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Scikit-learn VS ASAP Utilities

Compare Scikit-learn VS ASAP Utilities and see what are their differences

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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.

ASAP Utilities logo ASAP Utilities

ASAP Utilities is a powerful Excel add-in that fills the gaps in Excel.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • ASAP Utilities Landing page
    Landing page //
    2023-04-17

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.

ASAP Utilities features and specs

  • Time-Saving Features
    ASAP Utilities offers a wide range of features that automate repetitive tasks in Excel, allowing users to save time on data processing and analysis.
  • User-Friendly Interface
    The add-in integrates seamlessly into Excel and provides an intuitive interface that is easy to navigate, even for users who are not advanced Excel users.
  • Extensive Functionality
    It includes over 300 powerful utilities that cover a variety of functions like data cleaning, formatting, and formula management, enhancing Excel’s built-in capabilities.
  • Regular Updates
    ASAP Utilities is consistently updated with new features and improvements, ensuring compatibility with the latest versions of Excel and addressing user-requested enhancements.
  • Efficient Customer Support
    The software is backed by a responsive customer support team who assist with technical issues and user inquiries, widely praised for their helpfulness and efficiency.

Possible disadvantages of ASAP Utilities

  • Cost
    ASAP Utilities is a paid add-in, which might be a drawback for users who are looking for free solutions or for those with limited budgets.
  • Complexity of Choices
    With over 300 utilities available, users may find it overwhelming to navigate through all the options and identify the most useful tools for their specific needs.
  • Learning Curve
    Even though the interface is user-friendly, the sheer number of features can require a learning curve for new users to become fully proficient with the tool.
  • Compatibility Issues
    There could be occasional compatibility issues with specific Excel versions or other Excel add-ins, potentially leading to software conflicts or reduced functionality.
  • Limited to Excel
    The add-in is specifically designed for Excel and cannot be used in other spreadsheet applications, limiting its utility to Microsoft Office users.

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.

Analysis of ASAP Utilities

Overall verdict

  • ASAP Utilities is generally considered to be a valuable tool for Excel users, especially those who work extensively with spreadsheets and require enhanced functionality beyond what Excel natively offers. Its comprehensive set of features and ease of use make it a worthwhile investment for improving efficiency.

Why this product is good

  • ASAP Utilities is a popular add-in for Microsoft Excel that provides a wide range of tools designed to simplify and enhance spreadsheet tasks. It offers over 300 utilities that help users automate repetitive tasks, improve productivity, and perform advanced data analysis. Users frequently praise its ability to save time and reduce errors in Excel tasks.

Recommended for

    ASAP Utilities is recommended for business professionals, data analysts, accountants, and any individuals or teams who regularly work with large or complex Excel spreadsheets. It's particularly beneficial for users who want to streamline their workflow and enhance the capabilities of Excel through additional tools and automation features.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

ASAP Utilities videos

+ Add

Excel Add-in: ASAP Utilities

More videos:

  • Review - Trying out ASAP Utilities
  • Review - CARA CETAK BYNAME DENGAN ASAP UTILITIES DAN EXCEL MUDAH GAMPANG

Category Popularity

0-100% (relative to Scikit-learn and ASAP Utilities)

Scikit-learn

ASAP Utilities

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Data Dashboard
51 51%
Data Dashboard
49% 49
Data Science Tools
100 100%
Data Science Tools
0% 0
Technical Computing
0 0%
Technical Computing
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 ASAP Utilities

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

ASAP Utilities Reviews

We have no reviews of ASAP Utilities yet.
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Social recommendations and mentions

Based on our record, Scikit-learn seems to be more popular. 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 / 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

ASAP Utilities mentions (0)

We have not tracked any mentions of ASAP Utilities yet. Tracking of ASAP Utilities recommendations started around Mar 2021.

What are some alternatives?

When comparing Scikit-learn and ASAP Utilities, 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.

Kutools for Excel - A handy Microsoft Excel add-ins collection to free you from time-consuming operations.

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

Excel Dashboard School - Free Excel add-ins and tools on Excel Dashboard School. Boost your work productivity and save your time! No trials, 100% power!

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

KPI Dashboard in Excel - Professional Management KPI Dashboard. Includes trend charts, past year/target comparisons, monthly & cumulative analysis in performance dashboard.

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