Cosmetic Recommendation Engine
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Updated
May 4, 2019 - Jupyter Notebook
Cosmetic Recommendation Engine
A web crawler crawling all cosmetics information from Sephora implemented in Scrapy
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.
Identifying fake reviews on Sephora using One Class SVM
Sephora is an online e-commerce website for personal care and beauty products. Signup and login functionality , User and admin dashboard , Product and cart page are some of the features. Built the backend via NodeJS and MongoDB. Created the admin dashboard with all functionality Designed product and single product page.
Sephora is a French multinational retailer of personal care and beauty products. Featuring nearly 340 brands,along with its own private label, Sephora Collection, Sephora offers beauty products including cosmetics, skincare and many more.
Identifying fake reviews on Sephora using MLP autoencoder with anomaly detection
A recommender system for cosmetic products, implemented from scratch using Python.
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.
Chrome Extension Project
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.
Find an business insight from e-commerce sephora dataset
Sephora web scraped data, on sale on Databoutique.com. Dataset created with web scraping, full snapshot of the website.
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