surprise-python

There are 1 repository under surprise-python topic.

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 35
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 31
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 26
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 9
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 9
Whomrx666 / X-stupidity
X-stupidity is a surprise tool which you will know the contents of when you install it
android kali-linux kidding linux mobile surprise-python surprise-tool termux-hacking termux-tool x-stupidity
Language:Python 9
artisan1218 / Recommendation-System
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
collaborative-filtering content-based-recommendation cosine-similarity feature-augmentation item-based-recommendation jaccard-similarity pearson-correlation similar-items spark surprise-python svd-matrix-factorisation tf-idf-score tfidf upsampling user-based-recommendation xgboost xgboost-regression
Language:Python 7
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 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
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 4
JuliaRecsys / Surprise.jl
Suprise-Python Wrapper for Persa.jl
julia recommender-systems collaborative-filtering surprise-python recsys persa
Language:Julia 3
andreamanzini / RecommendationSystem
Machine Learning homework project at EPFL
machine-learning recommendation-system recommender-system machine-learning-algorithms netflix-prize surprise-python kaggle-competition epfl university-project data-science
Language:TeX 2
camcochet / iGSLR-Personalized-Geo-Social-Location-Recommendation
Implementation of the model iGSLR
recommender-system 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.
collaborative-filtering machine-learning recommender-system anime mal-dataset myanimelist surprise-python python python3 scikit-surprise
Language:Python 2
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 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
apfel-das / protasis-api
A movie recommendation engine made with FastAPI and Surprise (under the 100k movielens dataset)
fastapi python surprise-python
Language:JavaScript 1
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 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
anaconda artificial-intelligence dataset jupyter-notebook machine-learning python recommender-system spyder surprise-python 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
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.
bachelor-thesis generative-ai recommender-system surprise-python
Language:Python 0
farzanmrz / recommender-amazon-reviews
🛍️ Amazon Recommender 🚀 Exploring machine learning for e-commerce personalization with Amazon's Electronics data, using SVD and KNNBaseline algorithms to predict user preferences.
data-science machine-learning recommender-system surprise-python ai-algorithm
Language:Jupyter Notebook 0
Geeth-Priya / Predictive-Analytics-and-Recommendation-Systems-in-Banking
This project focuses on predicting Loan Defaults using Supervised Learning, Segmenting Customers with Unsupervised Learning, and Recommending Bank Products through a Recommendation Engine.
dbscan-clustering-algorithm decision-tree-classifier gradient-boosting kmeans-clustering kmeans-clustering-algorithm knn-classifier logistic-regression matplotlib numpy pandas python random-forest-classifier scikit-learn seaborn surprise-library surprise-python svd
Language:Jupyter Notebook 0
KennedyOwinoJnr / Library-web-app
In this project, we develop a fully functional web application for a library recommendation system. Users can register, create and manage profiles, write and manage blog reviews on their reading experiences, and share their writings with the world. The app includes a comprehensive UX/UI, encompassing all features from the original project vision.
data-science django html machine-learning natural-language-processing python scikit-learn surprise-python
Language:Python 0
lebenaba / movie_recommendation
Creation of a movie recommendation system during data scientist training at datascientest.com.
data-science deep-learning machine-learning recommendation-system surprise-python
Language:Jupyter Notebook 0
nits2612 / Data-Science-Projects
Portfolio of data science projects completed by me during PGP AI/ML, self learning, and hobby purposes.
deep deep-learning keras machine-learning numpy pandas python scikit-learn tensorflow transfer-learning matplotlib opencv seaborn surprise-python data data-science dataanalysis
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
adarsh-bhaskar / Recommender-System
This app analyzes ratings to suggest ideal products for e-commerce platforms. Upload your data, explore user trends, and train a model to predict what your customers will love!
machine-learning numpy pandas python3 surprise-python svd-matrix-factorisation
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
ikajdan / collaborative_filtering_benchmarking
Benchmarks of collaborative filtering techniques and optimizing K-NN and SVD for recommendation accuracy
colaborative-filtering f1-score k-nn python recommender-system surprise-python svd
Language:Python
muhammadadilnaeem / Simplilearn-Machine-Learning-using-Python-Course
Welcome to the Machine Learning Basics repository! This repository is dedicated to showcasing my journey through learning the fundamentals of machine learning. You'll find various datasets, Jupyter notebooks, and source code that I have worked on.
jupyter-notebook machine-learning machine-learning-algorithms matplotlib numpy pandas python seaborn sklearn surprise-python tensorflow
Language:Jupyter Notebook
Vignesh010101 / Product-Recommendation-System
A simple Product Recommendation System.
collaborative-filtering content-based-filtering content-based-recommendation data-analysis data-manipulation fine-tuning pandas product-management product-recommendation product-recommender product-recommender-system scikit-learn surprise-python
Language:Jupyter Notebook

surprise-python

klaudia-nazarko / collaborative-filtering-python

prakruti-joshi / Movie-Recommendation-System

melodygr / grocery_recommendation

aliceagrawal / HM-Recommender-System-App

amanjeetsahu / Recommender-Systems-Using-Python

Whomrx666 / X-stupidity

artisan1218 / Recommendation-System

singhsidhukuldeep / Recommendation-System

ruxandraburtica / recommender-systems

unclebrod / YelpRecommender

JuliaRecsys / Surprise.jl

andreamanzini / RecommendationSystem

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

hmc-cs-azhao / 158-recommender-system

jacobceles / Movie-Recommendation-Rating-Prediction

mohamedmansoura / Recommender-system-with-Netflix-database

roweyerboat / Recommendation_System_MovieLens

sheetalbongale / Recommender-Systems-Machine-Learning

apfel-das / protasis-api

danielchristopher513 / Hybrid_Movie_Recommendation

Luis-Palacios / movie-recommendations

prabhj / Smm_Recommendation_System

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

shaina-12 / Movie-Recommendation-System

emanuelneziraj / recommenders_gai_surprise

farzanmrz / recommender-amazon-reviews

Geeth-Priya / Predictive-Analytics-and-Recommendation-Systems-in-Banking

KennedyOwinoJnr / Library-web-app

lebenaba / movie_recommendation

nits2612 / Data-Science-Projects

tanyakuznetsova / Music_Mental_Health

adarsh-bhaskar / Recommender-System

FREDRICKKYEKI / Data-Science-Phase-4-Project

ikajdan / collaborative_filtering_benchmarking

muhammadadilnaeem / Simplilearn-Machine-Learning-using-Python-Course

Vignesh010101 / Product-Recommendation-System