Recommendation System

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This project is part of the liveProject series Recommendation System with Surprise and Fast.ai
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

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Look inside

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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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

you will learn

In this liveProject, you’ll learn how to put collaborative filtering techniques into action to create recommendation engines, one of the most useful types of machine learning application. You’ll become familiar with the workflow of a professional data scientist.


  • Selecting, cleaning and choosing data for collaborative filtering
  • Neighborhood based Collaborative Filtering techniques
  • Model based Collaborative Filtering techniques

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While within the liveProject platform, get help from other participants and our expert mentors.
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For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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