Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.

This repository contains collaborative filtering recommender system build in Python with surprise package to predict book ratings in Book-Crossing dataset.

Grocery Recommendation on Instacart Data

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.

Built a collaborative filtering and content-based recommendation/recommender system specific to H&M using the Surprise library and cosine similarity to generate similarity and distance-based recommendations.

Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering

Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity

Getting a better grasp of recommender systems

Suprise-Python Wrapper for Persa.jl

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.

X-stupidity is a surprise tool which you will know the contents of when you install it

Implementation of the model iGSLR

Recommender system with Netflix database using matrix factorization

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

Machine Learning homework project at EPFL

Exploring Recommender Systems using various Machine Learning Models like scikit-learn, Surprise, NLP and collaborative filtering using KNN and Tensorflow.

This repository covers a project of creating a recommendation system using collaborative filtering on the Grouplens movielens database. The surprise library is utilized to test out different models (KNN Basic, KNN Baseline, and SVD). SVD was found to be the most accurate and then was implemented into the system. The cold start problem was addres…

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