Personalized book recommender system to generate top 5 recommendations to users

This project aims to build & optimise a book recommendation system based on collaborative filtering and will tackle an example of both memory based & model based approach (using KNNWithMeans & Singular Value Decomposition)

This repo contains my practice and template code for all kinds of recommender systems using SupriseLib. More complex and hybrid Recommender Systems can build on top of these template codes.

Food Finder: An interface for a multi-user recommendation system.

Machine Learning Music Recommendation System: Hybrid Approach (Content & SVD) with Flask

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

A machine learning recommendation engine using Collaborative Filtering and Matrix Factorization on the MovieLens dataset.

Adaptive Learning Recommendation System using collaborative filtering to suggest personalized learning materials based on student engagement and performance metrics.

Common Machine Learning Examples 💻

A Wine Recommender System (My Capstone project for Data Science Immersive course)

🎵 SVD-based collaborative filtering recommender system — 100K MovieLens ratings, 3-fold cross-validation, RMSE/MAE evaluation, Top-N personalized recommendations, and structured logging. Built with Python & scikit-surprise.

This project is a movie recommender engine written using Python and the Scikit-Surprise libraries to generate better movie recommendations by utilizing temporal user interactiondata.

Movie Recommendation using SVD Algorithm

RECOMMENDER for films >= 2010. Uses Scikit-Surprise.

A repository containing various projects and microprojects.

Manga Recommender Systems based on Anilist API

A collaborative filtering recommendation system built with Python, Pandas, and Scikit-Surprise.

RECOMMENDER for allrecipes.com. Using Scikit-Surprise

movie recommendation system using python and ml

Python Recommendation System