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sephora

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This web crawler gathers the latest details, variations, imagery and pricing informations of a catalog of products given their urls from their corresponding online stores and prepares files ready for upload to your e-commerce platfrom. It was built with the purposes of making product additions easier for e-commerce retailers.

  • Updated Oct 29, 2019
  • Jupyter Notebook

Personalized recommender system for Sephora's cosmetics e-commerce platform. Using content-based filtering, with TF-IDF Vectorizer to extract product features and cosine similarity to recommend similar items based on user preferences. And collaborative filtering with SVD for identifying user patterns and recommending highly-rated products.

  • Updated Aug 30, 2023
  • Jupyter Notebook

Built upon an existing open-source project to analyze cosmetic product pricing by examining ingredients, brand influence, and customer ratings. Enhanced the project with ingredient scoring, brand analysis, and data visualizations using Python and Sephora data.

  • Updated Sep 1, 2024

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