surprise-python
There are 64 repositories 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.
prakruti-joshi/Movie-Recommendation-System
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
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.
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.
singhsidhukuldeep/Recommendation-System
Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering
Whomrx666/X-stupidity
X-stupidity is a surprise tool which you will know the contents of when you install it
artisan1218/Recommendation-System
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
ruxandraburtica/recommender-systems
Getting a better grasp of recommender systems
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
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.
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.
apfel-das/protasis-api
A movie recommendation engine made with FastAPI and Surprise (under the 100k movielens dataset)
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.
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
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.
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.
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.
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.
lebenaba/movie_recommendation
Creation of a movie recommendation system during data scientist training at datascientest.com.
nits2612/Data-Science-Projects
Portfolio of data science projects completed by me during PGP AI/ML, self learning, and hobby purposes.
tanyakuznetsova/Music_Mental_Health
Harnessing music's power for better mental health: genre recommendations and data-driven analysis of listeners' trends
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!
FREDRICKKYEKI/Data-Science-Phase-4-Project
Phase 4 project of the Flat Iron curriculum of Data Science in Moringa School
ikajdan/collaborative_filtering_benchmarking
Benchmarks of collaborative filtering techniques and optimizing K-NN and SVD for recommendation accuracy
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.
Vignesh010101/Product-Recommendation-System
A simple Product Recommendation System.