September 13, 2018 Report Open Access

Data Study Group team

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  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
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  "description": "<p>Data Study Groups are week-long events at The Alan Turing Institute&nbsp;bringing together some of the country&rsquo;s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges.</p>\n\n<p><strong>CodeCheck: How do our food choices affect climate change?</strong></p>\n\n<p>Different approaches were proposed to predict the carbon footprint of products from the different datasets provided by CodeCheck.</p>\n\n<p>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.</p>\n\n<p>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.</p>", 
  "license": "https://creativecommons.org/licenses/by-sa/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "The Alan Turing Institute", 
      "@type": "Person", 
      "name": "Data Study Group team"
    }
  ], 
  "headline": "Data Study Group Final Report: Codecheck", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2018-09-13", 
  "url": "https://zenodo.org/record/1415344", 
  "keywords": [
    "Data Study Groups", 
    "The Alan Turing Institute", 
    "Climate change", 
    "Machine learning"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.1415344", 
  "@id": "https://doi.org/10.5281/zenodo.1415344", 
  "@type": "ScholarlyArticle", 
  "name": "Data Study Group Final Report: Codecheck"
}