Data
Yu-Gi-Oh-Normal-Monster-Cards

Yu-Gi-Oh-Normal-Monster-Cards

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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Yu-Gi-Oh! is a Japanese manga series about gaming written and illustrated by Kazuki Takahashi. It was serialized in Shueisha's Weekly Shnen Jump magazine between September 30, 1996 and March 8, 2004. The plot follows the story of a boy named Yugi Mutou, who solves the ancient Millennium Puzzle. Yugi awakens a gambling alter-ego within his body that solves his conflicts using various games. Two anime adaptations were produced; one by Toei Animation, which aired from April 4, 1998 to October 10, 1998,[2] and another produced by NAS and animated by Studio Gallop titled Yu-Gi-Oh! Duel Monsters, which aired between April 2000 and September 2004. The manga series has spawned a media franchise that includes multiple spin-off manga and anime series, a trading card game, and numerous video games. Most of the incarnations of the franchise involve the fictional trading card game known as Duel Monsters, where each player uses cards to "duel" each other in a mock battle of fantasy "monsters". This forms the basis for the real life Yu-Gi-Oh! Trading Card Game. As of 2018, Yu-Gi-Oh is one of the highest-grossing media franchises of all time. PLEASE UPVOTE

11 features

Country_(region)string156 unique values
0 missing
Laddernumeric156 unique values
0 missing
SD_of_Laddernumeric156 unique values
0 missing
Positive_affectnumeric155 unique values
1 missing
Negative_affectnumeric155 unique values
1 missing
Social_supportnumeric155 unique values
1 missing
Freedomnumeric155 unique values
1 missing
Corruptionnumeric148 unique values
8 missing
Generositynumeric155 unique values
1 missing
Log_of_GDP_per_capitanumeric152 unique values
4 missing
Healthy_life_expectancynumeric150 unique values
6 missing

19 properties

156
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
23
Number of missing values in the dataset.
16
Number of instances with at least one value missing.
10
Number of numeric attributes.
0
Number of nominal attributes.
0.07
Number of attributes divided by the number of instances.
90.91
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
10.26
Percentage of instances having missing values.
Average class difference between consecutive instances.
1.34
Percentage of missing values.

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