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R Machine Learning Essentials

By : Usuelli

5 (1)

R Machine Learning Essentials

5 (1)

By: Usuelli

Overview of this book

If your company is like most, the number one reason that projects have failed over the years don’t have to do with technology. They have to do with people. People didn’t like the new technology. People weren’t trained properly on the change. People hadn’t received adequate communications and didn’t understand the change. Sound familiar?

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Transforming Data into Actions

1. Transforming Data into Actions

A data-driven approach in business decisions

Identifying hidden patterns

Estimating the impact of an action

Summary

2. R – A Powerful Tool for Developing Machine Learning Algorithms

2. R – A Powerful Tool for Developing Machine Learning Algorithms

Why R

The R tutorial

Some useful R packages

Summary

3. A Simple Machine Learning Analysis

3. A Simple Machine Learning Analysis

Exploring data interactively

Exploring the data using machine learning models

Predicting newer outcomes

Summary

4. Step 1 – Data Exploration and Feature Engineering

4. Step 1 – Data Exploration and Feature Engineering

Building a machine learning solution

Building the feature data

Exploring and visualizing the features

Modifying the features

Ranking the features using a filter or a dimensionality reduction

Summary

5. Step 2 – Applying Machine Learning Techniques

5. Step 2 – Applying Machine Learning Techniques

Identifying a homogeneous group of items

Applying the k-nearest neighbor algorithm

Optimizing the k-nearest neighbor algorithm

Summary

6. Step 3 – Validating the Results

6. Step 3 – Validating the Results

Validating a machine learning model

Tuning the parameters

Selecting the data features to include in the model

Tuning features and parameters together

Summary

7. Overview of Machine Learning Techniques

7. Overview of Machine Learning Techniques

Overview

Supervised learning

Linear regression

Perceptron

Unsupervised learning

Summary

8. Machine Learning Examples Applicable to Businesses

8. Machine Learning Examples Applicable to Businesses

Overview of the problem

Clustering the clients

Predicting the output

Summary

Index

Index

Summary

In this chapter, we learned how to process data using data table operations and built some simple R plots for exploratory data analysis. We learned how to use decision trees to find useful insights and build machine learning models (random forest) to perform predictions. We saw how to change the parameters of a model and how to validate it.

The next three chapters show the steps introduced in this chapter in detail. Chapter 4, Step 1 - Data Exploration and Feature Engineering, shows the first step of machine learning that consists of data exploration and feature engineering, in depth.

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83

Tech Concepts

36

Programming languages

73

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R Machine Learning Essentials

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