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

Generalized additive models - measuring the household income of New Zealand


An income survey provides a snapshot of income levels for people and households. It gives median and average weekly income from most sources. There are income comparisons across different population groups. Income is only received intermittently, whereas consumption is smoothed over time. As a consequence, it is reasonable to expect that consumption is more directly related to current living standards than current income, at least for short reference periods.

Getting ready

In order to perform shrinkage methods, we will be using a dataset collected on the New Zealand Census 2013.

Step 1 - collecting and describing data

The nzcensus package contains demographic values of New Zealand that are more than 60 in number. These values have been accumulated at the level of mesh block, area unit, territorial authority, and regional council.

How to do it...

Let's get into the details.

Step 2 - exploring data

The first step is to load...