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Pokmon-Legendary-Data

Pokmon-Legendary-Data

active ARFF GPL 2 Visibility: public Uploaded 23-03-2022 by Elif Ceren Gok
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Context In the world of Pokmon academia, one name towers above any other Professor Samuel Oak. While his colleague Professor Elm specializes in Pokmon evolution, Oak has dedicated his career to understanding the relationship between Pokmon and their human trainers. A former trainer himself, the professor has first-hand experience of how obstinate Pokmon can be particularly when they hold legendary status. For his latest research project, Professor Oak has decided to investigate the defining characteristics of legendary Pokmon to improve our understanding of their temperament. Hearing of our expertise in classification problems, he has enlisted us as the lead researchers. Our journey begins at the professor's research lab in Pallet Town, Kanto. The first step is to open up the Pokdex, an encyclopaedic guide to 801 Pokmon from all seven generations. Content After browsing the Pokdex, we can see several variables that could feasibly explain what makes a Pokmon legendary. We have a series of numerical fighter stats attack, defense, speed and so on as well as a categorization of Pokemon type (bug, dark, dragon, etc.). is_legendary is the binary classification variable we will eventually be predicting, tagged 1 if a Pokmon is legendary and 0 if it is not. Before we explore these variables in any depth, let's find out how many Pokmon are legendary out of the 801 total, using the handy count() function from the dplyr package. Acknowledgements DataCamp

14 features

is_legendary (target)numeric2 unique values
0 missing
pokedex_numbernumeric801 unique values
0 missing
namestring800 unique values
0 missing
attacknumeric114 unique values
0 missing
defensenumeric109 unique values
0 missing
height_mnumeric51 unique values
20 missing
hpnumeric99 unique values
0 missing
percentage_malenumeric7 unique values
98 missing
sp_attacknumeric111 unique values
0 missing
sp_defensenumeric97 unique values
0 missing
speednumeric113 unique values
0 missing
typestring18 unique values
0 missing
weight_kgnumeric421 unique values
20 missing
generationnumeric7 unique values
0 missing

19 properties

801
Number of instances (rows) of the dataset.
14
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
138
Number of missing values in the dataset.
117
Number of instances with at least one value missing.
12
Number of numeric attributes.
0
Number of nominal attributes.
0.02
Number of attributes divided by the number of instances.
85.71
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.
14.61
Percentage of instances having missing values.
0.98
Average class difference between consecutive instances.
1.23
Percentage of missing values.

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