Build a movies recommendation system clone using Movielens dataset to construct recommendation system such as Simple recommender, Content based recommender (based on movie description and metadata) , Collaborative-Filtering based recommender , and a Hybrid recommender system.

To recommend the next 10 movies to the user using the Prized Dataset provided by Netflix - over the span of 10 days for Capstone Project.

A book recommendation system using model based collabritive filtering. It is based on SVD machine learning model. It generate top 10 recommendation of books.Here i used surprise library.

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Système de recommandation

Recommendation Systems tutorial

Evaluating and comparing recommender system models using MovieLens-Ratings dataset

This project focuses on predicting Loan Defaults using Supervised Learning, Segmenting Customers with Unsupervised Learning, and Recommending Bank Products through a Recommendation Engine.

Predict user rating for a netflix movie.

Did you ever wonder how the recommendations on Netflix work? Find out in this project, where I build three basic movie recommenders and implement them in a streamlit App.

A Book Recommender System: Collaborative Filtering using Surprise (k-NN Baseline model)

Elice Generative AI Edu Hackaton TEAM10 <AI오늘>

I created a recommender system using a Python scikit named Surprise. The purpose of building this system is to predict a person's preferences so the user can find what they are looking for faster.

The project used Python to create a personalized book recommendation system that analyzed users' past ratings on books to identify their preferences and patterns and suggested books that the user is likely to enjoy but has not read yet.

Use the Scikit-Network for PageRank algorithms including Topic-specific PR and improve the performance of various recommendation-systems using Surprise library

Built a movie recommender system using Movielens dataset using both content-based filtering approach and collaborative filtering method.

Recommendation engine in Surprise that populates movie recommendations for users based on their existing preferences.

Machine Learning - Recommendation System