kaggle-CrowdFlower

Why we included this project

For engineers building search ranking for e-commerce or marketplaces, this is a complete look at the pipeline that won Kaggle's Search Results Relevance competition, where the task was scoring how well a query matches a product result. Instead of a trimmed notebook, you get the working parts: feature generation scripts, an XGBoost linear model, a model library, and the ensemble-selection step that produced the first-place submission. The code is modular enough to adapt to similar relevance problems, and the documentation walks through the reasoning behind each choice. Teams experimenting with semantic matching or learning-to-rank will find it a useful reference for structuring feature engineering and ensembling, even if the competition data is no longer current.