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Scikit-learn VS Minitab 18

Compare Scikit-learn VS Minitab 18 and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

Scikit-learn logo Scikit-learn

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

Minitab 18 logo Minitab 18

Get started with Minitab, get help using Minitab tools and features, and find definitions for common terms.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Minitab 18 Landing page
    Landing page //
    2022-11-10

Scikit-learn videos

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Learning Scikit-Learn (AI Adventures)

More videos:

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

Minitab 18 videos

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Crear Diagrama Causa - Efecto (Diagrama de Ishikawa) en Minitab 18 | Herramientas de calidad

Category Popularity

0-100% (relative to Scikit-learn and Minitab 18)

Scikit-learn

Minitab 18

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Technical Computing
0 0%
Technical Computing
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Data Dashboard
81 81%
Data Dashboard
19% 19

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 Minitab 18

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

Minitab 18 Reviews

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Social recommendations and mentions

Based on our record, Scikit-learn seems to be more popular. It has been mentiond 29 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 (29)

  • 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 / 4 days 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 / 3 months 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 / about 1 year ago
  • WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
    The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
  • PSA: You don't need fancy stuff to do good work.
    Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
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Minitab 18 mentions (0)

We have not tracked any mentions of Minitab 18 yet. Tracking of Minitab 18 recommendations started around Mar 2021.

What are some alternatives?

When comparing Scikit-learn and Minitab 18, 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.

RStudio - RStudio™ is a new integrated development environment (IDE) for R.

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

IBM SPSS Statistics - IBM SPSS Statistics is software that provides detailed analysis of statistical data. The company behind the product practically needs no introduction, as it's been a staple of the technology industry for over 100 years.

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

Stata - Stata is a software that combines hundreds of different statistical tools into one user interface. Everything from data management to statistical analysis to publication-quality graphics is supported by Stata. Read more about Stata.

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