Double Machine Learning Approach you own this product

prerequisites
basics of Python • basics of data science and machine learning
skills learned
pandas to manipulate tabular data • linear regression for causal inference • use machine learning to make causal models for personalization
Matheus Facure
1 week · 6-8 hours per week · INTERMEDIATE
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In this liveProject, you’ll utilize machine learning for treatment effect estimation in order to help an e-commerce company deliver targeted discounting to the most profitable customers. You’ll build a model that predicts the effect of a discount on a customer following a causal model, maximizing profits for the business.

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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