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scikit-learn

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60 reviews
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Average star rating
4.8
Serving customers since
2018

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scikit-learn Reviews

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54
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Verified User in Higher Education
UH
Verified User in Higher Education
12/17/2025
Validated Reviewer
Review source: G2 invite
Incentivized Review

Perfect Starter Library for Machine Learning Beginners

I guess this is the library which every newbie who's learning machine learning starts with and so am I. This is a very clean library where you get the basic logical code of many algorithms like regression, classification and clustering etc.As the algorithm is pre written so i only focus on traning the datas and output.It have very clean and smooth API.
Palash S.
PS
Palash S.
Graduate Researcher and Freelance data Counsellor in machine learning, data science, and analytics domain.
09/20/2023
Validated Reviewer
Verified Current User
Review source: G2 invite
Incentivized Review

Best open source library for Machine learning.

I like how dynamic scikit-learn library is. it provides preloaded and ready-to-use functions for all sorts of machine learning and data preprocessing algorithms.
KS
Kitriakos S.
06/09/2023
Validated Reviewer
Review source: G2 invite
Incentivized Review

scikit-learn

Scikit-learn is built on top of efficient numerical libraries, such as NumPy and SciPy, which provide optimized implementations of mathematical and numerical operations. This ensures that the library can handle large datasets and complex computations efficiently, contributing to its robustness and scalability.

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What is scikit-learn?

Scikit-learn is an open-source machine learning library for the Python programming language. It provides simple and efficient tools for data analysis and modeling, making it accessible to both beginners and experienced data scientists. Scikit-learn supports various supervised and unsupervised learning algorithms, including regression, classification, clustering, and dimensionality reduction. It is built on top of other scientific libraries such as NumPy, SciPy, and matplotlib, ensuring seamless integration into the broader Python data science ecosystem. The library emphasizes ease of use, performance, and interoperability, making it a popular choice for developing machine learning applications.

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Year Founded
2018