data analysis of tennis matches
DESCRIPTION
Data Analysis of Tennis Matches. Fatih Çalışır. Domain of the Data. ATP World Tour 250 ATP 250 Brisbane ATP 250 Sydney ... ATP World Tour 500 ATP 500 Memphis ATP 500 Dubai. 4 Types of Tennis Tournaments. Domain of the Data. ATP World Tour 1000 ATP 1000 Paris ATP 1000 Shanghai ... - PowerPoint PPT PresentationTRANSCRIPT
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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