September 13, 2018 Report Open Access

Data Study Group team

DataCite XML Export

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  <identifier identifierType="DOI">10.5281/zenodo.1415344</identifier>
  <creators>
    <creator>
      <creatorName>Data Study Group team</creatorName>
      <affiliation>The Alan Turing Institute</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Data Study Group Final Report: Codecheck</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Data Study Groups</subject>
    <subject>The Alan Turing Institute</subject>
    <subject>Climate change</subject>
    <subject>Machine learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-09-13</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Report"/>
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    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Data Study Groups are week-long events at The Alan Turing Institute&amp;nbsp;bringing together some of the country&amp;rsquo;s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;CodeCheck: How do our food choices affect climate change?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Different approaches were proposed to predict the carbon footprint of products from the different datasets provided by CodeCheck.&lt;/p&gt;

&lt;p&gt;Multivariate linear regression and random forest regression models perform well in predicting carbon footprint, especially when - in addition to the nutrition information - the product categories, learned through Latent Dirichlet Allocation (LDA), were used as extra features in the models.&lt;/p&gt;

&lt;p&gt;The prediction accuracy of the models that were considered varied across datasets. A potential way to display the footprint estimates in the app was proposed.&lt;/p&gt;</description>
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