ashis2004
commented
3 weeks ago @TAHIR0110 please assign it to me.
Open ashis2004 opened 3 weeks ago
ashis2004
commented
3 weeks ago @TAHIR0110 please assign it to me.
Is there an existing issue for this?
Feature Description
About the Data Set : Fixed acidity : The total acidity is divided into two groups: the volatile acids and the nonvolatile or fixed acids.The value of this variable is represented by in gm/dm3 in the data sets. Volatile acidity: The volatile acidity is a process of wine turning into vinegar. In this data sets, the volatile acidity is expressed in gm/dm3. Citric acid : Citric acid is one of the fixed acids in wines. It’s expressed in g/dm3 in the data sets. Residual Sugar : Residual Sugar is the sugar remaining after fermentation stops, or is stopped. It’s expressed in g/dm3 in the data set. Chlorides : It can be a important contributor to saltiness in wine. The value of this variable is represented by in gm/dm3 in the data sets. Free sulfur dioxide : It is the part of the sulfur dioxide that is added to a wine. The value of this variable is represented by in gm/dm3 in the data sets. Total Sulfur Dioxide : It is the sum of the bound and the free sulfur dioxide.The value of this variable is represented by in gm/dm3 in the data sets.
Use Case
Wine quality prediction using deep learning (DL) offers significant benefits across various aspects of the wine industry. One key use case is in quality control, where automated systems can analyze chemical properties and sensory data to classify wine quality, ensuring consistency across batches and reducing human error. Personalized recommendations are another important application, where DL models can tailor wine suggestions based on individual preferences, enhancing customer satisfaction and enabling targeted marketing. In inventory management, DL helps optimize stock by predicting quality and managing inventory effectively, reducing wastage by identifying potential issues early.
Benefits
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Add ScreenShots
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Priority
High
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