기본적인 Recommendation System을 갖춘 REST API 서버와, 이를 표현하기 위한 간단한 프론트엔드를 구현한 페이지입니다.

Using the MovieLens dataset with Surprise to compare different algorithms for rating prediction, and also create a movie recommendation system on top of it.

This Project is a simplifed Movie Recommendation System

Recommender system with Netflix database using matrix factorization

Use of Surprise Package in Python for Recommender System

Recommender system that applies a user-to-user collaborative filtering algorithm on the MAL dataset to recommend anime for users.

Workshop Surprise para a disciplina MATE65 (UFBA)

A generalized items recommender that lists out top 5 recommendations based on users with similar choices.

A Collaborative filtering recommendation system

Welcome to the Machine Learning Basics repository! This repository is dedicated to showcasing my journey through learning the fundamentals of machine learning. You'll find various datasets, Jupyter notebooks, and source code that I have worked on.

An overview of reccomendation systems in Python

This repository contains the source code and documentation for a Bachelor's thesis project that explores two different approaches to developing a movie recommendation system.

Repository to demonstrate how to use machine learning to generate recommendations

Projeto Final de Graduação - Engenharia de Computação UNICAMP 2019. Revisão de Ténicas em Sistemas de Recomendação.

🛍️ Amazon Recommender Study 🚀 A Python exploration into machine learning for e-commerce personalization, using Amazon's Electronics data. Investigates algorithms like SVD, KNNBaseline for predicting user preferences, offering insights into future shopping enhancements

The goal of this project was to build an explicit recommender system using collaborative filtering for restaurants in Charlotte using Yelp's Open Dataset. I wanted to explore the mechanics of recommendations systems, and explore a new library in Surprise.