surprise-library
There are 0 repository under surprise-library topic.
- Movie-Recommendation-Netflix
storieswithsiva / Movie-Recommendation-Netflix
🔮Trying to find the best movie to watch on Netflix can be a daunting. Case Study for Recommendation System of movies in Netflix.🔧
Language:Jupyter Notebook 51 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.
Language:Jupyter Notebook 22amanjeetsahu / 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.
Language:Python 8Balajirvp / Recommender-Systems---Content-Based-Systems-and-Collaborative-Filtering
Created Recommender systems using TMDB movie dataset by leveraging the concepts of Content Based Systems and Collaborative Filtering.
Language:Jupyter Notebook 4SajalSinha / ProductRecommendationEngine
Deployed Product Recommendation Model using collaborative filtering.
Language:Jupyter Notebook 3stxupengyu / MF-for-Movie-Recommendation
使用矩阵分解方法进行电影推荐的评分预测。The matrix factorization method is used to predict the rating of movie recommendation.
Language:Jupyter Notebook 3在Yelp数据集上摘取部分评分数据进行多种推荐算法（SVD,SVDPP,PMF,NMF）的性能对比。Some rating data are extracted from yelp dataset to compare the performance of various recommendation algorithms(SVD,SVDPP,PMF,NMF).
Language:Jupyter Notebook 3Deekshith126 / Recommender_systems
Simple Recommender Systems
Language:Jupyter Notebook 2roweyerboat / 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.
Language:Jupyter Notebook 2ahing / netflix-recommendation-system
Using a dataset from MovieLens, a movie recommendation system was created that recommends to users which movies they will like. The system also goes a step further to solve the cold start problem, which is when there is a new user in the dataset and there is no prior information on them. This system also finds a solution to this.
Language:Jupyter Notebook 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.
Language:Jupyter Notebook 1 Nehal-Pawar / Recommender-System
Predicted missing ratings using SVD algorithm from the Surprise Library for items from a file containing user ratings for multiple items by comparing a user’s ratings for available items with those of other user’s ratings and the project was built in Python
Language:Scala 1OsamaAlhalabi / Good-reads-recommender
Implementation for two different types of recommendation systems (Content-based and collaborative filtering)
Language:HTML 1somjit101 / Netflix-Movie-Recommendation
A case study of the Netflix Prize solution where, given anonymous data of users and the ratings given to movies, the objective to provide recommendations to users for movies which they would like, based on their past activity and taste.
Language:Jupyter Notebook 1sumanthvrao / MovieBuddy
Movie recommendation system to find common movie interests among a group of people.
Language:Jupyter Notebook 1auniyal486 / Book-Recommendation-System
A book recommendation system using model based collabritive filtering. It is based on SVD machine learning model. It generate top 10 recommendation of books.Here i used surprise library.
Language:Jupyter Notebook 0- Language:Jupyter Notebook 0
- ML-ALGORITHM
enockjamin01 / ML-ALGORITHM
This Repository provides the basic code snippets for all the most widely used ML Algorithms like Supervised, Unsupervised, and Recommender system algorithms.
artificial-intelligencedatapreprocessingdecision-treesisolation-forestkmeans-clusteringlinear-regressionlogistic-regressionmachine-learningmachine-learning-algorithmsneural-networkspythonrandom-forestrecommendation-systemsklearnsupervised-learningsurprise-librarysvd-factorizationtextvectorizationunsupervised-learningLanguage:Jupyter Notebook 0 hilarylim / Movie-Recommender
To recommend the next 10 movies to the user using the Prized Dataset provided by Netflix - over the span of 10 days for Capstone Project.
Language:Jupyter Notebook 0luthfiraditya / Ecommerce-Recommendation-System
I built recommender systems for recommending products to user using Model-based recommendation system.
Language:Jupyter Notebook 0mani24singh / Movies-Recommendation-System
Build a movies recommendation system clone using Movielens dataset to construct recommendation system such as Simple recommender, Content based recommender (based on movie description and metadata) , Collaborative-Filtering based recommender , and a Hybrid recommender system.
Language:Jupyter Notebook 0- Language:Jupyter Notebook 0
viviancaro / Data-Science
Proyectos de Data Science y Machine Learning.
0AkashBangalkar / Netflix-Movie-Recommendation
Machine Learning - Recommendation System
Language:Jupyter Notebook- Language:Jupyter Notebook
- Language:Jupyter Notebook
ebeui / restaurant_recommender_system
Tasty Trail: Restaurant Recommendation System
Language:Jupyter NotebookFridahKimathi / Book-Recommendation-System
The project used Python to create a personalized book recommendation system that analyzed users' past ratings on books to identify their preferences and patterns and suggested books that the user is likely to enjoy but has not read yet.
Language:Jupyter Notebookgiulio-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
Language:Jupyter Notebookizlata / book_recommender_system
A Book Recommender System: Collaborative Filtering using Surprise (k-NN Baseline model)
Language:Jupyter NotebookMoritzBaumann / Movie_Recommenders
Did you ever wonder how the recommendations on Netflix work? Find out in this project, where I build three basic movie recommenders and implement them in a streamlit App.
Language:Jupyter Notebooknnvij / Movie-Recommendation-System
Built a movie recommender system using Movielens dataset using both content-based filtering approach and collaborative filtering method.
Language:Jupyter Notebookpeteremiller / Predicting_User_Preferences
I created a recommender system using a Python scikit named Surprise. The purpose of building this system is to predict a person's preferences so the user can find what they are looking for faster.
Language:Jupyter NotebookSeungjaeLim / AIOneul-LLMEngine
Elice Generative AI Edu Hackaton TEAM10 <AI오늘>
Language:Pythonshulavkarki / Collabtrative-Filtering-with-SVD
A Movie Recommendation System using Collabrative Filtering
Language:Jupyter Notebookthimyxuan / movie-recommender-app
A movie recommender application
Language:Python
