Data
Performance-Prediction

Performance-Prediction

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Elif Ceren Gok
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Content The Dataset contains the summary of each player. on the basis of the summary of each player you have to predict the target variable Target Variable: 1-Signifies whether a player has a career of 5 years or more. 0-Signifies the career of the player is shorter than 5 years. Some features other than target variable: GamesPlayed PointsPerGame 3PointMade etc. Inspiration -This dataset is helpful in finding the features which are helpful in predicting that how long a player can play. -Use EDA to get the insight information about the features. -Create a classification model to predict the target variable.

21 features

Target (target)numeric2 unique values
0 missing
Namestring1294 unique values
0 missing
GamesPlayednumeric70 unique values
0 missing
MinutesPlayednumeric325 unique values
0 missing
PointsPerGamenumeric191 unique values
0 missing
FieldGoalsMadenumeric87 unique values
0 missing
FieldGoalsAttemptnumeric159 unique values
0 missing
FieldGoalPercentnumeric284 unique values
0 missing
3PointMadenumeric23 unique values
0 missing
3PointAttemptnumeric54 unique values
0 missing
3PointPercentnumeric254 unique values
11 missing
FreeThrowMadenumeric59 unique values
0 missing
FreeThrowAttemptnumeric76 unique values
0 missing
FreeThrowPercentnumeric383 unique values
0 missing
OffensiveReboundsnumeric44 unique values
0 missing
DefensiveReboundsnumeric74 unique values
0 missing
Reboundsnumeric101 unique values
0 missing
Assistsnumeric77 unique values
0 missing
Stealsnumeric26 unique values
0 missing
Blocksnumeric28 unique values
0 missing
Turnoversnumeric41 unique values
0 missing

19 properties

1340
Number of instances (rows) of the dataset.
21
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
11
Number of missing values in the dataset.
11
Number of instances with at least one value missing.
20
Number of numeric attributes.
0
Number of nominal attributes.
0.02
Number of attributes divided by the number of instances.
95.24
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.
0.82
Percentage of instances having missing values.
0.57
Average class difference between consecutive instances.
0.04
Percentage of missing values.

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