The site offers movie recommendations based on user and item-based collaborative filtering, utilizing other users' ratings to provide personalized suggestions on the website.

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

This repository contains all my Machine Learning projects.

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'.

CineBrain uses ML & NLP to analyze movies and recommend similar ones based on user preferences through cosine similarity.

Movie recommendation system using Machine learning

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

🎓 🍿 🎬 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.

Recommendation Algorithm Using Artificial Neural Networks and Data Clustering Techniques, for the purpose of the Pattern Recognition course of an MSc Program on Informatics.

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.

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.

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.

Movie96

A Movie Recommendator built using Neural Collaborative Filtering

This is a Movie Recommendation System built using the Streamlit by using Python. This project uses Hybrid CBF Weighted Sum Approach for movie keywords and Genres to make personalized movie recommendations to the end user.

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.

Movie Recommendation System using Bag of Words, TMDB API, Streamlit, and Dialogflow chatbot for personalized movie suggestions.

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.