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

R Machine Learning Essentials

By : Michele Usuelli
5 (1)
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R Machine Learning Essentials

R Machine Learning Essentials

5 (1)
By: Michele 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
8
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

Predicting the output

The past marketing campaign targeted part of the customer base. Among other 1,000 clients, how do we identify the 100 that are keener to subscribe? We can build a model that learns from the data and estimates which clients are more similar to the ones that subscribed in the previous campaign. For each client, the model estimates a score that is higher if the client is more likely to subscribe. There are different machine learning models determining the scores and we use two well-performing techniques, as follows:

  • Logistic regression: This is a variation of the linear regression to predict a binary output

  • Random forest: This is an ensemble based on a decision tree that works well in presence of many features

In the end, we need to choose one out of the two techniques. There are cross-validation methods that allow us to estimate model accuracy (see Chapter 6, Step 3 – Validating the Results). Starting from that, we can measure the accuracy of both the options and pick...

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R Machine Learning Essentials
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