Book Image

Machine Learning with scikit-learn Quick Start Guide

By : Kevin Jolly
Book Image

Machine Learning with scikit-learn Quick Start Guide

By: Kevin Jolly

Overview of this book

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
Table of Contents (10 chapters)

Implementing linear regression in scikit-learn

In this section, you will implement your first linear regression algorithm in scikit-learn. To make this easy to follow, the section will be divided into three subsections, in which you will learn about the following topics:

  • Implementing and visualizing a simple linear regression model in two dimensions
  • Implementing linear regression to predict the mobile transaction amount
  • Scaling your data for a potential increase in performance

Linear regression in two dimensions

In this subsection, you will learn how to implement your first linear regression algorithm, in order to predict the amount of a mobile transaction by using one input feature: the old balance amount of the account...