Movie Recommendation System using Collaborative Filtering

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Coursera Project Network
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在此指導項目中,您將:

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
Beginner初級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

Data ScienceCollaborative FilteringMachine LearningPython ProgrammingRecommender Systems

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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