Book Image

Machine Learning for Data Mining

By : Jesus Salcedo
Book Image

Machine Learning for Data Mining

By: Jesus Salcedo

Overview of this book

Machine learning (ML) combined with data mining can give you amazing results in your data mining work by empowering you with several ways to look at data. This book will help you improve your data mining techniques by using smart modeling techniques. This book will teach you how to implement ML algorithms and techniques in your data mining work. It will enable you to pair the best algorithms with the right tools and processes. You will learn how to identify patterns and make predictions with minimal human intervention. You will build different types of ML models, such as the neural network, the Support Vector Machines (SVMs), and the Decision tree. You will see how all of these models works and what kind of data in the dataset they are suited for. You will learn how to combine the results of different models in order to improve accuracy. Topics such as removing noise and handling errors will give you an added edge in model building and optimization. By the end of this book, you will be able to build predictive models and extract information of interest from the dataset

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Table of Contents (7 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introducing Machine Learning Predictive Models
Introducing Machine Learning Predictive Models
Characteristics of machine learning predictive models
Types of machine learning predictive models
Working with neural networks
A sample neural network model
Summary
2
Getting Started with Machine Learning
Getting Started with Machine Learning
Demonstrating a neural network
Support Vector Machines
Demonstrating SVMs
Summary
3
Understanding Models
Understanding Models
Models
Using graphs to interpret machine learning models
Using statistics to interpret machine learning models
Using decision trees to interpret machine learning models
Summary
4
Improving Individual Models
Improving Individual Models
Modifying model options
Using a different model to improve results
Removing noise to improve models
Doing additional data preparation
Balancing data
Summary
5
Advanced Ways of Improving Models
Advanced Ways of Improving Models
Combining models
Using propensity scores
Meta-level modeling
Error modeling
Boosting and bagging
Predicting continuous outcomes
Summary
6
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To get the most out of this book

  • Some knowledge on what data mining is, and the basic concepts of machine learning, will act as starting points for this book.
  • Familiarity with any machine learning modeler, specifically the SPSS Modeler provided by IBM, will be a plus, but isn't necessary.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packt.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Machine-Learning-for-Data-Mining. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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