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

K-means clustering - foodstuff


Nutrients in the food we consume can be classified by the role they play in building body mass. These nutrients can be divided into either macronutrients or essential micronutrients. Some examples of macronutrients are carbohydrates, protein, and fat while some examples of essential micronutrients are vitamins, minerals, and water.

Getting ready

Let's get started with the recipe.

Step 1 - collecting and describing data

In order to perform K-means clustering we shall be using a dataset collected on various food items and their respective Energy, Protein, Fat, Calcium, and Iron content. The numeric variables are:

  • Energy
  • Protein
  • Fat
  • Calcium
  • Iron

The non-numeric variable is:

  • Food

How to do it...

Let's get into the details.

Step 2 - exploring data

Note

Version info: Code for this page was tested in R version 3.2.3 (2015-12-10).

Loading the cluster() library.

> library(cluster)

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