Movie Recommendation System using Collaborative Filtering

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Coursera Project Network
In this Guided Project, you will:

Learn to create, train and evaluate a recommendation engine with Scikit-Surprise

Learn to clean, analyse and use real-word datasets for recommendation systems

Clock1 hour 25 minutes
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

With the amount of available online content ever-increasing and all the platforms trying to grab your attention by giving you personalized recommendations, recommendation engines are more important than ever. In this project-based course, you will create a recommendation system using Collaborative Filtering with help of Scikit-surprise library, which learns from past user behavior. We will be working with a movie lense dataset and by the end of this project, you will be able to give unique movie recommendations for every user based on their past ratings. This project is best suited for anyone who is venturing into data science and is curious as to how recommendation engines work. This project will be a great addition to your portfolio to showcase your real-world hands-on experience with recommendation systems as we would be working with a real-world dataset.

Skills you will develop

Data ScienceCollaborative FilteringMachine LearningPython ProgrammingRecommender Systems

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Set up required modules and get them ready for use. Become familiar with the guided project interface

  2. Import real-world dataset and clean it

  3. Do exploratory data analysis on the dataset

  4. Remove the unwanted ratings from the dataset and thus do Dimensionality Reduction

  5. Create trainset and antiset from the data

  6. Train your model on your data and see its performance

  7. Make predictions and recommend the best movies for each user

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.