Movie Recommendation System project developed in Java using Spring Boot and MySQL.

Pick Me A Flick: A content filtering based Movie Recommendation Engine .

A movie recommender written in Go that suggests movies considering various factors within a particular dataset, encompassing users, movies, and movie ratings.

Movie Recommendation System using Python

Application of Apriori algorithm for a move recommendation system

This project aims to provide movie recommendations based on user input and suggests movies that are similar in content or style to the one provided by the user, enhancing their movie-watching experience. 'cosine_similarity' function from sklearn is used to calculate the similarity between the user's input movie name and the movies in the dataset.

Movie Recommendation System using Python, Machine learning and Streamlit

Code repository for a sample end-to-end project for a Movie Recommender System built on Python Flask as backend

Movie Recommender System - MovieMiner API Deployment on Render built on Python Flask as backend and React for frontend with deployment on Netlify

Content based movie recommendation ml project.

🎥 Personalized movie recommendations using content-based filtering

An AI driven Python web application which recommends movies based on user's search result. It implements a ML model based on sentiment analysis and content based filtering. The application uses Streamlit to create interactive web user interface.

This code and data create a movie recommender system using content based filtering and cosine similarity. It uses the features of movies (genre, crew, etc.) to find and suggest similar movies to users.

A content-based movie recommendation system that recommends movies based on user preferences using cosine similarity.

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

Dive into this GitHub repository showcasing an Adaptive Website System coursework project. Implemented content filtering, collaborative filtering, and hybrid filtering techniques to enhance user experience through personalized recommendations. Explore well-documented code, datasets, and guides for seamless integration. Elevate your understanding

Build a machine learning model that can recommend movie based on user preference.

It is a content based movie recommendations web app. Based on the user input, it recommends similar movies/webseries to the user using machine learning.