Movie Recommendation System using Collaborative Filtering
Learn to create, train and evaluate a recommendation engine with Scikit-Surprise
Learn to clean, analyse and use real-word datasets for recommendation systems
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
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:
Set up required modules and get them ready for use. Become familiar with the guided project interface
Import real-world dataset and clean it
Do exploratory data analysis on the dataset
Remove the unwanted ratings from the dataset and thus do Dimensionality Reduction
Create trainset and antiset from the data
Train your model on your data and see its performance
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
Reviews
TOP REVIEWS FROM MOVIE RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERING
Off-line Practice is need for lesser equipped users.
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