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Twelve Data VS Scikit-learn

Compare Twelve Data VS Scikit-learn and see what are their differences

HasData
HasData is a top web scraping platform for developers and enterprises. It delivers structured, real-time data from the web using scalable APIs and no-code tools, removing the need to manage proxies, browsers, or anti-bot systems. featured
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Twelve Data logo Twelve Data

The simplest and most effective way to access both realtime and historical stock, forex, cryptocurrency data, and over 100 technical indicators.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Twelve Data Landing page
    Landing page //
    2023-09-23
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Twelve Data features and specs

  • Comprehensive Data Coverage
    Twelve Data provides access to a wide range of financial data including real-time and historical data for stocks, ETFs, mutual funds, indices, and forex, which is beneficial for comprehensive market analysis.
  • Affordable Pricing
    Twelve Data offers a variety of pricing plans, including a free tier with essential features, which makes it accessible to individual developers and small businesses.
  • Ease of Use
    The API is designed to be user-friendly with well-documented endpoints, making it easy for developers to integrate the service into their applications.
  • High Scalability
    Twelve Data offers robust and scalable API solutions that can handle a large volume of requests, suitable for both small projects and large-scale applications.
  • Global Market Coverage
    The platform provides data from global markets, allowing users to access information from multiple exchanges around the world.

Possible disadvantages of Twelve Data

  • Limited Advanced Features
    While Twelve Data offers a wide range of basic and intermediate features, it may lack some of the advanced analytics and tools needed by experienced traders or larger financial institutions.
  • Rate Limits on Lower Tiers
    The free and lower-tiered plans come with rate limits that can be restrictive for users requiring frequent data access or handling high traffic.
  • Dependency on API Availability
    As with any third-party data provider, users are dependent on the API's uptime and reliability, which can impact applications if there are outages or downtime.
  • Potential Data Lag for Free Plan
    Users on the free plan may experience delays or less frequent updates compared to those on paid plans, which can be a drawback for time-sensitive applications.

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.

Twelve Data videos

+ Add

Twelve Data Demo Review

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 Twelve Data and Scikit-learn)

Twelve Data

Scikit-learn

Finance
100 100%
Finance
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
APIs
100 100%
APIs
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Twelve Data and Scikit-learn. For example, how are they different and which one is better?
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Reviews

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

Twelve Data Reviews

  1. Andrew
    Best API for real-time financial data

    Straightforward to use and powerful API to get financial data in real-time and complete historical data. Best in class Python package.

    ๐Ÿ Competitors: Alpha Vantage, Polygon.io
    ๐Ÿ‘ Pros:    Equity data|Forex|Crypto|Affordable|Reliable

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

Based on our record, Scikit-learn should be more popular than Twelve Data. It has been mentiond 40 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.

Twelve Data mentions (8)

  • Building a Gold (XAUUSD) Trend Tracker with Python and SQLite
    I am using TwelveData forex API for Extraction of raw data from the financial markets in JSON format. - Source: dev.to / about 1 year ago
  • Top 5 Free Financial Data APIs for Building a Powerful Stock Portfolio Tracker โšก
    Twelve Data provides extensive financial market coverage, making it ideal for those looking for a free API for building a stock portfolio tracker. - Source: dev.to / over 1 year ago
  • Webflow Crypto Market Data Ticker
    I'd suggest Twelve Data for the financial data feed, if you have a budget for this: https://twelvedata.com/. Source: over 2 years ago
  • Where can I find a free source of historical S&P500 index data at hourly intervals?
    You can use https://twelvedata.com/ they offer an API and their free plan allows 800 credits a day so over a number of days you could get what you need. Source: about 4 years ago
  • Weekend Discussion Thread for the Weekend of May 06, 2022
    Twelve Data has a free one but don't know how good it is. u/flarmster that's the one I was talking about. Do you know a better one? Source: about 4 years ago
View more

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 1 month 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 / about 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 / about 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 / 4 months ago
View more

What are some alternatives?

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

Alpha Vantage - Alpha Vantage offers free APIs in JSON and CSV formats for realtime and historical stock and forex data, digital/crypto currency data and over 50 technical indicators.

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

Polygon.io - Polygon.io offers streaming realtime data for stocks/equities, ETFs, Indecies and Forex/Currencies including crypto currencies. Our Real-Time Stock Data APIs help you build the future on fintech.

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

Financial Modeling Prep - Access all stocks discounted cash flow statements, market price, stock markets news, and learn more about Financial Modeling. Learn M&amp;A, LBO, DCF, Comps, and Financial Statement Modeling thought concrete examples

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

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