This is an app for collaborative and hybrid filtering using multiple csv data a model is trained and a flask is used for the web representation of model
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Updated
Jun 18, 2024 - HTML
This is an app for collaborative and hybrid filtering using multiple csv data a model is trained and a flask is used for the web representation of model
Collaborative Filtering based on Google Analytics 360 data from BigQuery.
R interface to fast.ai
Anime recommendation system with pyspark
NextRead is a book recommender system built for Book Lovers. Simply enter your current favourite book and get peronalized book list to find your new favourite.
NextRead is a book recommender system created specifically for book readers. It allows a user to get personalised recommendation with a user-friendly interface. This is my final year project.
Building a Movie Recommendation System web application using Django framework and Collaborative Filtering technique
This is an app for collaborative and hybrid filtering using multiple csv data a model is trained and a flask is used for the web representation of model
The RECeSS (Robust Explainable Controllable Standard for drug Screening) project is funded by a Marie Skłodowska-Curie Postdoctoral Fellowship 2022.
Book Recommendation | Collaborative Filtering
Recommendation challenge which goal was to recommend a list of 10 relevant tracks for each target playlist (Spotify Challenge)
I have created a book recommender system that recommends similar books to the reader based on his/her interest. This project shows results of collaborative and content-based filtering of the given dataset.
a data science blog
This repository consists of code and datasets used to built a book recommender system using collaborative filtering.
📖Notes and remarks on Machine Learning related papers
In this machine learning project, we build a recommendation system from the ground up to suggest movies to the user based on his/her preferences.
Radius is a movie streaming website built with Python, Django, and MySQL.
It's a website that recommends books from database to users based on ratings given by other users. Two recommender models are built viz. 1) Popularity Based Recommender 2) Using Collaborative Filtering Algorithm
Recommends Anime using Content based filtering (using TFIDF vectorization and sigmoid kernel) and collaborative filtering (using KNN)
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