scikit-surprise

There are 0 repository under scikit-surprise topic.

rakshita95 / Book-Recommendation-System
Personalized book recommender system to generate top 5 recommendations to users
recommender-system scikit-surprise
Language:Jupyter Notebook 24
rochitasundar / Collaborative-Filtering-Book-Recommendation-System
This project aims to build & optimise a book recommendation system based on collaborative filtering and will tackle an example of both memory based & model based approach (using KNNWithMeans & Singular Value Decomposition)
numpy pandas plotly scikit-surprise knnwithmeans singular-value-decomposition accuracy recommender-system
Language:Jupyter Notebook 18
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
amolratnaparkhe / Foodfinder-Recommendation-System
Food Finder: An interface for a multi-user recommendation system.
restaurants hybrid-recommender-system yelp-dataset natural-language-processing scikit-surprise gensim-topic-modeling user-reviews matrix-factorization folium-maps las-vegas recommendation-system collaborative-filtering content-based-recommendation latent-dirichlet-allocation knowledge-based-recommendation
Language:Jupyter Notebook 3
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
leewanxian / wine_recommender
A Wine Recommender System (My Capstone project for Data Science Immersive course)
feature-engineering nlp pandas python recommendation-system scikit-surprise streamlit
Language:Jupyter Notebook 1
RyanDsilva / machine-learning-starters
Common Machine Learning Examples :computer:
python machine-learning scikit-learn pandas nlp textblob svm svr tensorflow keras genetic-algorithm tpot lightfm deep-learning object-detection opencv scikit-surprise decision-trees naive-bayes random-forest
Language:Python 1
angad-singh97 / Timestamp-Reranking-Movie-Recommender
This project is a movie recommender engine written using Python and the Scikit-Surprise libraries to generate better movie recommendations by utilizing temporal user interactiondata.
movie-recommender pandas python3 scikit-surprise
Language:Python 0
chiksumwong / recommendation_system
Python Recommendation System
scikit-surprise svdpp
Language:Python 0
Diane-GUEPET / Recommendation_Application
The aim of this project is to build a recommendation app. using Collaborative Filtering/ Content based models , scikit-surprise library, Azure function as API and Flask
azure-functions flask scikit-surprise
Language:Jupyter Notebook 0
Ohmdatazwld5 / Movies-Recommendation-using-SVD
Movie Recommendation using SVD Algorithm
cross-validate matplotlib-pyplot pandas-library scikit-surprise
Language:Jupyter Notebook 0
sheezer / Data-Projects
A repository containing various projects and microprojects.
data-science data-analysis data-visualization deep-learning machine-learning python scikit-learn keras-tensorflow matplotlib pandas scikit-surprise classification clustering dimensionality-reduction numpy regression survival-analysis time-series-analysis lifelines
Language:Jupyter Notebook 0
sinchana-eshwar / Movie-Recommender-System
collaborative-filtering data-cleaning data-science data-visualization explo hybrid-recommender-system python recommender-system scikit-learn scikit-surprise
Language:Jupyter Notebook 0
skaty5678 / Book-recommendation-system
Recommend books using various machine learning algorithms.
collaborative-filtering item-based-recommendation matrix-factorization recommender-system scikit-surprise machine-based-model memory-based-model
Language:Jupyter Notebook 0
svpathak / Collaborative-Filtering-based-Movie-Recommendation
A collaborating filtering based system on Movie Lens dataset to recommend user specific movie suggestions. The model was evaluated with recommedation specific metrics including Long Tail plot using 'surprise' library.
collaborative-filtering movielens recmetrics recommender-system scikit-surprise
Language:Jupyter Notebook 0
brayvid / recommend-recipes
Pantry-based recipe recommendations.
cooking data-science kaggle recipes recommendation scikit-surprise streamlit web-app
Language:Python
rahulduttt / product-recommender-streamlit
Simple Recommender System built using the Scikit Surprise Library. The application provides recommendations based on Amazon Products Reviews.
amazon collaborative-filtering knn python recommendation-system scikit-surprise streamlit svd
Language:Jupyter Notebook
sayande01 / Book_Recommender_System_Machine_Learning
This project aims to build an advanced book recommendation system by integrating collaborative filtering, content-based filtering, and machine learning. It offers tailored suggestions based on user preferences and interactions, using EDA for insights and cosine similarity and SVD for precise recommendations.
collaborative-filtering cosine-similarity scikit-surprise svd
Language:Jupyter Notebook
tamccullough / movieeze
RECOMMENDER for films >= 2010. Uses Scikit-Surprise.
flask movie-recommendation pandas python recommender-system scikit-surprise
Language:Jupyter Notebook
tamccullough / recipeeze
RECOMMENDER for allrecipes.com. Using Scikit-Surprise
allrecipes flask pandas python recipe-recommender recommender-system scikit-surprise
Language:Jupyter Notebook

scikit-surprise

rakshita95 / Book-Recommendation-System

rochitasundar / Collaborative-Filtering-Book-Recommendation-System

amanjeetsahu / Recommender-Systems-Using-Python

amolratnaparkhe / Foodfinder-Recommendation-System

hmc-cs-azhao / 158-recommender-system

leewanxian / wine_recommender

RyanDsilva / machine-learning-starters

angad-singh97 / Timestamp-Reranking-Movie-Recommender

chiksumwong / recommendation_system

Diane-GUEPET / Recommendation_Application

Ohmdatazwld5 / Movies-Recommendation-using-SVD

sheezer / Data-Projects

sinchana-eshwar / Movie-Recommender-System

skaty5678 / Book-recommendation-system

svpathak / Collaborative-Filtering-based-Movie-Recommendation

brayvid / recommend-recipes

rahulduttt / product-recommender-streamlit

sayande01 / Book_Recommender_System_Machine_Learning

tamccullough / movieeze

tamccullough / recipeeze