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Predicting Categories with K-Nearest Neighbors

The k-Nearest Neighbors (k-NN) algorithm is a form of supervised machine learning that is used to predict categories. In this chapter, you will learn about the following:

Preparing a dataset for machine learning with scikit-learn
How the k-NN algorithm works under the hood
Implementing your first k-NN algorithm to predict a fraudulent transaction
Fine-tuning the parameters of the k-NN algorithm
Scaling your data for optimized performance

The k-NN algorithm has a wide range of applications in the field of classification and supervised machine learning. Some of the real-world applications for this algorithm include predicting loan defaults and credit-based fraud in the financial industry and predicting whether a patient has cancer in the healthcare industry.

This book's design facilitates the implementation of a robust machine...

Key benefits

Build your first machine learning model using scikit-learn

Train supervised and unsupervised models using popular techniques such as classification, regression and clustering

Understand how scikit-learn can be applied to different types of machine learning problems

Description

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.

What you will learn

Learn how to work with all scikit-learn s machine learning algorithms

Install and set up scikit-learn to build your first machine learning model

Employ Unsupervised Machine Learning Algorithms to cluster unlabelled data into groups

Perform classification and regression machine learning

Use an effective pipeline to build a machine learning project from scratch

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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DRM FREE - Read whenever, wherever and however you want

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Machine Learning with scikit-learn Quick Start Guide

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Mastering Predictive Analytics with scikit-learn and TensorFlow

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Total € 86.97

Donald Kalley Apr 09, 2020

Now I am understanding Scikit learn. The approach to only focus on all the different algorithms was a smart move. Now I will proceed to purchase another book(Bokeh) from the author. Great job!

Amazon Verified review

MyAmazonReviewNameHere May 30, 2021

Well written and very useful.

Sven T Sep 13, 2023

Das Buch ist sehr verständlich und didaktisch super aufbereitet. Ein kleiner Fehler im Code wurde über den Errata Prozess schnell adressiert, geklärt und für die Zukunft korrigiert (auch online entsprechend abrufbar). Sehr netter Kontakt mit Verlag und Autor. Es ist nicht mein erstes Buch zu diesem Thema, aber wirklich top geschrieben. Die entsprechenden Bibliotheken werden super anhand von Beispielen erklärt. Theorie kommt auch nicht zu kurz, allerdings konzentriert sich der Autor ums "Machen", also sehr praxisorientiert. Absolute Kaufempfehlung!

Keolohilani L. Mar 06, 2019

I am liking this book so far. It is making some of the mathematically difficult concepts easier to follow with some code to work along. I hope to get through this book in the next couple of days.

Scott Zasadil Dec 29, 2020

Whoever reviewed this book for Packt publishing did not do a good job of it as a number of obvious mistakes stand out: 1) At the conclusion of Chapter 3, we are told that the topic of Chapter 4 will be decision trees and random forests, which don't appear until Chapter 6; 2) In chapter 5, the author uses a dataset with a *binary* target of isFraud to explain not only Linear Regression but also Ridge Regression and Lasso Regression, as well. Logistic regression - not linear regression - is best suited for binary classification problems and scikit-learn actually has RidgeClassifier - which the author does not use - for performing a classification problem using Ridge regression. Also, the Lasso code on page 67 will not run as intended as the author uses a variable called ridge_regression in it. That is, it uses the results from the previous Ridge Regression code to evaluate the Lasso Regression work; 3) The explanation of how the Random Forest algorithm operates on page 80 is incorrect.There are some good sections - in particular the chapter on Performance Evaluation Methods, but because the reader has to be on the lookout for what should have been easily caught errors, this is not a book for someone who is just getting started with scikit-learn.

Machine Learning with scikit-learn Quick Start Guide: Classification, regression, and clustering techniques in Python

What do you get with Print?

Machine Learning with scikit-learn Quick Start Guide

Predicting Categories with K-Nearest Neighbors

Technical requirements

Preparing a dataset for machine learning with scikit-learn

The k-NN algorithm

Implementing the k-NN algorithm using scikit-learn

Splitting the data into training and test sets

Fine-tuning the parameters of the k-NN algorithm

Scaling for optimized performance

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Machine Learning with scikit-learn Quick Start Guide: Classification, regression, and clustering techniques in Python

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

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Table of Contents

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