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ListenBrainz VS Scikit-learn

Compare ListenBrainz VS Scikit-learn and see what are their differences

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ListenBrainz logo ListenBrainz

Open source music website that allows users to import their listen history.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • ListenBrainz Landing page
    Landing page //
    2023-06-10
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

ListenBrainz features and specs

  • Open Source
    ListenBrainz is an open-source platform, allowing users to contribute to its development and improvement.
  • Privacy-Focused
    The platform emphasizes user privacy by allowing anonymity and providing transparency about collected data.
  • Community Driven
    As part of the MetaBrainz Foundation, ListenBrainz benefits from a community-driven approach, encouraging collaboration and innovation.
  • Integration with MusicBrainz
    It integrates seamlessly with MusicBrainz, providing enriched data insights and comprehensive music metadata.
  • Data Export
    Users can export their listening data, offering flexibility in how they handle their music listening history.

Possible disadvantages of ListenBrainz

  • Less Mainstream Integration
    Compared to other music tracking services, ListenBrainz might have limited integration with popular music streaming platforms.
  • Complex Setup
    Some users might find the setup process and integration with their existing music players non-intuitive or complex.
  • Limited Standalone Features
    On its own, ListenBrainz may lack some features users expect from more comprehensive music analytics services.
  • Smaller User Base
    The platform might have a smaller community of users compared to more established commercial music services, which could impact social features.

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Analysis of Scikit-learn

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

ListenBrainz videos

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Scikit-learn videos

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Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Category Popularity

0-100% (relative to ListenBrainz and Scikit-learn)

ListenBrainz

Scikit-learn

Music Streaming
100 100%
Music Streaming
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Music
100 100%
Music
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare ListenBrainz and Scikit-learn

ListenBrainz Reviews

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Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

ListenBrainz might be a bit more popular than Scikit-learn. We know about 45 links to it since March 2021 and only 31 links to Scikit-learn. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

ListenBrainz mentions (45)

  • Goodbye, Slopify
    For people moving off Spotify, have a look at https://listenbrainz.org. You can sync your listens to there and it will give you weekly recommendations. From my experience so far they are decent. Note they don't host songs themselves, but will auto-search youtube/bandcamp/etc. And play the closest match. So YMMV. - Source: Hacker News / 5 months ago
  • The Open Music Encyclopedia
    It really is an incredible resource, and Picard is a wonderful app. Very satisfying getting a library properly tagged! Takes a while, but totally worth it. Shoutout to ListenBrainz as well, their scrobbling service: https://listenbrainz.org/. - Source: Hacker News / 9 months ago
  • Analyzing Spotify Stream History
    There's also ListenBrainz, run by the MusicBrainz org, which offers similar functionality without API restrictions or other paid features that Last.FM tries to push. https://listenbrainz.org/ If you wish to use your scrobble data at all programmatically this is a far better tool to use. - Source: Hacker News / over 1 year ago
  • EP Showing Twice
    If you really <3 clean data you can give listenbrainz.org a go (you can scrobble to that and last.fm concurrently so no need to ‘jump ship’). Source: almost 2 years ago
  • Seeking Feedback: Tracking Vinyl Listening History
    Thanks for sharing, yea listenbrainz.org has been doing some great work. Also has an API which can make things interesting. Source: almost 2 years ago
View more

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 6 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / about 1 year ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / over 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / about 2 years ago
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What are some alternatives?

When comparing ListenBrainz and Scikit-learn, you can also consider the following products

Last.fm - The world's largest online music service. Listen online, find out more about your favourite artists, and get music recommendations, only at Last.fm

OpenCV - OpenCV is the world's biggest computer vision library

MusicBrainz - A community-maintained open source database and encyclopedia of music information.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Maloja - Simple self-hosted music scrobble database to create personal listening statistics.

NumPy - NumPy is the fundamental package for scientific computing with Python

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