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Snowball Analytics VS Scikit-learn

Compare Snowball Analytics VS Scikit-learn and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Snowball Analytics logo Snowball Analytics

Simple and powerful portfolio tracker for investors. Dividend tracker, portfolio performance and quick portfolio rebalancing. Supports thousands of stocks, funds and cryptocurrencies from all over the world.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Snowball Analytics Landing page
    Landing page //
    2022-09-14

Snowball Analytics is an investment tracking app for any thoughtful long-term investor. Get overview of all your investments in one place - portfolio performance, dividends, company fundamentals, benchmarking and more.

๐Ÿ“‹ Lose the spreadsheet โ€“ we make investment tracking easy and hassle-free

๐Ÿ’ฐ All your investments in one place - stocks, crypto, funds, real estate, etc.

๐ŸŒ Multiple currencies and stock exchanges

โฑ๏ธ Easy data import - link your brokerage account in a few minutes (US, EU, Asia, ...). 1000+ brokers supported

๐Ÿ“Š Benchmarking - compare your results with popular funds and indices

๐Ÿช™ Comprehensive dividend analytics, future dividends and our own rating of dividend companies

๐Ÿ–ฑ๏ธ One-click portfolio rebalancing

๐Ÿ“ˆ Company fundamentals

๐Ÿค Community - see how other investors are navigating their portfolio in current market conditions

  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Snowball Analytics

Website
snowball-analytics.com
Pricing URL
Official Snowball Analytics Pricing
$ Details
freemium $7.99 / Monthly (1 portfolio, automatic brokerage connection)
Platforms
Web
Release Date
2022 August
Edit details

Scikit-learn

Website
scikit-learn.org
Pricing URL
-
$ Details
Platforms
-
Release Date
-
Edit details

Snowball Analytics features and specs

  • Number of portfolios
    10
  • Number of holdings
    Unlimited
  • Automatic brokerage connection
  • Stocks, Funds, Cryptocurrencies
  • Dividend calendar
  • Returns analytics
  • IRR calculations
  • Benchmarking
  • Portfolio rebalancing
  • Multiple currencies and stock exchnages
  • Custom investments (real estate, deposits)
  • Binance API support
  • Top dividend stocks

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.

Snowball Analytics videos

+ Add

Snowball Analytics overview

Scikit-learn videos

+ Add

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 Snowball Analytics and Scikit-learn)

Snowball Analytics

Scikit-learn

Finance
100 100%
Finance
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Investing
100 100%
Investing
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 Snowball Analytics and Scikit-learn

Snowball Analytics 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

Scikit-learn might be a bit more popular than Snowball Analytics. We know about 40 links to it since March 2021 and only 33 links to Snowball Analytics. 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.

Snowball Analytics mentions (33)

  • Replacement for StockMarketEye?
    Https://snowball-analytics.com has categories, but about $150 a year for multiple portfolios. Source: almost 3 years ago
  • Portfolio rebalancing
    If you're contributing monthly, use those funds to top up. Otherwise, there are online tools you can use to get rebalancing very close. I use https://snowball-analytics.com/ but there are others I'm sure. Source: about 3 years ago
  • Website or app to track all retirement accounts?
    You can try Snowball Analytics. Have been using the free edition for a while now and its OK but its not as comprehensive as other tools. Source: about 3 years ago
  • Dividends across multiple brokerages
    You can use โ€œstock eventsโ€ or https://snowball-analytics.com to manually add your positions from all your brokers and see a total overview. Source: about 3 years ago
  • $150/month milestone! Still not buying schd and jepi (but added OKE and NEE)
    For trading/broker I use M1 Finance. The app on the screenshot is https://snowball-analytics.com/ but itโ€™s not a trading app, itโ€™s just to track dividends and stuff. Source: over 3 years ago
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Scikit-learn mentions (40)

  • Detecting Ingress Tool Transfer (T1105) with Python
    Certutil.exe or notepad.exe opening an external connection lands in rare because, fleet-wide, those processes almost never egress. Tune the <= 3 threshold to your environment size. For a more principled version, score each (process, destination) pair by frequency and treat the long tail as the hunt queue, which is the same idea behind scikit-learn's rarity-based anomaly methods without the model overhead. - Source: dev.to / about 2 months ago
  • Best AI Cybersecurity Training for Security Teams: How to Pick
    Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / 2 months ago
  • Where to Get Hands-On AI Training for Cybersecurity Professionals
    Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. No setup tax. - Source: dev.to / 2 months ago
  • How Anomaly Detection Actually Works in Security Operations
    Isolation-based models: Build random decision trees that split features. Points that are isolated quickly (short average path length across trees) are anomalies. IsolationForest in scikit-learn implements this. Handles high-dimensional feature spaces without assuming a distribution. - Source: dev.to / 3 months ago
  • Building a Personalized Meal Recommendation System
    In practice, youโ€™ll want to use libraries (like scikit-learn or TensorFlow.js for more advanced modeling), but the principle remains: find what similar users enjoy, and use that as a basis for recommendations. - Source: dev.to / 5 months ago
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What are some alternatives?

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

Sharesight - Online stock portfolio tracker that automatically tracks prices, dividends, performance and tax.

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

Kubera - Protect your wealth

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

getquin - Track all your investments in one place

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

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