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dc.contributorVellido Alcacena, Alfredo
dc.contributorRibas Ripoll, Vicent
dc.contributor.authorBilal, Ahsan
dc.date.accessioned2018-07-06T09:18:56Z
dc.date.available2018-07-06T09:18:56Z
dc.date.issued2018-04-26
dc.identifier.urihttp://hdl.handle.net/2117/119037
dc.description.abstractFeature selection is an important technique to find the most relevant features. Apache Spark is a big data processing framework but unable to cope with approx. 0.74 million features in our Obesity dataset. However, we tackle this challenge in 2-phase pipeline.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshBig data
dc.subject.lcshMachine learning
dc.subject.lcshGenomics
dc.subject.otherObesity Prediction
dc.subject.otherFeature Selection
dc.subject.otherApache Spark
dc.subject.otherGenomic Data
dc.subject.otherSNPs
dc.titleBig data analytics for obesity prediction
dc.title.alternativeBig data analytics in healt
dc.typeMaster thesis
dc.subject.lemacDades massives
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacGenòmica
dc.identifier.slug131677
dc.rights.accessOpen Access
dc.date.updated2018-04-30T04:01:34Z
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona
dc.contributor.covenanteeFundació Eurecat


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