Data
League-of-Legends-Diamond-Games-(First-15-Minutes)

League-of-Legends-Diamond-Games-(First-15-Minutes)

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Context Inspired by the following dataset , we have a collection of data on the first 15 minutes of about 50000 Diamond ranked League of Legends matches scraped using Riot's API. Can you predict their outcomes? Content Data All matches were collected with the following parameters: Season: 13 Server: NA1 Rank: Diamond Tier: I,II,III,IV Acknowledgements Thank you to Riot Games for allowing access to their API. Inspiration When working on the linked dataset above, we see classification accuracy peak around 70. Given that we have 5 times the amount of data, I wanted to explore how this would improve our results.

19 features

Unnamed:_0numeric48651 unique values
0 missing
matchIdnumeric48632 unique values
0 missing
blue_winnumeric2 unique values
0 missing
blueGoldnumeric11986 unique values
0 missing
blueMinionsKillednumeric259 unique values
0 missing
blueJungleMinionsKillednumeric137 unique values
0 missing
blueAvgLevelnumeric24 unique values
0 missing
redGoldnumeric11960 unique values
0 missing
redMinionsKillednumeric271 unique values
0 missing
redJungleMinionsKillednumeric141 unique values
0 missing
redAvgLevelnumeric24 unique values
0 missing
blueChampKillsnumeric35 unique values
0 missing
blueHeraldKillsnumeric5 unique values
0 missing
blueDragonKillsnumeric1 unique values
0 missing
blueTowersDestroyednumeric12 unique values
0 missing
redChampKillsnumeric37 unique values
0 missing
redHeraldKillsnumeric5 unique values
0 missing
redDragonKillsnumeric1 unique values
0 missing
redTowersDestroyednumeric11 unique values
0 missing
Show all 19 features

19 properties

NumberOfInstances
48651
Number of instances (rows) of the dataset.
NumberOfFeatures
19
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
19
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
Dimensionality
0
Number of attributes divided by the number of instances.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
MajorityClassSize
Number of instances belonging to the most frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
AutoCorrelation
Average class difference between consecutive instances.
PercentageOfMissingValues
0
Percentage of missing values.
Show all 19 properties

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