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Scikit-learn VS Whatagraph

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

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  3. » Reviews
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Scikit-learn logo Scikit-learn

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

Whatagraph logo Whatagraph

Whatagraph is the most visual multi-source marketing reporting platform. Built in collaboration with digital marketing agencies
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Whatagraph Landing page
    Landing page //
    2023-07-22

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.

Whatagraph features and specs

  • User-Friendly Interface
    Whatagraph's intuitive design makes it easy for users, even those without technical expertise, to create and understand comprehensive reports.
  • Customization
    Offers extensive customization options for reports, allowing users to tailor them to specific needs and branding requirements.
  • Integrations
    Seamlessly integrates with popular marketing tools and platforms such as Google Analytics, Facebook, and Mailchimp, providing a centralized reporting solution.
  • Automation
    Enables automated reporting, saving time and ensuring that reports are consistently delivered on schedule.
  • Collaboration
    Facilitates collaboration by allowing multiple users to access and edit reports, streamlining team workflows.
  • Visual Appeal
    Produces visually appealing, professional reports that can enhance presentations and client communications.

Possible disadvantages of Whatagraph

  • Pricing
    Whatagraph may be considered expensive for small businesses or startups due to its subscription model.
  • Learning Curve
    While relatively user-friendly, some users may experience a learning curve when first starting out with the platform.
  • Template Limitations
    Some users have reported limited flexibility in template designs, which may not suit highly specific reporting needs.
  • Data Sync Delays
    There can be occasional delays in data syncing from integrated platforms, which might affect the timeliness of reports.
  • Customer Support
    Some users have indicated that customer support can be slow to respond or not as helpful as desired.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Whatagraph videos

+ Add

Top 4 Whatagraph Features Released in 2019

More videos:

  • Review - Whatagraph Reviews - Honest thoughts after using the whatagraph tool (whatagraph review)
  • Review - whatagraph review - Everything You Need To Know About The Tool (whatagraph review 2019)

Category Popularity

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

Scikit-learn

Whatagraph

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Data Dashboard
25 25%
Data Dashboard
75% 75
Data Science Tools
100 100%
Data Science Tools
0% 0
Business Intelligence
0 0%
Business Intelligence
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 Scikit-learn and Whatagraph

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

Whatagraph Reviews

8 Databox Alternatives: Which One Is The Best?
Customers mainly use Whatagraph for tracking campaign results from various channels. The platform provides visualizations, reports, and data insights in the manner of leading your company’s success. It offers some features that you may not find in other competitor tools such as monitoring multiple channels at once or styling reports based on your needs.
Source: hockeystack.com
25 Best Reporting Tools for 2022
Whatagraph is known as a reporting tool that allows you to compare and monitor the performance of various campaigns. It also allows you to transfer custom data from API and Google Sheets.
Source: hevodata.com

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Whatagraph. 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.

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

Whatagraph mentions (4)

  • Linking visibility and positions data in google data studio
    I recommend pulling this easily into whatagraph.com through drag & drop functionality. Amazing integration depth, also! Source: almost 4 years ago
  • Does this tool exist?
    Try whatagraph.com. Should do the job for you. Source: almost 4 years ago
  • V2.0 of Google Data Studio
    Hey everyone, Just like the title says that's what Whatagraph.com is - those of you who are looking to significantly improve your data aggregation, visualization, and reporting capabilities, I would love to invite you to our webinar next week on Tuesday at 3pm BST.https://www.linkedin.com/events/6793088092371763200/. Source: about 4 years ago
  • New data analyst tasked with major overhaul needing guidance!
    The space I am more aware of is the data integration part of the process, and my team uses hotglue (though hotglue is built for developers) to collate the data into one place, do any transformations necessary (the transformations are done in Python in hotglue), and then send it to the tool we use (we recently switched from Databox to Whatagraph). The nice thing about this for us is we can actually remain on the... Source: about 4 years ago

What are some alternatives?

When comparing Scikit-learn and Whatagraph, 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.

Databox - Databox is an easy-to-use analytics platform that helps growing businesses centralize their data, and use it to make better decisions and improve performance.

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

Supermetrics - Supermetrics simplifies marketing analytics by connecting, consolidating, and centralizing data from 150+ platforms into your favorite tools. Trusted by 200K+ organizations, we empower marketers to focus on insights, not manual work.

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

Owler - Owler is a crowdsourced data model allowing users to follow, track, and research companies.

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