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prerequisites
basics of Python • basics of pandas • basics of scikit-learn • basics of machine learning
skills learned
selecting, cleaning and choosing data for collaborative filtering • neighborhood and model based collaborative filtering techniques with the Surprise library
Ariel Gamino
1 week · 6-8 hours per week · BEGINNER
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In this liveProject, you’ll create a product recommendation engine for an online store using collaborative filtering techniques from the Surprise library. You’ll work with Amazon review datasets to create your data corpus, and identify which would be best for a collaborative filtering recommender. You’ll then use two different approaches—neighbourhood-based and matrix factorization—to implement different solutions to the rating matrix completion problem. You’ll learn how to select and clean the necessary data for these different approaches. When you’re finished, you’ll have built a system that can predict the rating for a product a user has not yet purchased.

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

prerequisites

This liveProject is for beginner Python data scientists interested in creating recommendation engines. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Basics of Pandas and dataframe filtering and manipulation
  • Basics of scikit-learn
TECHNIQUES
  • Basics of machine learning

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