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.

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.

Common Machine Learning Examples 💻

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.

🎵 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.

movie recommendation system using python and ml

Python Recommendation System

Pantry-based recipe recommendations.

Advanced recommendation system for e-commerce applications.