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Data Analysis of Tennis Matches
Fatih Çalışır
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1. ATP World Tour 250 ATP 250 Brisbane ATP 250 Sydney ...
2. ATP World Tour 500 ATP 500 Memphis ATP 500 Dubai
Domain of the Data4 Types of Tennis Tournaments
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3. ATP World Tour 1000 ATP 1000 Paris ATP 1000 Shanghai ...
4. Grand Slams Australian Open Roland Garros Wimbeldon US Open
Domain of the Data
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• Men’s Single• Year 2010• 11 ATP 500 Tournament• 9 ATP 1000 Tournament• 4 Grand Slams
Domain of the Data
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Source of DataInternetOfficial Websites of the Players
ATP(Association of Tennis Professionals) Homa Page
2010 Result Archive
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Data ConstructionFrom different tablesEach table from different
websiteCombining easily
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Data ConstructionPlayers Table
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Data ConstructionTournament Results Table
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Data ConstructionTournament Info Table
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Data ConstructionFinal Data Table29 features1453 instances
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Aim of the ProjectClassification
Finding weights for attributes
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Missing ValuesPlayers’ HeightPlayers’ WeightPlayers’ BMIPlayers’ Date of being
Professional
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Missing ValuesPlayers’ HeightConsider players with same weight
Take the averagePlayers’ WeightConsider players with same height
Take the average
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Missing ValuesPlayers’ Height and WeightIf both of them are missingRemove the row
Players’ Date of beign ProfessionalConsider players with same ageTake the average
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Data UnderstandingMin,Max,Median,Average
values for numeric attributes
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Data UnderstandingOccurrence table for categorical
and numeric attributes
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Data UnderstandingHistogram for numeric attributes
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Data UnderstandingBox Plot for main characteristics
of numerical attributes
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Data UnderstandingScatter Plot to relate two
attributes
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Feature SelectionLinear Correlation
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Feature SelectionBackward EleminationNaive Bayes for Ranking
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Feature Selection28 attributes reduced to 19
attributesAtrributes are meaningful
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Weight of AttributesRIMARC to find weights
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ClassificationKNIME
Decision Tree – C4.5
Gain Ratio Qualitiy Meauser
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Classification1017 instances for training436 instances for testing842 positive instances611 negative instancesTraining and test data is
randomly selected
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ClassificationDecision Tree
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Classification
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ClassificationConfusion Matrix
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ClassificationConfusion Matrix
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ClassificationAccuracy Statistics
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ClassificationNaive Bayes ClassifierConfusion Matrix
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ClassificationConfusion Matrix
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ClassificationAccuracy Statistics
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ClassificationC4.5 vs Naive Bayes
Decision Tree (C4.5) Naive Bayes
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ClassificationC4.5 vs Naive Bayes
Decision Tree (C4.5)
Naive Bayes