Business Intelligence

 Business Intelligence Article


The report focuses on data mining way of predict human being wine flavor preferences. A big data established is considered with white and red wine beverage samples (" Vinho Verde” wine coming from Portugal). The inputs consist of objective tests (e. g. PH values) and the output is based on physical data (median of by least 3 evaluations created by wine experts). Each professional graded your wine quality among 0 (very bad) and 10 (very excellent). Due to privacy and logistic problems, only physicochemical (inputs) and sensory (the output) variables are available (e. g. there is not any data about grape types, wine manufacturer, wine selling price, etc . ).

Datasets Regarded as:

Each record contains 12 attributes. Every record contains a set of attributes and a single attribute (quality) is the class. Theattributes regarded are: 1 - set acidity, numeric

2 - unpredictable acidity, number

a few - citric acid, number

four - recurring sugar, number

a few – chlorides, numeric

6 -- free sulfur dioxide, number

7 - total sulfur dioxide, numeric

8 – density, numeric

being unfaithful – ph level, numeric

10 – sulphates, numeric

eleven – liquor, numeric

12 – R/W, nominal – R= red, W = light

Class: top quality (score between 0 and 10)

Software applied - WEKA 3. 6. 9

A data set of 6497 instances was considered pertaining to training. The whole set of info was once again considered for cross acceptance of the model created from schooling data. From the data arranged, 21 documents was picked for conjecture of class. Data mining technique used

Classification technique has been used for the task which contains analysis of training set and test started determine the partnership between various attributes together with the class and in addition determines the accuracy from the training collection analysis and test collection analysis. Unique sample is regarded as later to get the conjecture using the style built. Multilayer perceptron style has been accustomed to make the prediction.

Training Data

Log obtained in WEKA software for the training established

=== Operate information ===

Scheme: weka. classifiers. features. MultilayerPerceptron -L 0. 3 -M zero. 2 -N 1000 -V 0 -S 0 -E 20 -H a -G -R Regards: RedWhiteWine

Circumstances: 6497

Qualities: 13

fixed acidity

unpredictable acidity

citric acid

residual sugar


free sulfur dioxide

total sulfur dioxide


ph level





Test mode: evaluate on training info

=== Repertorier model (full training set) ===

Linear Node 0

Inputs Dumbbells

Threshold -0. 02275995959002934

Node 1 -0. 653393136396115

Node 2 -0. 7469969878051129

Client 3 -0. 704911518425741

Node 4 -0. 701794263076919

Client 5 0. 5998183336312791

Client 6 zero. 3337874695758612

Sigmoid Node 1

Inputs Weights

Threshold -14. 592503430015068

Attrib fixed level of acidity 0. 6573199798982804

Attrib unstable acidity -0. 32516080283177706

Attrib citric chemical p 0. 2564336792108779

Attrib left over sugar installment payments on your 0486838393264843

Attrib chlorides -0. 7503764443370672

Attrib free sulfur dioxide -7. 559699636268105

Attrib total sulfur dioxide -0. 7037414666514369

Attrib density -0. 12222961725416373

Attrib pH installment payments on your 173130400861142

Attribsulphates -8. 301551882234396

Attrib alcoholic beverages -1. 376404787081152

Attrib R/W -1. 750605684541054

Sigmoid Node 2

Advices Weights

Tolerance -3. 645163438052926

Attrib fixed acidity -0. 8389921784313095

Attrib volatile level of acidity 4. 083424113977112

Attrib citric acid -6. 32779829071717

Attrib residual glucose -1. 2885398323120203

Attrib chlorides 1 . 7232054266992476

Attrib free sulfur dioxide 3. 12473374735373

Attrib total sulfur dioxide -0. 782474566415625

Attrib thickness 2 . 478505776050499

Attrib ph level 0. 9657520709788047

Attribsulphates -1. 648632533668692

Attrib alcohol -1. 3370433272374365

Attrib R/W 2 . 368062112315071

Sigmoid Node three or more

Inputs Weights

Threshold 0. 3240906770764899

Attrib fixed acid -0. 9839648006412054



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