Haftu 201324487 Shamini 201424192 Thomas 201424190 Temesgen Seyoum 201425090.

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Presentation transcript:

Haftu Shamini Thomas Temesgen Seyoum

 Wine Rating is a score assigned by one or more wine critics.  In most cases, wine ratings are set by single wine critic.  Wine ratings are done with a scale of: ◦ ◦ 0-10 ◦ 0-5

 We would like to automate the wine ratting system and replace the wine critics role with a data mining algorithm.  The problem is of classification type. We would be classifying wine quality from class of 1 to 10.  The problem is also of inference type as we would be interested in what kinds of attributes affect the quality of the wine the most.

 The data is gotten from the following link: ◦  Data set Characteristics: Multivariate  Attribute Characteristics: Real  Number of Instances: 4898  Number of Attributes: 12 Attributes Fixed acidity Volatile Acididty Citric acid Residual sugar Chlorides Free sulfur dioxide Total sulfur dioxide Density Ph Sulphates alchol Quality

 It has been indicated that it is uncertain that all input variables are really relevant,  As such, we would be dealing with subset selections among the 11 attributes.  We would be using K-fold cross validation.  We currently presume to use k=10, though we might resort to iteratively choosing of the optimal k.

 Once we finished with the modeling, we will use the accuracy, specificity, sensitivity evaluation parameters to judge our results.  We would include a confusion matrix to show the effectiveness of our model.  We would express the error rate for each iteration of the K-fold validation.

Thank You!