RetailHero Uplift Modeling

Uplift-modelling task. Rank the customers in descending order of communication efficiency.

The competition page:

• https://retailhero.ai/c/uplift_modeling/
• https://ods.ai/competitions/x5-retailhero-uplift-modeling/data

The data:

• data/clients.csv — clients information
• data/products.csv — products information
• data/purchases.csv — purchase history before sms advertisement
• data/uplift_train.csv — train set of clients with conversion information
• data/uplift_test.csv — test set to model the uplift

Solutions:

• Baseline solution: uplift_solution.py
• Final solution (top-100): create_dataset.py, pipeline.ipynb

The libraries needed to run the code are listed in requirements.txt.

Reference materials:

• https://github.com/maks-sh/scikit-uplift
• https://github.com/olegitor/X5.Uplift.public
• https://github.com/nersirion/nersirion-RetailHero.ai-uplift
• https://github.com/rekcahd/retailhero_uplift

Tutorials on uplift-modeling (in Russian):

• Туториал по Uplift моделированию. Часть 1
• Туториал по Uplift моделированию. Часть 2