ChenglongChen/kaggle-CrowdFlower resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1770 stars · 649 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The 1st place winning solution code for the CrowdFlower Search Results Relevance competition on Kaggle. It provides a full pipeline for feature extraction, XGBoost model training, and ensemble selection for predicting search result relevance.

Use cases

• predict search results relevance for product queries
• learn how to win a kaggle nlp competition
• build semantic matching features for query-product pairs
• ensemble multiple model submissions for better scores
• study feature engineering for text relevance tasks
• reproduce a winning xgboost linear booster solution

When to choose

• you want to study a proven top-ranked approach to search relevance prediction
• you need a reference pipeline for text feature engineering and model ensembling in Kaggle competitions
• you are working on product search relevance or semantic similarity problems

When to avoid

• you need a production-ready or maintained search relevance library
• you want a clean modular framework (the author recommends the Kaggle_HomeDepot repo instead)
• you need actively supported software with a license

Facets

learning-resource · maturity maintenance

nlp search-engine machine-learning data-science machine-learning data-science python cpp cli kaggle competition-solution search-relevance semantic-similarity xgboost feature-engineering ensemble-learning natural-language-processing search

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ChenglongChen/kaggle-CrowdFlower")