wepe/O2O-Coupon-Usage-Forecast resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1419 stars · 835 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 Alibaba Tianchi O2O Coupon Usage Forecast competition, written in Python. It provides feature engineering pipelines and XGBoost/GBDT/RandomForest model ensembling for predicting whether users will redeem coupons within 15 days.

Use cases

• predict whether users will redeem coupons after claiming them
• learn feature engineering techniques for tabular competition data
• study a winning machine learning competition solution
• build a coupon redemption prediction model with xgboost
• learn how to ensemble gradient boosting models
• understand data leakage exploitation in competition settings

When to choose

• you want to study a top-ranked competition solution for tabular prediction
• you need reference feature engineering ideas for user-merchant interaction data
• you are learning XGBoost and model ensembling on real-world e-commerce data

When to avoid

• you need production-ready, maintained software with a license
• you want a general-purpose coupon marketing tool rather than competition code
• you need features that are valid in real business settings, since some exploit competition leakage

Facets

learning-resource · maturity maintenance

machine-learning data-science machine-learning data-science e-commerce python kaggle-competition xgboost feature-engineering coupon-redemption tianchi gradient-boosting recommender-systems

Member repositories

For agents

markdown · JSON · MCP: product_card(name="wepe/O2O-Coupon-Usage-Forecast")