surprise-python
There are 56 repositories under surprise-python topic.
prakruti-joshi/Movie-Recommendation-System
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
klaudia-nazarko/collaborative-filtering-python
This repository contains collaborative filtering recommender system build in Python with surprise package to predict book ratings in Book-Crossing dataset.
melodygr/grocery_recommendation
Grocery Recommendation on Instacart Data
amanjeetsahu/Recommender-Systems-Using-Python
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.
artisan1218/Recommendation-System
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
aliceagrawal/HM-Recommender-System-App
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.
ruxandraburtica/recommender-systems
Getting a better grasp of recommender systems
singhsidhukuldeep/Recommendation-System
Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering
JuliaRecsys/Surprise.jl
Suprise-Python Wrapper for Persa.jl
unclebrod/YelpRecommender
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.
andreamanzini/RecommendationSystem
Machine Learning homework project at EPFL
camcochet/iGSLR-Personalized-Geo-Social-Location-Recommendation
Implementation of the model iGSLR
mohamedmansoura/Recommender-system-with-Netflix-database
Recommender system with Netflix database using matrix factorization
roweyerboat/Recommendation_System_MovieLens
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 addressed by giving new users the opportunity to rate a random sample of 5 movies from movies that are among the most popular.
sheetalbongale/Recommender-Systems-Machine-Learning
Exploring Recommender Systems using various Machine Learning Models like scikit-learn, Surprise, NLP and collaborative filtering using KNN and Tensorflow.
hmc-cs-azhao/158-recommender-system
Recommender system that applies a user-to-user collaborative filtering algorithm on the MAL dataset to recommend anime for users.
jacobceles/Movie-Recommendation-Rating-Prediction
Using the MovieLens dataset with Surprise to compare different algorithms for rating prediction, and also create a movie recommendation system on top of it.
Luis-Palacios/movie-recommendations
Repository to demonstrate how to use machine learning to generate recommendations
SAKET-SK/Building-a-Recommendation-Engiene-Course
Building a Recommendation engine course walkthrough. IDE used :- Spyder ; Environment name :- RecSys (created in Anaconda Navigator) ; Python Package used :- Surprise ; Tutor :- Frank Kane, Sundog Education
shaina-12/Movie-Recommendation-System
This Project is a simplifed Movie Recommendation System
azury74/HotelRecomendationSystem
This program combines several recommendation approaches in order to predict and display to users recommendations of hotels located in the Paris area.
Dacker15/python-reccomendation
An overview of reccomendation systems in Python
danielchristopher513/Hybrid_Movie_Recommendation
The Hybrid Movie Recommender is a system that recommends movies using a combination of collaborative and content-based filtering techniques. The system is designed to address the cold start problem(new users) by using a popularity based approach. The dataset used for the system is obtained from Kaggle.
didizhx/Recommendation-Tools-Project
The goal of this project is to develop recommendation systems for amazon reviews dataset using Surprise package. This project demonstrated the application of 6 recommendation systems, as well as the preprocessing steps needed to apply the methods.
Farzanmrz/recommender-amazon-reviews
๐๏ธ Amazon Recommender Study ๐ A Python exploration into machine learning for e-commerce personalization, using Amazon's Electronics data. Investigates algorithms like SVD, KNNBaseline for predicting user preferences, offering insights into future shopping enhancements
Gruz2520/rec_system_collab_filtering
Collaborative filtering based recommender system using the surprise library
MDanish99/Recommender_System
Use of Surprise Package in Python for Recommender System
tanyakuznetsova/Music_Mental_Health
Harnessing music's power for better mental health: genre recommendations and data-driven analysis of listeners' trends
TEAM-URS/Hybrid-Recsys
์ธ์ฐ ๋ง์ง,์นดํ/๋ช ์ ์ถ์ฒ ์์คํ
timbergrizz/simple_movie_recommendation_service
๊ธฐ๋ณธ์ ์ธ Recommendation System์ ๊ฐ์ถ REST API ์๋ฒ์, ์ด๋ฅผ ํํํ๊ธฐ ์ํ ๊ฐ๋จํ ํ๋ก ํธ์๋๋ฅผ ๊ตฌํํ ํ์ด์ง์ ๋๋ค.
emanuelneziraj/recommenders_gai_surprise
This repository contains the source code and documentation for a Bachelor's thesis project that explores two different approaches to developing a movie recommendation system.
FREDRICKKYEKI/Data-Science-Phase-4-Project
Phase 4 project of the Flat Iron curriculum of Data Science in Moringa School
giulio-derasmo/Page-Rank-and-Recommendation-Systems
Use the Scikit-Network for PageRank algorithms including Topic-specific PR and improve the performance of various recommendation-systems using Surprise library
thislawyercodes/Film-Recommendation-system
A Collaborative filtering recommendation system
Vignesh010101/Product-Recommendation-System
A simple Product Recommendation System.