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ThingSpeak VS Scikit-learn

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

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ThingSpeak logo ThingSpeak

Open source data platform for the Internet of Things. ThingSpeak Features

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • ThingSpeak Landing page
    Landing page //
    2021-07-26
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

ThingSpeak features and specs

  • Ease of Use
    ThingSpeak provides a user-friendly interface and extensive documentation, making it suitable for users with varying levels of technical expertise.
  • Real-time Data Processing
    It allows real-time data collection, analysis, and visualization, which can be beneficial for applications that require immediate feedback.
  • Integration with MATLAB
    Seamless integration with MATLAB allows users to leverage MATLAB's powerful data analysis and visualization tools for more advanced analysis.
  • API Support
    ThingSpeak provides RESTful APIs, making it easier to collect, store, and retrieve data from IoT devices and other sources.
  • Free Tier
    Offers a free tier for users to start with basic usage, which is useful for small projects or initial experimentation.
  • Community Support
    A broad community of users means more available resources such as tutorials, forums, and shared projects for learning and troubleshooting.

Possible disadvantages of ThingSpeak

  • Limited Free Tier
    The free version has limitations on the number of channels and data storage, which might not be sufficient for larger projects.
  • Dependence on Internet
    Requires a constant internet connection to transmit data to the cloud, which could be a drawback in remote or unstable network environments.
  • Data Privacy
    As a cloud-based service, data control and privacy can be concerns, especially for sensitive or proprietary information.
  • Limited Advanced Features
    Advanced data analytics features are relatively basic compared to more comprehensive IoT platforms, which might limit its use for more complex requirements.
  • Cost for Pro Features
    To access more advanced features and larger data capacities, a paid plan is required, which may not be cost-effective for all users.
  • Latency
    For applications requiring ultra-low latency, using a cloud service can introduce delays that might be unacceptable.

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 ThingSpeak

Overall verdict

  • Whether ThingSpeak is 'good' largely depends on user needs and project requirements. It is considered a good choice for those who require a straightforward, robust platform for IoT projects and appreciate its integration with MATLAB. However, users with very advanced or custom requirements might find its features limiting compared to other more extensive IoT platforms.

Why this product is good

  • ThingSpeak is a popular IoT (Internet of Things) platform that allows users to collect, visualize, and analyze live data streams from devices or sensors over the internet. It is favored for its ease of use, integration capabilities, and support for MATLAB analytics, which provides advanced data analysis and visualization tools. It is also compatible with various hardware platforms like Arduino, Raspberry Pi, and more, making it accessible for both hobbyists and professionals.

Recommended for

  • Students and educators looking to learn and teach IoT concepts
  • Hobbyists interested in creating simple IoT projects
  • Developers seeking an easy-to-use platform for quick prototyping
  • Professionals who require MATLAB's analytical features for data analysis
  • Organizations looking for reliable data logging and visualization solutions

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.

ThingSpeak videos

+ Add

How to Analyze IoT Data in ThingSpeak

More videos:

  • Review - Review Higrow Board ESP32 and Aplication on Thingspeak #IoT #ESP32
  • Tutorial - How to Use ThingSpeak with Arduino

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 ThingSpeak and Scikit-learn)

ThingSpeak

Scikit-learn

Data Dashboard
71 71%
Data Dashboard
29% 29
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
IoT Platform
100 100%
IoT Platform
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using ThingSpeak 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 ThingSpeak and Scikit-learn

ThingSpeak Reviews

Best IoT Platforms in 2022 for Small Business
ThingSpeak is an IoT platform that uses channels to store data sent from apps or devices. A special feature of ThingSpeak is that you can create your own channel to collect the analyzed data hence giving a great level of flexibility to the users. You can also collect the data from the public (for example, ThingSpeak channel 12397 – Weather Station) and configure to write...
Source: www.fogwing.io
Open Source Internet of Things (IoT) Platforms
Known as the cloud IoT platform with MATLAB analytics, ThingSpeak allows you to aggregate, analyze, and visualize live data streams. IoT devices send their live data directly to ThingSpeak. From there, you create instant visualizations and can send alerts using web services. Essentially, however, you write and execute MATLAB code to do your data preparation, visualization...
Source: jansutris10.medium.com
14 of the Best IoT Platforms to Watch in 2021
ThingSpeak is a 100% analytics platform which supports advanced developer applications in environmental monitoring, energy, and smart farming. All the analysis is done on Matlab, and you can utilize the data insights for really cool stuff. For example, connecting an IoT device to Twitter and sending alerts. The best part is that using data for a certain interval is free....
Source: www.iottechtrends.com

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 ThingSpeak. It has been mentiond 31 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.

ThingSpeak mentions (9)

  • Kotlin/ Thingspeak Interfacing.
    First of all, you need to ask yourself how familiar you are with MatLab. Then from a dev point of view, could you use an API to reference cloud data then apply analytics. Great intro to IoT. I can see that company going far in 5-10 and may invest based on trajectory. Https://thingspeak.com. Source: over 1 year ago
  • Google sheets and esp32
    You can use solutions like thingspeak https://thingspeak.com/. Source: about 2 years ago
  • Help me check my circuit for my self-sustaining water meter
    I'm not sure yet. Maybe something custom, but probably not. I was thinking about Thingspeak before. Source: over 2 years ago
  • Displaying readings to website?
    I haven't got around to MQTT yet, but as an easy interim solution I recommend ThingSpeak https://thingspeak.com/ as you can set up an account for free and getting an ESP to send data to it is trivial. Plus you can access it via the web, or embed their graphs and dials into a webpage. The graphics are a bit meh though. Source: over 2 years ago
  • i have an idea for a database+arduino+matlab, i need some help plz
    ThingSpeak for IoT Projects Data collection in the cloud with advanced data analysis using MATLAB Https://thingspeak.com/. Source: over 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 / 12 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 / 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 / almost 2 years ago
View more

What are some alternatives?

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

AWS IoT - Easily and securely connect devices to the cloud.

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

Blynk.io - We make internet of things simple

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

Azure IoT Hub - Manage billions of IoT devices with Azure IoT Hub, a cloud platform that lets you easily connect, monitor, provision, and configure IoT devices.

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

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