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wine-reviews

wine-reviews

active ARFF CC BY-NC-SA 4.0 Visibility: public Uploaded 12-11-2018 by Florian Pargent
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130k wine reviews with variety, location, winery, price, and description. Downloaded from Kaggle [https://www.kaggle.com/zynicide/wine-reviews/home] on 29.10.2018. The original data was scraped from the WineEnthusiast homepage [https://www.winemag.com/?s=&drink_type=wine]. The second version of the dataset was used, which was scraped on 22.11.2017. The Kaggle dataset was licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) [https://creativecommons.org/licenses/by-nc-sa/4.0/]. The variable 'points' (the number of points WineEnthusiast rated the wine on a scale of 1-100) was selected as target variable. For a description of all variables, checkout the Kaggle dataset repo. The variable 'region_2' is ignored by default as it contains a large portion of missing values. The variable 'designation' is not used by default, as the number of factor labels is extremely high compared to the number of observations. The dataset further includes the text based variables 'description', 'taster_twitter_handle', and 'title' (ignored by default) which could be used to construct additional features. Special characters in text features have been removed to allow the upload to the platform. The ID variable from the Kaggle version was removed from the dataset. The factor labels of all nominal features had to be changed to integers to prevent a problem which would not allow the upload of nominal features with too many and too long labels.

13 features

points (target)numeric21 unique values
0 missing
countrynominal43 unique values
63 missing
descriptionstring119951 unique values
0 missing
designationstring37526 unique values
37465 missing
pricenumeric390 unique values
8996 missing
provincenominal425 unique values
63 missing
region_1nominal1229 unique values
21247 missing
region_2string17 unique values
79460 missing
taster_namenominal19 unique values
26244 missing
taster_twitter_handlestring15 unique values
31213 missing
titlestring118815 unique values
0 missing
varietynominal707 unique values
1 missing
winerynominal16757 unique values
0 missing

62 properties

129971
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
204752
Number of missing values in the dataset.
107584
Number of instances with at least one value missing.
2
Number of numeric attributes.
6
Number of nominal attributes.
0.84
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
3.04
First quartile of standard deviation of attributes of the numeric type.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
3196.67
Average number of distinct values among the attributes of the nominal type.
414.61
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
9.02
Mean skewness among attributes of the numeric type.
61.91
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
22.03
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
9.02
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.3
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
22.03
Second quartile (Median) of standard deviation of attributes of the numeric type.
829.52
Maximum kurtosis among attributes of the numeric type.
35.36
Minimum of means among attributes of the numeric type.
82.78
Percentage of instances having missing values.
Third quartile of entropy among attributes.
88.45
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
12.12
Percentage of missing values.
829.52
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
19
The minimal number of distinct values among attributes of the nominal type.
15.38
Percentage of numeric attributes.
88.45
Third quartile of means among attributes of the numeric type.
16757
The maximum number of distinct values among attributes of the nominal type.
0.05
Minimum skewness among attributes of the numeric type.
46.15
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
18
Maximum skewness among attributes of the numeric type.
3.04
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
18
Third quartile of skewness among attributes of the numeric type.
41.02
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.3
First quartile of kurtosis among attributes of the numeric type.
41.02
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
35.36
First quartile of means among attributes of the numeric type.
6658.5
Standard deviation of the number of distinct values among attributes of the nominal type.
414.61
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
61.91
Mean of means among attributes of the numeric type.
0.05
First quartile of skewness among attributes of the numeric type.

9 tasks

0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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