Personalizing Discounts you own this product

prerequisites
basics of Python • basics of data science
skills learned
implement causal models for personalization • evaluate the performance of your causal model using experimental data
Matheus Facure
1 week · 6-8 hours per week · INTERMEDIATE
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In this liveProject, you’ll use causal inference to investigate data on randomized discounts and determine if an e-commerce company should offer personalized discounting. You’ll estimate a different treatment effect for each customer in the hopes to see if some are positive, and figure out which customers should get what discounts. You’ll utilize Python and machine learning to build this personalization system, and implement a causal model for personalization.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Matheus Facure
Matheus Facure is an economist, data scientist and causal inference specialist at Nubank. He currently works with adding causal inference capabilities to machine learning models with application in the credit card business, and acts as a consultant for other business areas inside Nubank, such as personal loans and marketing.

prerequisites

This liveProject is for data scientists with knowledge of Python, machine learning, and statistics. To begin this liveProject you will need to be familiar with the following:


TOOLS
  • Basics of Python
  • Basics of pandas
  • Basics of Matplotlib
  • Basics of NumPy
TECHNIQUES
  • Basics of data science and machine learning
  • Basics of causal inference

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