Data
video-game-sales

video-game-sales

in_preparation ARFF Publicly available Visibility: public Uploaded 06-11-2018 by Florian Pargent
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Kaggle dataset containing a list of video games with sales greater than 100,000 copies [https://www.kaggle.com/gregorut/videogamesales#vgsales.csv], which was generated by a scrape of vgchartz.com [https://github.com/GregorUT/vgchartzScrape]. For a description of the dataset, checkout the home of the kaggle dataset repo [https://www.kaggle.com/gregorut/videogamesales/home]. In contrast to the intention of the original author, the categorical variable 'Genre' was selected as the target here. Note that the variable 'Name' (ignored by default) is not unique for each row, as many games are published on more than one 'Platform'. The original dataset was sorted deacreasingly by the variable 'Global_Sales' (which was not changed here), which is why the ID variable 'Rank' directly reflects the rank order of 'Global_Sales'.

9 features

Genre (target)nominal12 unique values
0 missing
Rank (row identifier)numeric16598 unique values
0 missing
Name (ignore)string11493 unique values
0 missing
Platformnominal31 unique values
0 missing
Yearnumeric39 unique values
271 missing
Publishernominal578 unique values
58 missing
NA_Salesnumeric409 unique values
0 missing
EU_Salesnumeric305 unique values
0 missing
JP_Salesnumeric244 unique values
0 missing
Other_Salesnumeric157 unique values
0 missing
Global_Salesnumeric623 unique values
0 missing

62 properties

16598
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
12
Number of distinct values of the target attribute (if it is nominal).
329
Number of missing values in the dataset.
307
Number of instances with at least one value missing.
6
Number of numeric attributes.
3
Number of nominal attributes.
502
Third quartile of means among attributes of the numeric type.
0.74
Maximum mutual information between the nominal attributes and the target attribute.
12
The minimal number of distinct values among attributes of the nominal type.
66.67
Percentage of numeric attributes.
0.74
Third quartile of mutual information between the nominal attributes and the target attribute.
578
The maximum number of distinct values among attributes of the nominal type.
-1
Minimum skewness among attributes of the numeric type.
33.33
Percentage of nominal attributes.
20.22
Third quartile of skewness among attributes of the numeric type.
24.23
Maximum skewness among attributes of the numeric type.
0.19
Minimum standard deviation of attributes of the numeric type.
4.03
First quartile of entropy among attributes.
2.62
Third quartile of standard deviation of attributes of the numeric type.
5.83
Maximum standard deviation of attributes of the numeric type.
3.51
Percentage of instances belonging to the least frequent class.
146.14
First quartile of kurtosis among attributes of the numeric type.
321.44
Standard deviation of the number of distinct values among attributes of the nominal type.
5.13
Average entropy of the attributes.
582
Number of instances belonging to the least frequent class.
0.07
First quartile of means among attributes of the numeric type.
538.42
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
0.23
First quartile of mutual information between the nominal attributes and the target attribute.
334.58
Mean of means among attributes of the numeric type.
8.15
First quartile of skewness among attributes of the numeric type.
0.12
Average class difference between consecutive instances.
0.48
Average mutual information between the nominal attributes and the target attribute.
0.28
First quartile of standard deviation of attributes of the numeric type.
3.4
Entropy of the target attribute values.
9.64
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
5.13
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
207
Average number of distinct values among the attributes of the nominal type.
626.53
Second quartile (Median) of kurtosis among attributes of the numeric type.
7.04
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
14.92
Mean skewness among attributes of the numeric type.
0.21
Second quartile (Median) of means among attributes of the numeric type.
19.98
Percentage of instances belonging to the most frequent class.
1.53
Mean standard deviation of attributes of the numeric type.
0.48
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
3316
Number of instances belonging to the most frequent class.
4.03
Minimal entropy among attributes.
18.1
Second quartile (Median) of skewness among attributes of the numeric type.
6.24
Maximum entropy among attributes.
1.85
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.66
Second quartile (Median) of standard deviation of attributes of the numeric type.
6.24
Third quartile of entropy among attributes.
1025.35
Maximum kurtosis among attributes of the numeric type.
0.05
Minimum of means among attributes of the numeric type.
1.85
Percentage of instances having missing values.
823.36
Third quartile of kurtosis among attributes of the numeric type.
2006.41
Maximum of means among attributes of the numeric type.
0.23
Minimal mutual information between the nominal attributes and the target attribute.
0.22
Percentage of missing values.

3 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Genre
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Genre
0 runs - estimation_procedure: 50 times Clustering
Define a new task