surprise-python

There are 1 repository under surprise-python topic.

grocery_recommendation
melodygr / grocery_recommendation
Grocery Recommendation on Instacart Data
market-basket-analysis surprise-python nlp-machine-learning countvectorizer cosine-similarity clustering kmeans-clustering collaborative-filtering content-based-recommendation flask
Language:Jupyter Notebook 23
prakruti-joshi / Movie-Recommendation-System
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
recommendation-system movie-recommendation surprise-python collaborative-filtering content-based-recommendation svd-matrix-factorisation movielens-dataset hybrid-recommender-system
Language:Jupyter Notebook 23
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.
collaborative-filtering recommender-system recommendation-system recommendation-engine python book-crossing book-recomendation book-recommender surprise surprise-python surprise-library data-science data-analysis exploratory-data-analysis machine-learning
Language:Jupyter Notebook 22
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.
collaborative-filtering content-based-recommendation evaluation hybrid-recommender-system jupyter-notebooks python python3 recommender-system scikit-surprise surprise-library surprise-python
Language:Python 8
artisan1218 / Recommendation-System
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
jaccard-similarity cosine-similarity tfidf collaborative-filtering content-based-recommendation user-based-recommendation item-based-recommendation spark pearson-correlation tf-idf-score similar-items svd-matrix-factorisation xgboost xgboost-regression surprise-python feature-augmentation upsampling
Language:Python 7
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.
cosine-similarity jupyter-notebook modeling non-negative-matrix-factorization python python-3 python3 rfm-analysis surprise-python svd svdpp
Language:Jupyter Notebook 6
ruxandraburtica / recommender-systems
Getting a better grasp of recommender systems
recommender-systems machine-learning collaborative-filtering surprise-python tf-idf
Language:Jupyter Notebook 5
singhsidhukuldeep / Recommendation-System
Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering
recommender-system recommendation-system recommendation-engine recommendation recomendation-algorithm collaborative-filtering matrix-factorization svd svd-factorization surprise-python surprise
Language:Jupyter Notebook 5
JuliaRecsys / Surprise.jl
Suprise-Python Wrapper for Persa.jl
julia recommender-systems collaborative-filtering surprise-python recsys persa
Language:Julia 3
YelpRecommender
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.
yelp-dataset reviews cold-starts scores recommender recommendation-system explicit surprise surprise-python collaborative-filtering svd-recommendation-algorithm charlotte restaurants
Language:Jupyter Notebook 3
andreamanzini / RecommendationSystem
Machine Learning homework project at EPFL
data-science epfl kaggle-competition machine-learning machine-learning-algorithms netflix-prize recommendation-system recommender-system surprise-python university-project
Language:TeX 2
camcochet / iGSLR-Personalized-Geo-Social-Location-Recommendation
Implementation of the model iGSLR
recommender-system surprise-python
Language:Jupyter Notebook 2
mohamedmansoura / Recommender-system-with-Netflix-database
Recommender system with Netflix database using matrix factorization
matrix-factorization recommender-system recommendation-engine recommendation-system recommendation-algorithms surprise-python collaborative-filtering user-item-utility-matrix netflix netflix-prize rating-prediction svd-matrix-factorisation latent-factor-model
Language:Jupyter Notebook 2
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.
movielens ratings users-ratings movielens-data surprise-library surprise-python movies
Language:Jupyter Notebook 2
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.
collaborative-filtering cosine-similarity knn-model nlp recommender-system scikit-learn surprise-python
Language:Jupyter Notebook 2
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.
anime collaborative-filtering machine-learning mal-dataset myanimelist python python3 recommender-system scikit-surprise surprise-python
Language:Python 1
Movie-Recommendation-Rating-Prediction
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.
machine-learning movie-recommendation python rating-prediction surprise-library surprise-python modin
Language:Jupyter Notebook 1
Luis-Palacios / movie-recommendations
Repository to demonstrate how to use machine learning to generate recommendations
anaconda jupyter-notebook pandas python scikit-learn surprise-python
Language:Jupyter Notebook 1
prabhj / Smm_Recommendation_System
recommender-system python3 collaborative-filtering community-detection girvan-newman clustering-algorithm surprise-python data-mining mario-maker
Language:Jupyter Notebook 1
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
python anaconda recommender-system spyder machine-learning artificial-intelligence dataset surprise-python jupyter-notebook tutorial
Language:Jupyter Notebook 1
shaina-12 / Movie-Recommendation-System
This Project is a simplifed Movie Recommendation System
machine-learning data-analytics python3 surprise-python
Language:Jupyter Notebook 1
azury74 / HotelRecomendationSystem
This program combines several recommendation approaches in order to predict and display to users recommendations of hotels located in the Paris area.
flask recommendation-system surprise-python
Language:HTML 0
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.
colloborative-filtering content-based-recommendation hybrid-recommender-system movie-recommendation python recommender-system surprise-python
Language:Jupyter Notebook 0
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.
python python-programming surprise-python machine-learning recommender-system pandas-python
Language:Jupyter Notebook 0
Farzanmrz / recommender-amazon-reviews
🛍️ Amazon Recommender Study 🚀 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
ai-algorithm data-science machine-learning recommender-system surprise-python
Language:Jupyter Notebook 0
forestinblue / Recommendar-System-Restaurant
Collaborative , Contents Filtering Recommdar System
surprise surprise-python tf-idf collaborative-filtering content-filtering
Language:Jupyter Notebook 0
Gruz2520 / rec_system_collab_filtering
Collaborative filtering based recommender system using the surprise library
collaborative-filtering kaggle-dataset plotly recommender-system surprise-python
Language:Jupyter Notebook 0
MDanish99 / Recommender_System
Use of Surprise Package in Python for Recommender System
recommender-system surprise-python
Language:Jupyter Notebook 0
tanyakuznetsova / Music_Mental_Health
Harnessing music's power for better mental health: genre recommendations and data-driven analysis of listeners' trends
data-visualization decision-tree decision-tree-classifier exploratory-data-analysis k-means-clustering pca-analysis recommendation-system recommender-system surprise-python
Language:Jupyter Notebook 0
TEAM-URS / Hybrid-Recsys
울산 맛집,카페/명소 추천 시스템
bs4 gensim python3 recommendation-system scikit-learn selenium surprise-python tensorflow2 konlpy matplotlib numpy pandas
Language:Jupyter Notebook 0
timbergrizz / simple_movie_recommendation_service
기본적인 Recommendation System을 갖춘 REST API 서버와, 이를 표현하기 위한 간단한 프론트엔드를 구현한 페이지입니다.
expressjs reactjs flask surprise-python
Language:Python 0
apfel-das / protasis-api
A movie recommendation engine made with FastAPI and Surprise (under the 100k movielens dataset)
fastapi python surprise-python
Language:JavaScript
Dacker15 / python-reccomendation
An overview of reccomendation systems in Python
jupyter-notebook matplotlib movielens-dataset python reccomendation-system surprise-python
Language:Jupyter Notebook
FREDRICKKYEKI / Data-Science-Phase-4-Project
Phase 4 project of the Flat Iron curriculum of Data Science in Moringa School
data-science flask-api machine-learning movie-recommendation python recommender-system scikit-learn surprise-python
Language:Jupyter Notebook
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
data-mining pagerank personalized-recommendation python recommendation-system scikit surprise-library surprise-python topic-specific-rank
Language:Jupyter Notebook
thislawyercodes / Film-Recommendation-system
A Collaborative filtering recommendation system
collaborative-filtering data-science knnwithmeans recommendation-system surprise-python
Language:Python

surprise-python

melodygr / grocery_recommendation

prakruti-joshi / Movie-Recommendation-System

klaudia-nazarko / collaborative-filtering-python

amanjeetsahu / Recommender-Systems-Using-Python

artisan1218 / Recommendation-System

aliceagrawal / HM-Recommender-System-App

ruxandraburtica / recommender-systems

singhsidhukuldeep / Recommendation-System

JuliaRecsys / Surprise.jl

unclebrod / YelpRecommender

andreamanzini / RecommendationSystem

camcochet / iGSLR-Personalized-Geo-Social-Location-Recommendation

mohamedmansoura / Recommender-system-with-Netflix-database

roweyerboat / Recommendation_System_MovieLens

sheetalbongale / Recommender-Systems-Machine-Learning

hmc-cs-azhao / 158-recommender-system

jacobceles / Movie-Recommendation-Rating-Prediction

Luis-Palacios / movie-recommendations

prabhj / Smm_Recommendation_System

SAKET-SK / Building-a-Recommendation-Engiene-Course

shaina-12 / Movie-Recommendation-System

azury74 / HotelRecomendationSystem

danielchristopher513 / Hybrid_Movie_Recommendation

didizhx / Recommendation-Tools-Project

Farzanmrz / recommender-amazon-reviews

forestinblue / Recommendar-System-Restaurant

Gruz2520 / rec_system_collab_filtering

MDanish99 / Recommender_System

tanyakuznetsova / Music_Mental_Health

TEAM-URS / Hybrid-Recsys

timbergrizz / simple_movie_recommendation_service

apfel-das / protasis-api

Dacker15 / python-reccomendation

FREDRICKKYEKI / Data-Science-Phase-4-Project

giulio-derasmo / Page-Rank-and-Recommendation-Systems

thislawyercodes / Film-Recommendation-system