Movie Recommendation System using Collaborative Filtering

4.4
별점
43개의 평가
제공자:
Coursera Project Network
1,778명이 이미 등록했습니다.
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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
Beginner초급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

Data ScienceCollaborative FilteringMachine LearningPython ProgrammingRecommender Systems

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.