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Data Study Group Final Report: Codecheck

Data Study Group team

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Data Study Group team</dc:creator>
  <dc:description>Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges.

CodeCheck: How do our food choices affect climate change?

Different approaches were proposed to predict the carbon footprint of products from the different datasets provided by CodeCheck.

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.

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.</dc:description>
  <dc:subject>Data Study Groups</dc:subject>
  <dc:subject>The Alan Turing Institute</dc:subject>
  <dc:subject>Climate change</dc:subject>
  <dc:subject>Machine learning</dc:subject>
  <dc:title>Data Study Group Final Report: Codecheck</dc:title>
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