Personalized book recommender system to generate top 5 recommendations to users
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scikit-surprise
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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 plotly pandas accuracy recommender-system singular-value-decomposition scikit-surprise knnwithmeans
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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.
python evaluation python3 collaborative-filtering recommender-system jupyter-notebooks content-based-recommendation surprise-python hybrid-recommender-system scikit-surprise surprise-library
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- Python
Food Finder: An interface for a multi-user recommendation system.
natural-language-processing collaborative-filtering matrix-factorization restaurants recommendation-system latent-dirichlet-allocation user-reviews yelp-dataset content-based-recommendation hybrid-recommender-system las-vegas scikit-surprise knowledge-based-recommendation gensim-topic-modeling folium-maps
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Machine Learning Music Recommendation System: Hybrid Approach (Content & SVD) with Flask
flask machine-learning numpy scikit-learn ml pandas music-recommendation seaborn scipy matplotlib svd surprise scikit-surprise content-based-filtering
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- Python
Recommender system that applies a user-to-user collaborative filtering algorithm on the MAL dataset to recommend anime for users.
python machine-learning anime python3 collaborative-filtering recommender-system myanimelist surprise-python scikit-surprise mal-dataset
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A machine learning recommendation engine using Collaborative Filtering and Matrix Factorization on the MovieLens dataset.
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Adaptive Learning Recommendation System using collaborative filtering to suggest personalized learning materials based on student engagement and performance metrics.
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Common Machine Learning Examples 💻
python nlp opencv machine-learning deep-learning random-forest tensorflow svm naive-bayes genetic-algorithm scikit-learn keras pandas object-detection decision-trees tpot textblob lightfm svr scikit-surprise
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A Wine Recommender System (My Capstone project for Data Science Immersive course)
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A collaborative filtering recommendation system built with Python, Pandas, and Scikit-Surprise.
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A repository containing various projects and microprojects.
python data-science machine-learning deep-learning clustering numpy scikit-learn regression pandas data-visualization classification dimensionality-reduction data-analysis survival-analysis matplotlib keras-tensorflow time-series-analysis lifelines scikit-surprise
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Manga Recommender Systems based on Anilist API
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Movie Recommendation using SVD Algorithm
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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.
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- Python
RECOMMENDER for films >= 2010. Uses Scikit-Surprise.
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RECOMMENDER for allrecipes.com. Using Scikit-Surprise
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movie recommendation system using python and ml
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Simple Recommender System built using the Scikit Surprise Library. The application provides recommendations based on Amazon Products Reviews.
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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.
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