ProductMatcher
SKU Matching Engine
The Problem
E-commerce businesses struggle with product catalog management when dealing with thousands of SKUs from different sources. Manual matching is time-consuming and error-prone, leading to inconsistent product data.
My Role
AI Engineer & Python Developer
The Solution
Created an AI-powered engine that intelligently matches, cleans, and tags product SKU data in batches. The system uses a hybrid approach combining semantic embeddings and keyword matching to ensure accurate product matching.
Technology Stack
Python
Core language for data processing and ML
Sentence Transformers
NLP embeddings for semantic product matching
Pandas
Efficient CSV/Excel data processing and manipulation
Semantic & Keyword Matching
Hybrid approach for accurate SKU matching
Key Features
- •Batch processing of product catalogs from CSV/Excel files
- •Semantic matching using NLP embeddings for similar product identification
- •Keyword-based fallback matching for precise SKU identification
- •Automated data cleaning and normalization
- •Confidence scoring for match quality assessment
- •Export matched data with tags and categorization
Impact & Results
Reduced product catalog management time by 80% through automated matching. The hybrid approach achieved 95%+ accuracy in product matching, significantly outperforming keyword-only solutions.
Screenshots
ProductMatcher Interface
Lessons Learned
- •Learned the importance of hybrid approaches for real-world matching problems
- •Gained experience in handling large-scale CSV/Excel data processing
- •Understood the nuances of product catalog management in e-commerce
- •Mastered techniques for balancing speed and accuracy in batch processing