Python Svd

Open-source Python projects categorized as Svd
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Topics: #Matrix #Python #Systems #Pandas #Machine Learning
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Top 3 Python Svd Projects

  • Surprise

    A Python scikit for building and analyzing recommender systems

    Project mention: Recommender Systems: Surprise library installation on m1 mac | /r/learnpython | 2023-01-12

    Something is wrong with the repo. The compiler fails with this error clang: error: no such file or directory: 'surprise/similarities.c' If you go to the repo, you'll see the file is indeed missing: https://github.com/NicolasHug/Surprise/tree/master/surprise

  • prince

    :crown: Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA (by MaxHalford)

    Project mention: Categorical data in K-means | /r/datascience | 2023-05-19

    Well, my favourite thing about posting on Reddit is learning new things. https://github.com/MaxHalford/prince I looked up the Prince and I'll be reading up about FAMD shotly - thanks!

  • Sonar

    Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.

  • letterboxd_recommendations

    Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username

    Project mention: Self Hosted Content Recommender? | /r/selfhosted | 2023-06-20

    Use an existing self-hostable tool for getting recommendations from there, such as letterboxd_recommendations

NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020). The latest post mention was on 2023-06-20.

Python Svd related posts

Index

What are some of the best open-source Svd projects in Python? This list will help you:

Project Stars
1 Surprise 5,958
2 prince 1,075
3 letterboxd_recommendations 167

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