Project is Music Recommender System using Apache Spark and Python. Project suggests different songs or musical artists to a user. This is important to many music streaming services, such as Pandora and Spotify. In addition, this type of recommender system could also be used as a means of suggesting TV shows or movies to a user (e.g., Netflix).

Project to build a Shiny app that randomly suggests ONE viewing suggestion, based on genre preference and critics meta-score.

A Netflix content-based recommendation system using NLP, dimensionality reduction (UMAP), and clustering (HDBSCAN) to group similar titles and suggest relevant shows/movies.

Based on a user's preferred movie or TV show, Unsupervised Machine Learning-Netflix Recommender suggests Netflix movies and TV shows. These suggestions are based on a K-Means Clustering model. These algorithms base their recommendations on details about movies and tv shows, such as their genres and description.

Netflix clone using the MERN stack with a responsive UI designed with Tailwind CSS. Implemented user authentication with JWT, enabling features such as movie and TV show browsing, actor searches, and trailer viewing. The application supports search history tracking and suggests similar content

A Netflix recommendation wizard that uses machine learning to suggest movies and TV shows you'll love.

Netflix Recommendation Model – A machine learning-based recommendation system that predicts and suggests movies/shows based on user preferences

Netflix offers thousands of movies and TV shows, which can make it hard for users to decide what to watch. To solve this problem, this project focuses on creating a movie recommendation system that suggests movies based on user preferences.

A chat bot that suggests Netflix shows and movies by genre actor or mood

A recommendation system using Linear Kernel method to suggest netflix movies and shows based on your recent watches.

Doing an EDA on Netflix dataset and using a clustering algorithm creating an application suggesting movies and tv shows

A Netflix show recommender built with Streamlit that uses cosine similarity to suggest similar titles based on textual features.

NetFlix-GPT is a web app that uses the Open.ai feature to search movies name in NetFlix Search bar and show suggested more results using TMDB API.

A simple Netflix recommendation system that suggests movies/shows based on genre similarity using Pandas, NumPy, ML, TF-IDF and cosine similarity.

A Netflix Clone is a full-stack web application that mimics the core features of Netflix, enhanced with an AI-based recommendation system that suggests movies/shows based on user preferences and behavior.

"A Content-Based Movie Recommendation System using Python, TF-IDF Vectorization, and Cosine Similarity to suggest Netflix movies and TV shows based on user preferences."

Analyzes the Kaggle Netflix dataset and builds a content-based recommender to suggest similar movies and TV shows based on plot descriptions, genres, and titles.

A simple content-based recommendation system for Netflix movies and TV shows using cosine similarity on genres and descriptions to suggest similar titles from a given dataset.

A machine learning–based Netflix movie recommendation system built with Python and Streamlit. Uses TF-IDF and cosine similarity to suggest shows similar to user input.

A Netflix-style content recommendation system that suggests movies and TV shows based on user age, genre preferences, and content maturity using a Flask backend and a modern JavaScript frontend.

Netflix GPT is a React-based, AI-powered recommendation platform that suggests movies and TV shows based on user mood and preferences. It features a Netflix-inspired UI, secure authentication, and a GPT-powered search for natural language content discovery

A Netflix Recommendation System that suggests movies and TV shows based on user preferences. It uses collaborative and content-based filtering techniques to predict what users might enjoy next, offering a personalized experience similar to Netflix's own recommendation engine.

Performed EDA on Netflix dataset and built a content-based recommendation system using K-Nearest Neighbors (KNN) to suggest similar movies/shows. Includes preprocessing, feature engineering, and model evaluation.

It is a frontend application where using of debouncing which is a concept of clousers when searching of anny movie name it shows or suggest some other movies related to that particukar movie as shows in hotstar or netflix

A personalized recommendation system designed for Netflix's movie and TV show catalog. By leveraging clustering algorithms and unsupervised learning techniques, the system groups similar titles and suggests content based on a user's preferred movie or TV show.

This project is to design and develop a recommendation engine using Netflix data. The engine will suggest personalized TV shows and movies to users based on their viewing history and preferences.

A content-based recommendation system built with Flask that suggests Netflix movies and shows based on user input. This project leverages machine learning techniques to analyze movie metadata and provide personalized recommendations.

The purpose of a recommender system is to suggest users something based on their interest or usage history. So next time Amazon suggests you a product, or Netflix recommends you a tv show or medium display a great post on your feed, understand that there is a recommendation system working under the hood.

This project provides a content-based recommendation system for Netflix shows and movies. The system suggests similar titles based on user input, utilizing features such as title, director, cast, country, genre, and description.

This app pairs The Movie DB database with availability confirmation on Netflix (using Streaming Availability). The top section of the app will suggest a random movie, the bottom section will show streaming availability.