Section: Baselinesmentioning

confidence: 99%

Wine Type Classification Using Random Forest Model

Cao¹,

Chen²,

Lin³

2022

HSET

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“…In recent years there is an increase in wine consumption, and wine industries strive to produce good quality wine at less cost (Kumar, 2020). Most of the chemicals are almost the same for different types of wine.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the chemicals are almost the same for different types of wine. However, the exact fine concentration of each chemical is different in different types of wines (Kumar, 2020).…”

Section: Introductionmentioning

confidence: 99%

Wine quality and type prediction from physicochemical properties using neural networks for machine learning: a free software for winemakers and customers.

Mor¹,

Asras²,

Gal³

et al. 2022

agriRxiv

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Quality assessment is a crucial issue within the wine industry. The traditional way of assessing by human experts is time consuming and very expensive. Machine learning techniques help in the process of quality assurance in a wide range of industries. The purpose of this study was to develop and offer a free software, for winemakers and customers in which they can easily provide the physicochemical properties of the wine and receive an accurate prediction of the anticipated quality and type of the wine. We used comprehensive datasets of 6497 examples, which contained physicochemical properties and appropriate quality. We combined these datasets, built and trained several neural networks models. We evaluated their performance and selected the best model. Wine quality estimations were modeled as a regression problem and wine type detection as a classification problem. The best model performed well for prediction of wine quality (root means squared error = 0.54) and type (f-score=0.99). With our free software, winemakers and customers can examine how a fine change in each physicochemical property could affect the quality of the wine. They could easily figure out the importance of each physicochemical property, and which one to ignore for reduction of cost. The process is very fast, accurate and does not require taste experts for sensory tests.

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“…When utilizing RStudio software to estimate white wine quality, the Support Vector Machine performed best, with an accuracy of 67.25 percent, followed by the Random Forest, which had a 65.83 percent accuracy, and the Nave Bayes technique, which had a 55.91 percent accuracy. [6] Based on neural networks supplied with 15 input parameters, [7] identified six geographic wine origins. For their investigations in Germany, they employed 170 data samples.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Performance of Several Regression Algorithms in Predicting the Quality of White Wine in WEKA

Clarin¹

2022

IJETAE

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The goal of the study was to examine the efficacy of multiple regression algorithms in predicting white wine quality. The white wine dataset from the UCI Machine Learning Repository was used. The Waikato Environment for Knowledge Analysis uses and implements regression methods. The correlation coefficient reveals that the result of performance is that the According to the results of the experiment, the Random Forest (r = 0.7459) ranked first, followed by k-Nearest Neighbor (r = 0.6225), Decision Tree (r= 0.5500), Linear regression (r =0.5257), Multi-layer perceptron (r = 0.5236), and Support Vector Machine (r= 5225). Alcohol and fixed acidity, with (r=0.48) and (r=-0.39), respectively, show a substantial correlation on regression model performance prediction, but other variables have a weak to no significant link. Future research, such as the use of additional prediction models, could be utilized to determine and compare its performance to the study's existing findings

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Red Wine Quality Prediction Using Machine Learning Techniques

Cited by 39 publications

References 8 publications

Wine Type Classification Using Random Forest Model

Wine Type Classification Using Random Forest Model

Wine quality and type prediction from physicochemical properties using neural networks for machine learning: a free software for winemakers and customers.

Comparison of the Performance of Several Regression Algorithms in Predicting the Quality of White Wine in WEKA

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