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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 - European countries protein consumption

A food consumption pattern is of great interest in the field of medicine and nutrition. Food consumption is correlated to the overall health of an individual, the nutritional value of the food, the economics involved in purchasing a food item, and the environment in which it is consumed. This analysis is concerned with the relationship between meat and other food items in 25 European countries. It is interesting to observe the correlation between meat and other food items. The data includes measures of red meat, white meat, eggs, milk, fish, cereals, starchy foods, nuts (including pulses and oil-seeds), fruits, and vegetables.

Getting ready

In order to perform K-means clustering, we shall be using a dataset collected on protein consumption for 25 European countries.

Step 1 - collecting and describing data

The dataset titled protein which is in the CSV format shall be used. The dataset is in standard format. There are 25 rows of data...

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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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