Beginner’s Guide to Machine Learning with scikit-learn in Python

Welcome! This article aims to provide a comprehensive introduction to the scikit-learn library, one of the most popular and widely-used libraries for machine learning in Python.

We’ll start by discussing the library’s key features and how to install and import scikit-learn, then cover the library’s basics, including how to create train-test splits, fit models, and make predictions. From there, we’ll delve into the specifics of supervised and unsupervised learning models and cover techniques for evaluating and selecting the best models for your data. By the end of this article, you will be equipped with the knowledge you need to start using it in your projects.

scikit-learn

Scikit-learn (also known as sklearn) is a machine-learning library for Python. It provides a wide range of tools for tasks such as classification, regression, and clustering, as well as for feature selection, preprocessing and model evaluation. scikit-learn is built on top of other Python libraries, such as NumPy and Pandas, which makes it easy to integrate with other data science tools. scikit-learn is widely used in industry and academia due to its simplicity and the variety of algorithms it provides. It’s a free software machine learning library for Python programming language. It features various…