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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook Supervised and unsupervised machine learning simplified

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Length 570 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Machine Learning

Author (1):

Atul Tripathi

View More author details

Table of Contents (15) Chapters

Preface

1. Introduction to Machine Learning FREE CHAPTER

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

Exploring World Bank data

13. Case Study - Pricing Reinsurance Contracts

Pricing reinsurance contracts

14. Case Study - Forecast of Electricity Consumption

Principal component analysis - understanding world cuisine

Food is a powerful symbol of who we are. There are many types of food identification, such as ethnic, religious, and class identifications. Ethnic food preferences become identity markers in the presence of gustatory foreigners, such as when one goes abroad, or when those foreigners visit the home shores.

Getting ready

In order to perform principal component analysis, we shall be using a dataset collected on the Epicurious recipe dataset.

Step 1 - collecting and describing data

The dataset titled epic_recipes.txt shall be used. The dataset is in standard format.

How to do it...

Let's get into the details.

Step 2 - exploring data

The first step is to load the following packages:

    > install.packages("glmnet") 
    > library(ggplot2)
    > library(glmnet)

Note

Version info: Code for this page was tested in R version 3.3.2 (2016-10-31)

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

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Authors (1)

Atul Tripathi

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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