Software Alternatives, Accelerators & Startups
Register | Login

Scikit-learn VS stats.fm

Compare Scikit-learn VS stats.fm and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

stats.fm logo stats.fm

With the click of a button you'll be logged with your Spotify account and you'll instantly gain access to a valhalla of cool stats and insights.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • stats.fm Landing page
    Landing page //
    2022-12-22

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.

stats.fm features and specs

  • Detailed Analytics
    Stats.fm provides in-depth insights into your music listening habits, offering detailed statistics on your favorite tracks, artists, and genres.
  • User-Friendly Interface
    The platform features an intuitive and easy-to-navigate interface, making it accessible for users of all experience levels.
  • Personalization
    It offers personalized music recommendations based on your listening history, helping users discover new music tailored to their preferences.
  • Integration with Streaming Services
    Stats.fm integrates seamlessly with popular music streaming services like Spotify, enhancing the user experience by providing comprehensive analytics.

Possible disadvantages of stats.fm

  • Privacy Concerns
    Users need to allow access to their streaming data, which may raise privacy concerns for some individuals.
  • Limited Free Features
    Many of the platform’s features are locked behind a paywall, requiring a subscription for full access.
  • Platform Dependency
    Stats.fm's effectiveness is dependent on the streaming service's API, and any changes or limitations in data access can impact its functionality.
  • Inconsistent Updates
    Some users have reported delays in the updating of statistics, which can affect the accuracy and timeliness of data insights.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

stats.fm videos

+ Add

SPOTISTATS / STATS.FM - HOW TO USE?

Category Popularity

0-100% (relative to Scikit-learn and stats.fm)

Scikit-learn

stats.fm

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

User comments

Share your experience with using Scikit-learn and stats.fm. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

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

stats.fm Reviews

We have no reviews of stats.fm yet.
Be the first one to post

Social recommendations and mentions

Based on our record, stats.fm should be more popular than Scikit-learn. It has been mentiond 104 times since March 2021. 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.

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 / 3 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 / 5 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 / 11 months 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 / about 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 / almost 2 years ago
View more

stats.fm mentions (104)

  • Show HN: I made a tool to turn your Spotify profile into a website
    If you're interested in something for consumers, I recommend you try https://stats.fm/. - Source: Hacker News / over 1 year ago
  • A song with lyrics he got word that overnight his friend went to sleep and never woke again
    If you cannot find it, you always have the option of paying and importing your stats to stats.fm and it should hopefully be there on the songs you have played! Source: over 1 year ago
  • Notbused this App before
    Stats.fm is just for spotify right? I use last.fm to track music on almost any interface you can think of:. Source: over 1 year ago
  • From what number of minutes do 0.001% and 0.0001 start?
    Not sure if the stats.fm 'leader board' for top listeners are correct since there seems to be some users with inflated minutes. Some random dude has 157,274 minutes on 'Fantastic Magic' alone which is unbelievable. Source: over 1 year ago
  • I need advice, falsely quarantined streams
    I tried contacting support about this but they just hit me with a "we think you deserve it so we're not going to do anything" (not literally, but it has those vibes). I do not farm streams or practice fraudulent behavior in any manner, and y'all are free to check this out yourselves. I use Spotify very frequently, whether it be to drown out noise or to distract me from other things. I also have diagnosed ADHD and... Source: over 1 year ago
View more

What are some alternatives?

When comparing Scikit-learn and stats.fm, you can also consider the following products

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

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

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

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

BandNext - Discover bands that sound similar to artists you already love with BandNext. A single click saves your results to a Youtube playlist.

Loading...