product-categorization

The 1st place solution for SIGIR 2020 E-Commerce Workshop Multimodal Product Classification Challenge

This repository contains code and data download instructions for the workshop paper "Improving Hierarchical Product Classification using Domain-specific Language Modelling" by Alexander Brinkmann and Christian Bizer.

Proper categorization of e-commerce products enhances the user experience and achieves better results with external search engines. The objective of the project is to classify a product into four given categories, based on its description available on an e-commerce platform.

SAP Commerce extension using TypeSafe's Jev to moderate product reviews and suggest product categories and classification attribute values: dry runs on your own data first, an audit record per decision. Plus a Claude Code skill.

E-Commerce web application based on Django framework

This repository illustrates the task of applying Machine Translation ( Seq2Seq Attention Network ) for Product Categorization of an E-Commerce Website data (Flipkart), classification of the description of products into the primary category of their category tree, and documenting the path to an optimal model pipeline

Masterschool's capstone project integrating skills and tools for data analysis.

Map product titles to a 3-level category tree using OpenAI embeddings: cosine matching or FAISS top-k + GPT.

This ML model is trained on BestBuy dataset and predicts 10 categories of product on the basis of title and description.

Industrial RFID and data collection device website built with WordPress, Elementor Pro, and WooCommerce, featuring product catalog, hardware solutions, and industry applications.

Manual Product Data Scraper with ChatGPT Integration

E-Commerce web application based on Django framework.

This is an ecommerce website for Pardo by Mireia Pardo. STREAM4 project of #codeinstitute´s diploma in Fullstack-Software-Development // Pardo by Mireia ecommerce

Curating Shopify's public product catalogue into a clean, balanced category dataset: leakage checks, deduplication, text hygiene and rebalancing. 25k products, 141 classes.

AI-powered Telegram bot for processing supermarket receipts using GPT-4o vision API

Ranking marketplace offers by how likely their category is wrong, so reviewers check the most suspicious first. TF-IDF + logistic regression, grouped out-of-fold. Precision@50: 96% on simulated label swaps (random: 9.1%), 16% on original labels after manual review.