Personalized book recommender system to generate top 5 recommendations to users

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

Python Recommendation System

Common Machine Learning Examples 💻

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.

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

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

Simple Recommender System built using the Scikit Surprise Library. The application provides recommendations based on Amazon Products Reviews.

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)

Recommend books using various machine learning algorithms.

The aim of this project is to build a recommendation app. using Collaborative Filtering/ Content based models , scikit-surprise library, Azure function as API and Flask

A collaborating filtering based system on Movie Lens dataset to recommend user specific movie suggestions. The model was evaluated with recommedation specific metrics including Long Tail plot using 'surprise' library.

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

Movie Recommendation using SVD Algorithm

RECOMMENDER for allrecipes.com. Using Scikit-Surprise

A repository containing various projects and microprojects.

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.

This project aims to build an advanced book recommendation system by integrating collaborative filtering, content-based filtering, and machine learning. It offers tailored suggestions based on user preferences and interactions, using EDA for insights and cosine similarity and SVD for precise recommendations.

Pantry-based recipe recommendations.