Book Image

Practical Machine Learning Cookbook

By : Atul Tripathi
Book Image

Practical Machine Learning Cookbook

By: Atul Tripathi

Overview of this book

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.
Table of Contents (21 chapters)
Practical Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
14
Case Study - Forecast of Electricity Consumption

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...