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Machine Learning with scikit-learn Quick Start Guide

You're reading from   Machine Learning with scikit-learn Quick Start Guide Classification, regression, and clustering techniques in Python

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789343700
Length 172 pages
Edition 1st Edition
Languages
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Author (1):
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Kevin Jolly Kevin Jolly
Author Profile Icon Kevin Jolly
Kevin Jolly
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Table of Contents (10) Chapters Close

Preface 1. Introducing Machine Learning with scikit-learn 2. Predicting Categories with K-Nearest Neighbors FREE CHAPTER 3. Predicting Categories with Logistic Regression 4. Predicting Categories with Naive Bayes and SVMs 5. Predicting Numeric Outcomes with Linear Regression 6. Classification and Regression with Trees 7. Clustering Data with Unsupervised Machine Learning 8. Performance Evaluation Methods 9. Other Books You May Enjoy

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...

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