In addition to these contributors and communities, there are also various meetups held across the globe. The following people are currently the core contributors to scikit-learn’s development and maintenance: Since it is open-source, anyone can contribute to it. One of the main reasons behind the popularity of Sklearn is the community and contributors behind it. Here is a full rundown of the different versions of Scikit Learn: The project was taken to another level by a number of volunteers and was first made public on 1st Feb 2010. It was developed by David Cournapeau as a Google Summer of Code (GSoC) project in 2007. Scikit Learn was originally called scikits.learn. Note: Sklearn is used to build machine learning models. A number of big organizations such as Spotify, Evernote, JP Morgan, Inria, AWeber, and many more use Sklearn. Although the library is fairly new, it has quickly become one of the most popular libraries on GitHub. It also integrates well with many other Python libraries, such as Matplotlib, Plotly, NumPy, Pandas, SciPy, etc. Scikit-learn is one of NumFOCUS’s fiscally sponsored projects. Additionally, it provides many other tools for evaluation, selection, model development, and data preprocessing. It provides a range of tools for machine learning and statistical modeling including dimensionality reduction, clustering, regression, and classification, through a consistent interface in Python. It is open source and built upon NumPy, SciPy, and Matplotlib. Scikit Learn or Sklearn is one of the most robust libraries for machine learning in Python.

Conda install sklearn