Imagine an online clothing store that can guess your right size as accurately as a good salesperson who’s seen thousands of customers before. This research tests different machine learning "brains" to see which one predicts the best size for each shopper using past data like body measurements and purchase history.
Online fashion retailers lose money and customers when items don’t fit—returns are expensive and frustrate shoppers. This work evaluates which machine learning models are most effective for predicting clothing sizes, helping reduce size-related returns and improve customer satisfaction.
If deployed commercially, the moat would come from proprietary fitting/returns data at scale and tight integration into the e-commerce and sizing workflow, making it hard for competitors to replicate the same accuracy quickly.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data quality and coverage of accurate size/fit labels across different brands, cuts, and demographics; model performance will be limited by noisy or sparse ground-truth fit data.
Early Majority
This is a rigorous academic evaluation of multiple ML models specifically for clothing size prediction, focusing on comparative performance and methodology rather than being tied to a single retailer’s proprietary system. It can guide practitioners on which classical models and feature designs work best before moving to more complex or black-box approaches.