anishjohnson/NETFLIX-MOVIES-AND-TV-SHOWS-CLUSTERING
This project is a part of "Unsupervised Machine Learning” curriculum as capstone projects at AlmaBetter School
This project helps content strategists and market researchers understand the Netflix catalog. By analyzing Netflix movie and TV show data, it identifies content trends across different countries and over time, and groups similar titles based on their descriptions. It takes raw Netflix content data and provides insights into content distribution, genre trends, and clusters of similar shows or movies.
No commits in the last 6 months.
Use this if you need to quickly understand thematic groupings within a large content library or analyze content strategy trends over time and by region.
Not ideal if you need real-time recommendations or a production-ready content recommendation system for end-users.
Stars
9
Forks
3
Language
Jupyter Notebook
License
—
Category
Last pushed
Mar 28, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/anishjohnson/NETFLIX-MOVIES-AND-TV-SHOWS-CLUSTERING"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
asif536/Movie-Recommender-System
Basic Movie Recommendation Web Application using user-item collaborative filtering.
victorverma3/Letterboxd-Movie-Recommendations
Generate AI-powered movie recommendations, discover insightful profile statistics, pick movies...
snowch/movie-recommender-demo
This project walks through how you can create recommendations using Apache Spark machine...
kishan0725/AJAX-Movie-Recommendation-System-with-Sentiment-Analysis
A content-based recommender system that recommends movies similar to the movie the user likes...
skotz/cp-user-behavior
Recommendation engine using collaborative filtering and matrix factorization