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.

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'.

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.

A content-based recommender system that recommends movies similar to the movie the user likes and analyses the sentiments of the reviews given by the user Topics

Recommendo is a movie recommendation system developed using Flask, HTML, Bootstrap, JavaScript, and MySQL. The system allows users to search for movie recommendations and get personalized suggestions based on their favorite movies.

This projects serves as a web app to showcase the recommendation engine project

Movie recommendation system using Machine learning

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.

FilmFinder is a personalized movie recommendation system that suggests films based on user preferences using machine learning algorithms. Integrated into a Streamlit web-app, it offers detailed insights on over 5,000 movies, including summaries, ratings, and genres, to enhance user viewing choices.

This Repo contains The Implementation of Content-Based Recommended System and Collaborative Filtering Recommended System for movies dataset in python.

This project is a Movie Recommendation System that suggests movies to users based on their input of a favorite movie. It uses Cosine Similarity and TF-IDF Vectorizer to compute similarity between movies based on features like genres, keywords, cast, crew, and more.

FLIX-HUB is a movie recommendation system utilizing the Netflix dataset. It features comprehensive data preprocessing and analysis, generating personalized movie and TV show suggestions based on TF-IDF vectorization and cosine similarity. The project includes interactive visualizations for insights into content trends and distributions.

Movie Recommendation System is a web application designed to provide personalized movie recommendations to users based on their input movie titles.

This repository contains a robust and user-friendly movie recommendation system built to enhance your movie-watching experience. Whether you're a film enthusiast or just looking for something new to watch, our system has you covered topics

This project it's a movie recommendation engine composed of a custom NCF model to predict user preferences and generate personalized recommendations for large-scale user-item interaction data.

Movie96

🎓 🍿 🎬 Final project for the Numerical Analysis for Machine Learning course at Politecnico di Milano.

A system incorporating collaborative, content-based, and hybrid techniques to offer personalised movie recommendations, thereby improving the overall user experience.