An advanced "content-based filtering" movie recommendation system built with Python, scikit-learn, and SQLite. It provides personalized movie suggestions based on user preferences through data analysis, and also allows users to search by a specific movie title to find similar recommendations.

A social platform for movie enthusiasts to explore, discuss, and review films. Designed to be your one-stop destination for all movie-related needs, offering a superior user experience and unparalleled depth of content.

Tvflix is a simple and responsive web app built using Vanilla JS, leveraging the power of Postman and the TMDB API to seamlessly fetch and display comprehensive movie details. This project serves as a template for larger applications.

The 'MOVICO' project is a 'Movie Recommendation System'. It is an 'Artificial Intelligence-Machine Learning' project. Specifically, it is a 'Movie Recommendation System' that uses 'Collaborative Filtering Techniques'. The project 'Movie-Recommendation-System-MOVICO' was created as a project for the course 'Machine Intelligence', 'ue20cs302'.

A modern movie recommendation web app built with React, TypeScript, and Material-UI. Discover movies, search by genre, and get details instantly.

🎬 It helps you discover films. Search for your favorite movies, get a "Surprise Me" pick, and explore trending movies—all while viewing live details like posters, trailers, ratings, and cast information.

RUMORS is a framework designed to implement RESTful APIs for a Recommender System using the MovieLens dataset. The system provides personalized movie recommendations based on user preferences and behavior.

🎬 FastAPI-based Movie & TV Show Recommender using TMDb API 🎱| Search movies, get recommendations & browse top-rated content!

Absolute Cinema

Full-stack machine learning project that predicts viewer satisfaction (high ratings) on Netflix using demographic data and TMDB movie metadata. Includes EDA, XGBoost modeling, and real-time enrichment using the TMDB API.

This React app, named usePopcorn, is a movie search and watchlist manager built using: OMDb API for fetching movie data Custom Hooks for reusability (useMovie, useKey, useLocalStorageState) Components for modular UI (e.g., Navbar, SearchBar, SelectedMovies, Watchsummary)

The Movie Information App is a Flutter-based mobile application that provides movie recommendations by utilizing the TMDB (The Movie Database) API. The app allows users to explore movies, view detailed information, and get recommendations based on their interests.

A machine learning-based movie recommendation engine trained on 25 million ratings from 15,000 users. Utilizes collaborative filtering and matrix factorization to predict user preferences and deliver personalized movie suggestions.

AI powered movie recommendation system using vector search and cosine similarity. The system takes user input in the form of a movie title or overview and returns the most similar movies based on their embeddings.

🎥 Cine Suggest – Your Personalized Movie Companion CineSuggest helps you discover films you'll love based on what you already enjoy. Powered by intelligent recommendations and TMDB data, it's your perfect guide to movie nights.

An AI-powered system that classifies Netflix titles (Movie vs. TV Show) and recommends similar content using both TF-IDF and Transformer-based semantic models. Built with Python, scikit-learn, Sentence Transformers, and Gradio for an interactive UI.

🎬 A content-based movie recommendation system using NLP (CountVectorizer + Cosine Similarity) on TMDB 5000 dataset. Built with Python, scikit-learn, and Streamlit.

this project is a movie recommendation system that combines multiple algorithms to provide personalized movie suggestions. The system utilizes content-based filtering, collaborative filtering, neural collaborative filtering, and gradient boosting techniques to generate accurate and diverse recommendations.

Simple and interpretable recommender system using cosine similarity between movie vectors.