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  • Book Overview & Buying R Machine Learning Essentials
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R Machine Learning Essentials

R Machine Learning Essentials

By : Usuelli
5 (1)
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R Machine Learning Essentials

R Machine Learning Essentials

5 (1)
By: Usuelli
Buy this Book

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?
Table of Contents (10 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Transforming Data into Actions
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1. Transforming Data into Actions
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A data-driven approach in business decisions
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Identifying hidden patterns
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Estimating the impact of an action
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Summary
2
2. R – A Powerful Tool for Developing Machine Learning Algorithms
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2. R – A Powerful Tool for Developing Machine Learning Algorithms
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Why R
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The R tutorial
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Some useful R packages
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Summary
3
3. A Simple Machine Learning Analysis
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3. A Simple Machine Learning Analysis
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Exploring data interactively
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Exploring the data using machine learning models
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Predicting newer outcomes
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Summary
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4. Step 1 – Data Exploration and Feature Engineering
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4. Step 1 – Data Exploration and Feature Engineering
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Building a machine learning solution
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Building the feature data
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Exploring and visualizing the features
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Modifying the features
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Ranking the features using a filter or a dimensionality reduction
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Summary
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5. Step 2 – Applying Machine Learning Techniques
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5. Step 2 – Applying Machine Learning Techniques
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Identifying a homogeneous group of items
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Applying the k-nearest neighbor algorithm
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Optimizing the k-nearest neighbor algorithm
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Summary
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6. Step 3 – Validating the Results
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6. Step 3 – Validating the Results
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Validating a machine learning model
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Tuning the parameters
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Selecting the data features to include in the model
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Tuning features and parameters together
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Summary
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7. Overview of Machine Learning Techniques
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7. Overview of Machine Learning Techniques
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Overview
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Supervised learning
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Linear regression
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Perceptron
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Unsupervised learning
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Summary
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8. Machine Learning Examples Applicable to Businesses
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8. Machine Learning Examples Applicable to Businesses
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Overview of the problem
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Clustering the clients
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Predicting the output
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Summary
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Index
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Index

Chapter 3. A Simple Machine Learning Analysis

This chapter shows examples of exploratory data analysis and machine learning techniques. R provides us with different datasets that can be used to experiment with the tools. In this chapter, we will use an interesting dataset about the Titanic passengers.

There are some facts that happened during the Titanic event, such as the policy of saving the women and children first and the privileges of the first social classes. In order to investigate what happened, we can use the data related to the event. The R dataset is about some passengers and it displays their personal data and who survived. First, we can explore some data in order to understand what happened. Then, starting from the personal data of other passengers, the goal of the machine learning model is forecasting which new passengers will survive.

In this chapter, we'll cover the following topics:

  • Exploring the data
  • Visualizing the data using simple charts
  • Exploring the data...
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Tech Concepts
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Programming languages
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R Machine Learning Essentials
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