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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Pages 570 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Atul Tripathi

Table of Contents (21) Chapters

Practical Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Introduction to Machine Learning

2. Classification

3. Clustering

4. Model Selection and Regularization

5. Nonlinearity

6. Supervised Learning

7. Unsupervised Learning

8. Reinforcement Learning

9. Structured Prediction

10. Neural Networks

11. Deep Learning

12. Case Study - Exploring World Bank Data

13. Case Study - Pricing Reinsurance Contracts

14. Case Study - Forecast of Electricity Consumption

Introduction

K-means clustering - foodstuff

Nutrients in the food we consume can be classified by the role they play in building body mass. These nutrients can be divided into either macronutrients or essential micronutrients. Some examples of macronutrients are carbohydrates, protein, and fat while some examples of essential micronutrients are vitamins, minerals, and water.

Getting ready

Let's get started with the recipe.

Step 1 - collecting and describing data

In order to perform K-means clustering we shall be using a dataset collected on various food items and their respective Energy, Protein, Fat, Calcium, and Iron content. The numeric variables are:

Energy
Protein
Fat
Calcium
Iron

The non-numeric variable is:

Food

How to do it...

Let's get into the details.

Step 2 - exploring data

Note

Version info: Code for this page was tested in R version 3.2.3 (2015-12-10).

Loading the cluster() library.

> library(cluster)

Let's explore the data and understand relationships among the variables. We'll begin by importing the...

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Authors (1)

Atul Tripathi

Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

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