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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

Measuring the unemployment rate

The unemployment rate is defined as the percentage of the total labor force that is unemployed, but actively seeking employment and willing to work. As defined by the International Labor Organization (ILO), an unemployed person is someone who is actively looking for work but does not have a job. The unemployment rate is a measure of the number of people who are both jobless and looking for a job.

Getting ready

In order to perform a measurement of the unemployment rate using neural networks, we shall be using a dataset collected on the unemployment rate in Wisconsin.

Step 1 - collecting and describing data

For this, we will be using a CSV dataset titled FRED-WIUR.csv. There are 448 rows of data. There are two numeric variables as follows:

Date
Value

This dataset shows the unemployment rate in Wisconsin between January 1, 1976 and April 1, 2013.

How to do it...

Let's get into the details.

Step 2 - exploring data

First, the following packages need to be loaded:

    &gt...

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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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